February 28, 2012 

1 

Report to EPA on Etch Factor Proposal for Fab GHG Emissions Reporting

 
February 28, 2012 

2 

1. Executive Summary 

On December 1, 2010, EPA adopted a rule (40CFR Part 98 Subpart I) governing the reporting of 

Greenhouse Gas emissions from electronics manufacturing, including semiconductor fabs. The 

procedures in that rule present significant problems for the semiconductor industry. The rule specifies 

default emission factors for CVD chamber cleaning, which represents the majority of semiconductor Fgas usage. However, the rule requires that the F-Gas emissions from etch processes at larger fab 

facilities be determined and reported on a “recipe” basis. That approach has aspects that are technically 

infeasible and are otherwise costly and difficult to accomplish. Further, this method requires the use 

and disclosure of Confidential Business Information (CBI). 

For these and other reasons, on January 30, 2011, SIA filed a petition for review and a petition for 

reconsideration of the rule. SIA and EPA are now working together to find a better approach to 

reporting F-Gas emissions that will be practical and accurate and will minimize the need to use 

confidential information. 

SIA is advocating the adoption of two alternative methods and has both collected data and conducted 

analyses in support of those two alternative methods. One of the two is referred to as the Etch Factor 

Method. This approach is a refinement of the Subpart I Tier 2c method, with industry-level etch process 

emissions factors developed using a greatly expanded database of etch emission factor data 

(differentiated by gas and wafer size), and with etch and wafer clean processes combined. The factors 

in 40CFR Part 98 Subpart I would be used for non-etch activities. SIA and ISMI have carried out an 

extensive data gathering effort to support the development of new etch emissions factors. 

At the time the Subpart I rule was adopted, EPA had relatively limited data on emissions from etch tools, 

the gas use in etch tools, and the performance of abatement devices. After months of data gathering 

and analysis, we are now in a position to create and evaluate a better way to determine emissions. 

The first step in our analysis was to determine the form of the Etch Factor Method. SIA has developed

and tested the performance of a 2-Factor model, where emission factors are a function of input gas and

wafer size, and a 3-Factor model, which adds film type as the third factor. These models were selected 

through detailed analysis of the statistical relationships of 13 attributes of the etch process to etch 

emissions and the determination of which attributes best modeled etch process emissions. 

At the November 17, 2011 meeting with EPA, building on the data provided to EPA in an August 11, 

2011 meeting, SIA and ISMI presented the data and models and an initial analysis of the precision and 

accuracy of the Etch Factor Method as compared to the IPCC Tier 2b, EPA Tier 2c, and Tier 3 methods. 

On November 30, 2011, there was a similar presentation on the alternative Stack Test Method. EPA and 

SIA have developed and agreed to a Work Plan addressing these alternatives and the other issues 

associated with modifying the Subpart I reporting rule. That Work Plan included a set of questions from 

EPA as follow up to the November 17th meeting. Most of those questions were addressed in a February 

3

rd submittal to EPA. This submittal addresses the remaining questions related to the Etch Factor 

Method. (Questions and comments related to the Stack Test Method and the determination of 

abatement device performance are being addressed in separate submittals.) 

This document presents the data behind the 2-Factor model, the way the 2-Factor model is constructed, 

and how it performs in comparison to other models (including the 3-Factor model). The starting point is 

the precision and accuracy of the model at the industry-level. Further precision and accuracy analysis of 

the 2-Factor model was then done at the facility-level to demonstrate the viability of the model for 
February 28, 2012 

3 

facility level reporting. This work is a major refinement of the work presented in November. It includes 

some corrections to data, an assessment of precision based on expected error rather than worst case 

error, the extension of gas apportioning error to the 2-Factor model and the non-etch sources of 

emissions affected by gas apportioning, the precision of the 2-Factor model at the facility level, and the 

impact of emissions abatement on the 2-Factor model’s precision. 

We have endeavored to provide a thorough statistical analysis of the emissions estimation model 

options, building on the additional data that we have gathered over the past year. 

From this work, we conclude the following: 

1. The 2-Factor Model is accurate when compared to the Tier 3 method, which is the closest 

simulation of the recipe method in the current rule.

1

2. The 2-Factor Model is more accurate and precise than the available alternative Tier 2 models 

(IPCC Tier 2B, EPA Tier 2C) 

3. The 3-Factor Model offers no benefits over the 2-Factor model and the 2-factor model provides 

a slightly more conservative estimation of semiconductor manufacturing GHG emissions when 

evaluated at an industry level. 

4. The 2-Factor Model provides very good precision in characterizing fab F-Gas emissions at both 

the industry and the facility level. 

In support of these conclusions, the following pages present and briefly discuss selected results of our 

analyses. The complete analytical results are presented and explained in detail in Section 5 of this 

report. 

 

1

 Due to the technical challenges of the recipe approach in the current Subpart I, we have not been able to apply it 

to a fab for a direct comparison with the 2-Factor Model. 
February 28, 2012 

4 

Table E-1 shows facility-level2 emissions estimates created using five methods. As stated above, we are 

using the Tier 3 model to be the closest representation of the etch recipe approach in the final rule. The 

performance of the SIA 2-Factor model is closest to the Tier 3 model, coming within -9% to +8% for the

etch emissions and within -5% to +2% for total emissions. 

Table E-1: Facility Level Accuracy of Etch F-Gas Emission Estimates By Various Methods, 

(Based on a Comparison to Tier 3 Emissions Estimates For One 200mm and Two 300mm Fabs, 

using Expected Error via the Error Propagation Method, and without Considering Abatement)

 Year Estimation Method 

Etch F-Gas 

Emissions 

Total F-Gas 

Emissions 

(Etch + Non-Etch) 

Emissions 

(mtCO2e) 

Emissions 

(mtCO2e) 

200mm 2009 IPCC Tier 2B (1 factor) 22,700 45,900 

 2009 EPA Tier 2C (2 factor) 23,000 46,100 

 2009 SIA 2-Factor 28,100 51,300 

 2009 SIA 3-Factor 22,700 45,800 

 2009 Tier 3 29,500 52,700 

 2010 IPCC Tier 2B (1 factor) 23,300 44,600 

 2010 EPA Tier 2C (2 factor) 24,100 45,400 

 2010 SIA 2-Factor 28,000 49,300 

 2010 SIA 3-Factor 22,400 43,700 

 2010 Tier 3 30,600 51,800 

300mm 2009 IPCC Tier 2B (1 factor) 28,100 54,600 

 2009 EPA Tier 2C (2 factor) 24,700 51,300 

 2009 SIA 2-Factor 15,600 42,100 

 2009 SIA 3-Factor 15,200 41,700 

 2009 Tier 3 14,600 41,200 

 2010 IPCC Tier 2B (1 factor) 50,800 86,400 

 2010 EPA Tier 2C (2 factor) 44,100 79,700 

 2010 SIA 2-Factor 27,000 62,600 

 2010 SIA 3-Factor 26,700 62,300 

 2010 Tier 3 25,600 61,200 

 

2

 Tier 3 is not in common use. Tier 3 analyses were only available for these three fabs . 
February 28, 2012 

5 

Table E-2 compares the industry level3 performance of the SIA 2-Factor Model, the SIA 3-Factor Model 

and the two IPCC and EPA Tier-2 models, when the error in the etch emissions factors is considered. 

The new SIA models, because of the larger data base they draw upon, have greater precision than the 

Tier-2 models. However, the 3-Factor does not provide any significant improvement in precision when 

compared to the 2-Factor Model. 

Table E-2: Industry Level Analysis of Precision of Etch F-Gas Emission Estimates by Various Methods 

(Based on Emission Factor Expected Errors; Error Propagation Method, 

Not Considering Gas Apportioning Error or Abatement)

 Year Estimation Method 

Etch F-Gas Emissions 

Total F-Gas 

Emissions 

(Etch + NonEtch) 

Etch 

Emissions 

(mtCO2e) 

Error

Standard 

Deviation 

(mtCO2e) 

95% 

Confidence 

Interval 

(Percent) 

Total 

Emissions 

(mtCO2e) 

200mm 2009 IPCC Tier 2B (1 factor) 339,300 41,800 ± 41% 1,242,900 

 2009 EPA Tier 2C (2 factor) 342,400 40,700 ± 41% 1,246,000 

 2009 SIA 2-Factor 402,200 14,200 ± 18% 1,305,800 

 2009 SIA 3-Factor 346,700 21,400 ± 16% 1,250,300 

 2010 IPCC Tier 2B (1 factor) 435,100 55,900 ± 51% 1,236,700 

 2010 EPA Tier 2C (2 factor) 438,700 54,700 ± 51% 1,240,400 

 2010 SIA 2-Factor 530,300 20,100 ± 19% 1,331,900 

 2010 SIA 3-Factor 456,500 29,000 ± 17% 1,258,100 

300mm 2009 IPCC Tier 2B (1 factor) 392,800 63,600 ± 58% 570,400 

 2009 EPA Tier 2C (2 factor) 374,200 63,200 ± 62% 551,800 

 2009 SIA 2-Factor 264,200 10,600 ± 8% 441,800

 2009 SIA 3-Factor 251,000 8,900 ± 7% 428,600 

 2010 IPCC Tier 2B (1 factor) 464,600 68,200 ± 52% 713,000 

 2010 EPA Tier 2C (2 factor) 438,000 67,800 ± 56% 686,300 

 2010 SIA 2-Factor 297,900 10,900 ± 7% 546,300 

 2010 SIA 3-Factor 285,200 9,700 ± 7% 533,600

 

3

 Throughout this report, the industry level analysis is based on the composite gas use of 12 fabs, seven 200mm 

and five 300 mm, that are representative of the range of fabs operating within the industry. 
February 28, 2012 

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Table E-3 shows the precision of the 2-Factor Model when it is applied at the industry level, when 

emissions factor error, gas apportioning error, and abatement are included4. Its precision in predicting 

etch emissions has a 95% confidence interval that is less than ± 30%. For total F-Gas emissions, the 95% 

confidence interval is less than ± 20%. 

Table E-3: Industry Level Analysis of the Precision of the SIA 2-Factor Model 

(Expected error; Monte Carlo Method; 

Accounting for Gas Apportioning and Etch Emission Factor Errors and Abatement in 300mm Fabs) 

 Etch F-Gas Emissions Total F-Gas Emissions 

(Etch + Non-Etch) 

Wafer 

Size Year 

Etch 

Emissions 

(mtCO2e) 

Error 

Standard 

Deviation 

(mtCO2e) 

95% Confidence 

Interval 

(Percent) 

Total 

Emissions 

(mtCO2e) 

95% Confidence 

Interval 

(Percent) 

200mm 2009 402,194 57,500 -25% to +30% 1,305,764 -6% to + 7% 

 2010 530,270 63,600 -21% to +27% 1,331,926 -5% to + 7% 

300mm 2009 188,526 24,100 -19% to +31% 276,565 -16% to +19% 

 2010 222,935 28,300 -19% to +31% 334,606 -16% to +19% 

 

 

4

 Throughout this report whenever the gas apportionment error was analyzed, the results are based on Monte 

Carlo simulations. 
February 28, 2012 

7 

Lastly, Table E-4 shows the precision of the SIA 2-factor Model at the facility level, applied to two 

200mm fabs and two 300mm fabs. Again the precision is very good, with three of the four fabs having 

95% confidence intervals of less than ± 20%. The one fab with a higher interval is a fab that is fully 

abated and utilizes a gas that has high variability in the current data set. 

Table E-4. Facility Level 2-Factor Model Precision Analysis 

(Includes the Effects of Gas Apportioning and Emission Factor Errors 

and Abatement at 300mm Fabs) 

 Etch F-Gas Emissions Total F-Gas Emissions 

Etch + Non-Etch 

Wafer Size Level Year ± 95% Confidence 

Interval mtCO2e ± 95% Confidence Interval (Range) 

200mm Industry 2009 -25% to +30% -6% to + 7% 

Fab B “ -28% to +48% 187,400 -4% to +4% 

Fab C “ -28% to +37% 149,100 -7% to + 9% 

 Industry 2010 -21% to +27% -5% to + 7% 

Fab B “ -26% to +39% 259,100 -3% to +4% 

Fab C “ -26% to +29% 174,400 -10% to +12% 

300mm Industry 2009 -19% to +31% -16% to +19% 

Fab A “ -17% to +16% 32,200 -13% to +15% 

Fab E “ -40% to +53% 42,700 -33% to +45% 

 Industry 2010 -19% to +31% -16% to +19% 

Fab A “ -17% to +18% 38,100 -13% to +16% 

Fab E “ -33% to +46% 46,700 -27% to +39% 

 

In the process of completing this analysis, ISMI has identified a few gas and tool combinations where 

added data is expected to reduce emissions factor variability. Additional data is being sought from fab 

operators and tool manufacturers and those data will be available in the first half of 2012. The inclusion 

of this data should further improve the precision achieved by the SIA 2-Factor Model. 

### 

 
February 28, 2012 

8 

2. Background 

2.1 Final Subpart I rule and SIA concerns

The final greenhouse gas reporting rule for electronics manufacturing facilities, including semiconductor 

fabs, requires fabs to calculate and report emissions based on emissions factors and recipe-specific 

emissions factors. See 75 Fed. Reg. 74,774 (Dec. 1, 2010). SIA member companies were extremely 

concerned with the recipe-specific approach for five primary reasons: 

• Technical infeasibility – The rule calls for the measurement and reporting of emissions from 

“each individual recipe” within a semiconductor fab. Each fab, however, can have many steps, 

and each fab may have thousands of such individual recipes that continually undergo change. 

This makes compliance infeasible as a practical matter. 

• High and disproportionate cost – Even assuming that the industry could comply with the rule, it 

imposes a huge cost on the industry that is wholly disproportionate to its tiny share of overall 

U.S. emissions of GHGs (approximately 0.12 percent of total emissions). 

• A group of industry experts working in collaboration with ISMI estimated – using conservative 

assumptions which likely underestimate costs – that the compliance cost for the semiconductor 

industry would be $119M in the first year, and $82M in each subsequent year. 

• EPA’s estimate of compliance costs for all sectors is approximately $165 million in the first year, 

and $95 million per year in subsequent years. Thus, the ISMI-estimated costs for the 

semiconductor industry equal 72% of EPA’s estimate for first-year compliance across all sectors, 

and 86% of EPA’s estimate of the ongoing cost for all sectors, despite the industry’s very small 

contribution to overall emissions. 

• Disclosure of proprietary intellectual property – By requiring precise measurement and 

reporting of specific process steps, the rule will result in the disclosure of valuable trade secrets 

to foreign competitors and others. 

In order to preserve its legal rights, SIA filed a petition for review and a petition for reconsideration of 

the rule on January 30, 2011. The petition for reconsideration sets forth in considerable detail a number 

of concerns with the rule, including the concerns identified above. SIA’s goal is to work with EPA to

develop alternative, more reasonable and technically sound methods of reporting. 

2.2 SIA legal challenge and this ongoing cooperative effort moving towards a re-proposal 

In response to the petitions filed by SIA, SIA and the Agency have been engaged in a constructive 

dialogue to resolve our concerns and reach agreement on alternative reporting methods. SIA and 

member companies, in partnership with ISMI, have undertaken a significant effort to collect and analyze 

data from semiconductor fabs and fab tool manufacturers in support of our proposed alternatives. We 

have submitted considerable amounts of information to EPA over the past year, and EPA is reviewing 

this information on an ongoing basis. We are hopeful that, working together with EPA, the Agency will
February 28, 2012 

9 

find the information we are submitting to provide a sufficient basis for EPA to withdraw the existing rule 

and propose the alternative methods. 

2.3 The Work Plan and Schedule 

EPA and SIA have developed a work plan to set forth a blueprint with milestones and action items in 

order to ensure timely resolution of the outstanding issues. SIA and EPA are now working to implement

the work plan, and this submission is part of that continuing effort. 

Under the work plan, SIA is continuing to provide EPA with additional information and to respond to the 

Agency’s questions about the reporting alternatives, other concerns with the current rule, and the 

supporting data and analyses. EPA is reviewing this information, and we hope to develop the broad 

outlines of a proposed rule by April 2012 that ensures technical feasibility considering burden and cost. 

EPA then intends to initiate a new rulemaking based on the outcome of this broad outline, with a goal of 

issuing a final rule by Summer/Fall 2013. 

On February 3, 2012, SIA filed with EPA answers to the questions presented in the Appendix A document 

specific to the Etch Factor Method. That submittal refers to additional analyses that would be provided 

in February. This document addresses the additional analyses and fulfills that commitment. 

### 
February 28, 2012 

10 

3. Methodology 

3.1 Data gathering

At the time that the current Subpart I was adopted, EPA had limited data on emissions, emission factors 

and the performance of abatement systems. This was a significant concern when the rule was adopted. 

SIA and ISMI have undertaken the following data development activities in order to improve the 

emissions factors for fab F-Gas emissions, to create a data base on the performance of abatement 

devices, and to provide a basis of evaluating the appropriateness of those factors for the development

of a revised rule. 

3.1.1 Etch tool emissions testing data 

ISMI requested that etch equipment suppliers and semiconductor device manufacturers provide etch 

emissions characterization data so that more robust industry emission factors could be developed. ISMI 

has combined this emission factor data and the IPCC 2006 and NODA datasets to create the ISMI 

emission factor database. The combined database includes 976 input gas data points of which 883 are 

new data points from etch equipment manufacturers and seven facilities operated by one 

semiconductor device manufacturer. 

3.1.2 Abatement equipment destruction efficiency data 

 ISMI collected abatement system inventory data from 9 U.S. semiconductor manufacturing fabs 

and DRE test data for 72 abatement devices. 

3.1.3 Fab Gas use and level of abatement data 

ISMI collected gas use data for the 2009 and 2010 calendar years from 12 facilities, five 300 mm fabs 

and seven 200mm facilities. Fabs that participated in the gas use survey are representative of the 

breadth of U.S. semiconductor device manufacturing. These fabs manufacture products such as digital 

signal processors, analog devices, logic devices for automotive and networking, embedded flash 

memory, DRAM and NAND flash memory devices, high end main frame, server and graphic intensive 

game devices, and leading edge logic devices. Linewidths of devices manufactured in responding fabs 

range from 250nm down to 32 nm. 

a. 3 of these facilities, one 200mm and two 300 mm, have included an IPCC Tier 3 analysis. 

b. ISMI provided a description of the gas apportioning method used at the facilities providing Tier 3 

analysis at the November 17th presentation and the February 3rd filing in response to the work 

plan. 

c. All five of the 300 mm fabs have abatement systems, with one 300 mm facility having full F-Gas 

emissions abatement. Each of the 300mm fabs provided information to ISMI on the level of FGas abatement installed. 
February 28, 2012 

11 

3.2 The 2-Factor and 3-Factor models 

SIA, working with ISMI, used the data detailed above to perform a statistical analysis of the expanded

emissions factor data base to achieve the following objectives: 

• Determine the modeling approach which best characterizes etch emissions considering 

precision, accuracy, and technical feasibility. 

• Calculate and compare the precision and accuracy of the emissions estimates determined using 

candidate models in comparison to Tier 2b, Tier 2c, and Tier 3. 

• Demonstrate that the model chosen provides good precision and accuracy. 

The ISMI 2011 database consists of F-Gas emission characterization data covering steps performed in 

semiconductor plasma etch equipment. This data was used to model emission factors for both main 

input gases and byproduct gasses. 

The ISMI 2011 database consists of both etch and “non-etch” steps performed in plasma etch 

equipment. The following definitions apply: 

• Etch Step: Step where a pattern is being etched in a product wafer. An etch step may consist of 

several substeps that use different quantities and mixtures of gases. 

• Non-etch Step: Process step run on an etch tool that do not involve etching a patterned wafer. 

Non-etch processing include aging / cleaning / conditioning / seasoning / dry clean season. Nonetch processing typically occurs infrequently (several times per 25 wafer lot); whereas etch 

processing occurs on all 25 wafers in a lot. 

• Data set: Measured F-Gas emissions factors for a specific input F-Gas and associated F-Gas 

byproduct(s). 

• Data point: Each measured emission factor whether for input gas or byproducts. 

• Tier 2c Emission Factors: Default factors listed in Tables I-3 and I-4 of Subpart I. 

• Refined Emission Factors: Default factors developed using this ISMI 2011 data set. 

• Principal film type: The principal film being etched from a patterned wafer, i.e., oxide, nitride, 

silicon or metal. 

The database consisted of 890 data sets as of the initial analysis and presentation on August 11, 2011. 

Several data points were subsequently added as were the unique portions of the EPA Tier 2C database. 

The database contains 976 data sets as of February 23, 2012. 

The “2-Factor” model has emissions factors as a function of the F-Gas used and wafer size, those being

the two factors. The “3-Factor” model has emissions factors as a function of these two factors plus film 

type. The decision to develop and evaluate the 2-Factor and the 3-Factor models was based on the 

results of a statistical analysis of the relationships between the key etch process parameters and the

emissions factors. 
February 28, 2012 

12 

3.2.1 Factors considered at the outset 

At the outset, 13 possible emission factor modeling parameters were considered: 

1. Wafer Diameter (mm) 

2. Power (Watts) 

3. Pressure (milliTorr) 

4. Flowrate (SCCM) 

5. Multiple Gases (Y/N) 

6. Stabilization (Y/N) 

7. Ratio of Stabilization to Etch Time 

8. Principal Film Type (oxide, metal, nitride, silicon) 

9. Feature Type (text field) 

10. Film Stack (Y/N) 

11. Measurement Protocol (text field) 

12. Process Type (Etch, Non-Etch) 

13. Input Gas Name (text field) 

An exploration of the modeling space was conducted using these 13 possible parameters. The initial 

models were built using the Etch Process Type data only. The distribution of etch versus non-etch data 

is shown in Figure 3-1. 

 Figure 3-1: Distribution of Process Type: Etch vs. Non-Etch Steps 
February 28, 2012 

13 

Statistical analysis of variance (ANOVA) modeling was conducted using the JMP software system. 

Models were constructed for each parameter individually as well as for all parameters and their 

interactions together. Several of these input parameters had too much missing data to be used in the 

modeling. Models that were built using interactions tended to have extensive singularities and were 

discarded. Some of these parameters are highly correlated over parts of the process space. For highly 

correlated parameters, only one can be used in the modeling. The parameter that better explains the 

emission factors changes is the preferred choice. 

The outcome of this exploration in shown in Tables 3-1 (Parameters with no Effect) and Table 3-2 

(Parameters with Effect). 

Table 3-1: Parameters with No Effect 

Parameter Notes

Feature Type Missing Data 

Multiple Gases No difference 

Flowrate No difference 

Ratio of Stabilization to Etch Time Missing Data 

Film Stack Missing Data, No difference 

Measurement Protocol Appeared correlated with other factors 

Stabilization 97% have stabilization 

Table 3-2: Parameters With Effect 

Parameter Notes

Wafer Diameter 59% is 200mm data 

Power 0.2 to 5100 W 

Input Gas 11 input gases 

Principal Film Type 4 Types 

Pressure 0 to 2000 mTorr 

Process Type 2 Types – analyzed separately 

3.2.2 Selection of the Two Factors and the Three Factors. 

The exploration of possible models resulted in the summary shown in Table 3-3. For individual factor 

models, input gas gave the largest R2

 value, followed by power and wafer diameter. However, there are 

correlations amongst these parameters. 

Detailed exploration of the distribution of the fits of the parameters was also performed. The data 

contains various spikes, clumps and clusters. One or two distributions could not be found to fit the data 

when broken down by parameter. Normal distributions were used. 
February 28, 2012 

14 

The R2 for several combined models, 2-factor and 3-factor, are also given in Table 3-3. Note the 

relatively small effects of power and principal film type in these multi-factor models. Adding additional 

parameters beyond these 3-factor models made no appreciable difference to the R2

 values. 

A decision was made to select two possible models for further consideration: a two factor model and a 

more complex three factor model with the potential of higher R2. With power and principal film type 

both playing a roughly equal but small role; a choice had to be made between them. Examination of 

power showed high stratification of the data by gas type along with distinct clustering. Because 

stratification and clustering can skew statistical analysis and possibly create false model results, principal 

film type was chosen 

A second analysis was then conducted by adding process type (etch vs. non-etch steps) to these two 

models. For the two factor model, only SF6 showed a statistical difference in emission factors for etch 

vs. non-etch steps. The decision was then made to include both of these (etch, non-etch steps) in the

analysis and calculation of emission factors. 

3.2.3 Data issues presented by the three factor model 

Two models were carried forward in the analysis. However, there are difficulties with emission factor

calculations when the data is broken into the smaller and smaller distinct groups that come with 

additional model parameters. The number of distinct groups and summary of these model approaches 

is give below: 

• 2-Factor model (Gas, Wafer Size) has 19 categories with associated gas use and gives a 57% R2

. 

• 3-Factor model (Gas, Wafer Size, Film) has 43 categories with associated gas use and gives 60% 

R

2

; this is much more complex and impacts only the etch portion of industry’s F-Gas emissions 

estimate. 

Table 3-3: Summary of Models Considered – Etch Data Set 

Model Parameter(s) Model R2

Input Gas 55% 

Principal Film Type 5% 

Power 18% 

Pressure 4% 

Wafer Diameter 12% 

Wafer Diameter, Input Gas 57% 

Wafer Diameter, Input Gas, Principal Film Type 60% 

Wafer Diameter, Input Gas, Power 59% 
February 28, 2012 

The 3-factor model has more than twice as many 

Factor model. Any potential benefit of a small model improvement (3%) 

come at the expense of having to apportion gas use 

error being introduced) and the need to use data at a level of detail that is considered confidential 

business information. 

3.2.4 Treatment of wafer clean 

SIA recommends that wafer clean be combined with etch into a single category for purposes of 

developing emissions factors and tracking emissions. Etch is defined by EPA as “a process type that 

consists of any production process using fluorinated GHG reagents to selectively remove materials from 

a substrate during electronics manufacturing.” Wafer cleaning is similar to etch in that 

etch F-Gases are used to remove contaminants or residues from the wafer surfac

designated for wafer cleaning use the same gases in plasma to remove these materials and 

emissions are similar. Wafer cleaning currently represents 1% or less of the gas use for the 12 facilities 

analyzed here as representative of the industry.

rule (and gas apportioning effort) can be eliminated as the gases in question

for etch and wafer cleaning. Further

emissions factors are similar for the two major gases: SF6 and CF4. 

not been integrated in the etch emission factors

Table 3-4: Comparison of Emissions Factors

3.3 Determination of the etch emission factors

Emission factors were calculated by averaging the emission factor data over the categories in the 2

Factor and 3-Factor models. This approach was chosen for simplicity and its 

Two types of emission factors were calculate

factors. A total of 976 etch and non

Byproduct emission factors have a total of 1221 data points.

accordance with the ISMI method described later in this section

3.3.1 Data analyses 

The input gas emissions factors for the 2

shown in Figures 3-2 and 3-3. The diamonds represent the standard error.

300mm emission factors are lower than 200 mm emission factors. Newer technologies present in 

15 

factor model has more than twice as many populated factor combinations or categories as the 2

Factor model. Any potential benefit of a small model improvement (3%) in the 3-factor model would 

apportion gas use to the larger number of categories

and the need to use data at a level of detail that is considered confidential 

SIA recommends that wafer clean be combined with etch into a single category for purposes of 

developing emissions factors and tracking emissions. Etch is defined by EPA as “a process type that 

ess using fluorinated GHG reagents to selectively remove materials from 

a substrate during electronics manufacturing.” Wafer cleaning is similar to etch in that 

es are used to remove contaminants or residues from the wafer surface. The tools specifically 

designated for wafer cleaning use the same gases in plasma to remove these materials and 

emissions are similar. Wafer cleaning currently represents 1% or less of the gas use for the 12 facilities 

sentative of the industry. A significant amount of gas apportioning burden of this 

) can be eliminated as the gases in question, except NF3

for etch and wafer cleaning. Further, as shown in Table 3-4, our data indicate that wafer cleaning 

emissions factors are similar for the two major gases: SF6 and CF4. However, wafer cleaning data has 

not been integrated in the etch emission factors used in this analysis. 

4: Comparison of Emissions Factors for Etch and Wafer Cleaning

Determination of the etch emission factors

Emission factors were calculated by averaging the emission factor data over the categories in the 2

Factor models. This approach was chosen for simplicity and its direct linkage to the data. 

of emission factors were calculated: input gas emission factors, and byproduct gas emission 

etch and non-etch data points were used for the input gas emission factors. 

ors have a total of 1221 data points. Byproduct factors were calculated in 

described later in this section. 

The input gas emissions factors for the 2-Factor and 3-Factor models, along with standard erro

. The diamonds represent the standard error. Note that, in general, 

300mm emission factors are lower than 200 mm emission factors. Newer technologies present in 

categories as the 2-

factor model would 

the larger number of categories (with associated 

and the need to use data at a level of detail that is considered confidential 

SIA recommends that wafer clean be combined with etch into a single category for purposes of 

developing emissions factors and tracking emissions. Etch is defined by EPA as “a process type that 

ess using fluorinated GHG reagents to selectively remove materials from 

a substrate during electronics manufacturing.” Wafer cleaning is similar to etch in that a subset of the 

e. The tools specifically 

designated for wafer cleaning use the same gases in plasma to remove these materials and the F-Gas 

emissions are similar. Wafer cleaning currently represents 1% or less of the gas use for the 12 facilities 

A significant amount of gas apportioning burden of this 

except NF3, are only used 

data indicate that wafer cleaning 

wafer cleaning data has 

for Etch and Wafer Cleaning

Emission factors were calculated by averaging the emission factor data over the categories in the 2-

direct linkage to the data. 

input gas emission factors, and byproduct gas emission 

for the input gas emission factors. 

Byproduct factors were calculated in 

h standard errors, are 

Note that, in general, 

300mm emission factors are lower than 200 mm emission factors. Newer technologies present in 
February 28, 2012 

300mm tend to result in lower emission factors. In part

much lower in 300mm. For other gases such as C4F8, CH2F2 and NF3, the emission factors are very 

close between 200mm and 300mm.

 Figure 3-2: Two factor model emission factors, with standard error diamonds

Figure 3-3: Three factor model emission factors, with standard error diamonds

3.3.2 Treatment of byproducts 

Byproduct emission factors were calculated for the 2

considerations when working with byproducts.

1. How are byproducts assigned when there are multiple input gasses in the test?

2. When a test results in no byproduct for a particular gas, is this treated as zero byproduct or is 

the test ignored? 

Regarding consideration 1, Table 3-

2-factor model, using industry-level

16 

300mm tend to result in lower emission factors. In particular, SF6, CF4 and CHF3 emission factors are 

much lower in 300mm. For other gases such as C4F8, CH2F2 and NF3, the emission factors are very 

close between 200mm and 300mm.

2: Two factor model emission factors, with standard error diamonds

3: Three factor model emission factors, with standard error diamonds

Byproduct emission factors were calculated for the 2-Factor and 3-Factor models. There are several 

considerations when working with byproducts. 

ow are byproducts assigned when there are multiple input gasses in the test?

When a test results in no byproduct for a particular gas, is this treated as zero byproduct or is 

-5 shows the results of calculating F-Gas emissions in mtCO2e for the 

level data, in the two cases: 

icular, SF6, CF4 and CHF3 emission factors are 

much lower in 300mm. For other gases such as C4F8, CH2F2 and NF3, the emission factors are very 

Factor models. There are several 

ow are byproducts assigned when there are multiple input gasses in the test?

When a test results in no byproduct for a particular gas, is this treated as zero byproduct or is 

Gas emissions in mtCO2e for the 
February 28, 2012 

17 

• All byproduct data is used; both for single input gas test and multiple input gas tests following 

the ISMI method.5

• Only byproduct data from tests with a single input gas are used. 

We can see that the choice of approach does not have a large effect. Therefore, all byproduct data for 

both single and multi-gas tests was used to estimate the byproduct emission factors used in the analysis 

presented in this paper. 

Regarding consideration 2, the current approach has been to ignore those tests where the byproduct 

gas was not detected. For example, take the case of ten separate tests conducted on an input gas X. If 

nine of these tests produce no detectable byproduct of gas Y and one does, then the current method 

establishes the byproduct emission factor based on the one test of the byproduct Y from gas X and does

not take into consideration the measurements where byproducts are not detected. SIA makes no 

specific recommendation on this matter. The current approach was adopted to be consistent with 

previous work done by the EPA and IPCC. 

Table 3-5: Byproduct methodology comparisons – 2-Factor Model 

This methodology has implications for the error calculations in later analysis as well as in the estimation 

of byproduct emissions, because byproduct emissions may be overestimated. The current method, in 

effect, assigns the byproduct to the input gas in all cases where that input gas is used, but applies an 

emission factor that was computed only from tests where the byproduct was detected. Tests where the 

byproduct was not detected or was too low to measure are ignored, possibly inflating the result. Error

calculations will be effected because of the artificially low value of N, the number of data points 

available. Fewer data points generally result in higher expected errors. In general, we think this 

 

5

 As previously discussed and documented in “ISMI Response to EPA Etch Data Questions” submitted to EPA in 

November, 2011, for multi F-gas processes, ISMI assigns byproducts to the total mass of all input F-gases. In 

contrast, IPCC assigned byproducts to the input gas with the largest mass flow. Neither the IPCC nor the ISMI 

method is completely accurate for multiple etch gas processes. However, the ISMI method tries to account for 

empirical data which shows that all of the F-gases input to a plasma process contribute in some way to the 

formation of F-gas byproducts and that byproducts are formed in plasma reactions that would not otherwise be 

formed following conventional thermodynamic pathways. 
February 28, 2012 

18 

approach overestimates both the byproduct emissions and their associated errors but we have chosen 

to use this approach to be consistent with the IPCC requirements. 

3.3.3 Filling in the data gaps 

ISMI has collected data and created a database containing 976 etch emission data sets. This database: 

• Has 10 times the data in the NODA and IPCC database

• Contains broad coverage of etch process types and gases 

This data is considered representative of the industry as a whole because of its size, the presence of a 

measured EF for almost every gas, and the use by tool manufacturers of similar process chemistries to 

accomplish the various etches used in the industry. Etch process are similar for technology types; 

memory and logic. In addition: 

• Manufactures do not generally make specific tools for a product type 

• Memory and logic functions are increasingly integrated 

• 300 mm and above process development results from cross industry collaboration 

Looking at the data specifically by gas, wafer size and tool supplier, Figure 3-4 shows the current data 

gaps. One can see that there are two gases for which there is presently no data: C5F8 for 200mm and 

C3F8 for 300mm. Both are used in very small amounts. While we believe that the current database of 

emissions factors is sufficiently representative to create and compare the two and three factor models, 

ISMI has commitments from several tool manufacturers and a device manufacturer to generate 

additional data points to close most of the identified data gaps. This is discussed further in Section 6. 

 Figure 3-4: Data Coverage 

Etch F-Gas 

Use Totals*

7 fabs

(kg) 

Supplier 

A

Supplier 

B

Supplier 

C

Supplier

D

Etch F-Gas 

Use Totals*

5 fabs

(kg) 

Supplier 

A

Supplier 

B

Supplier 

C

Supplier

D

CF4 35,632 CF4 44,541 

C2F6 13,666 C2F6 - 

SF6 30,295 SF6 8,451 

CHF3 8,156 CHF3 10,168

NF3 6,483 NF3 43,609

CH2F2 398 CH2F2 2,798

CH3F 245 CH3F 481

C4F6 8 C4F6 3,704 

C5F8 164 C5F8 1,619 

c-C4F8 11,754 c-C4F8 10,628 

C2HF5 - C2HF5 - 

C3F8 - C3F8 45

ISMI database contains emissions factors

Gas is used on supplier's equipment; no emissions data in ISMI database.

* - Aggregated total usage for 2009-2010.

Process Gas Process Gas

200 mm 300 mm
February 28, 2012 

19 

3.4. Determining Error 

The analysis presented in this report places particular emphasis on the determination of the likely errors 

in emission estimates of the several estimation methods. Two primary sources of error are considered: 

(1) uncertainties in the emission factors used in the emission calculations; and (2) uncertainties in the 

gas use quantities apportioned by EPA process type (and by etch film type in the SIA 3-Factor model), 

which convert emission factors into emission estimates. 

Emission calculations are performed in the later stages of the analysis to show the effect of F-Gas 

abatement on emissions and error estimates, to provide a better estimate of the performance of the 

preferred estimation method in actual practice. Because the consideration of abatement is not central

to the comparison of the alternative estimation methods, and because its overall effect is to significantly 

reduce the CO2e emissions estimate, no effort has been made to account for uncertainties in the DREs 

that are used. 

While the early work on precision and accuracy of the Etch Alternative that was presented in November 

used a concept of “worst case” error for this purpose, SIA has since adopted a revised approach in the

present work to estimate the “expected error”. The expected error analysis is based on the usual 

statistical rules for the combination of variances that result in combining independent errors on a “root 

mean square” basis. Two forms of error analysis have been employed in this work – error propagation 

and Monte Carlo analysis. The forms of analysis give comparable (but not identical answers) and so the 

error results presented here clearly identify the method used. 

The analysis was structured to follow two paths. First, a precision and accuracy analysis was conducted 

to examine the performance of four different emission estimation methods (IPCC Tier 2B, EPA Tier 2C, 

SIA 2-Factor, and SIA 3-Factor). Error propagation methods were used to assess their relative 

performance based primarily on precision of estimate. In this, only the errors introduced by uncertainty 

in the underlying emission factors were considered, because this error source will be the major 

determining factor of the relative performance of the methods. 

Second, Monte Carlo analysis was conducted for the subset of best performing methods, which prove to 

be the SIA 2-Factor and 3-Factor models. Monte Carlo methods involve the explicit simulation of 

variability in a process or calculation by enumerating the different outcomes of random processes. The 

simulation nature of the Monte Carlo method permits it to provide a more detailed assessment of errors

and so has been used to account for the effects of both emission factor errors and gas apportioning 

errors on emission estimates. Where parallel error analyses have been done, the results of the error 

propagation method compare well to those of the Monte Carlo method. 

3.4.1 Error Propagation 

Error propagation has been used from the start of ISMI’s and SIA’s analysis to provide a rapid 

assessment of errors to permit us, particularly in the early stages of the analysis, to look at a range of 

datasets, different methods for preparing emission factors, alternative statistical models for emission 

factors, and different estimation methods. Error propagation has allowed us to track the relative 
February 28, 2012 

20 

precisions of the numerous cases involved and to update those estimates quickly as the underlying data

and models have changed. 

Error propagation is a reliable tool for combining uncertainties to determine the likely error in a 

calculated result and is well accepted in science and engineering. It is the first of two methods for

combining uncertainties discussed by IPCC in Section 3.2.3.1 of its Chapter 3 on Uncertainties (Monte 

Carlo being the other). In that discussion, IPCC notes that certain assumptions are normally made in 

taking this approach, specifically that the uncertainties being combined are uncorrelated and that the

relative errors (standard deviation divided by the mean) are 0.3 or less. It also notes: “In practice, 

however, the approach will give informative results even if this criterion is not strictly met and some 

correlations remain.”6

Note that the IPCC discussion does not limit use of the error propagation technique to instances where

the data are normally distributed. The “central limit theorem” is one of the reasons that conventional 

statistics, in general, and error propagation, in particular, are useful research tools. The theorem says 

that, except when dealing with small samples, the mean value calculated for a variable X is 

approximately normally distributed even if the variable X is not normally distributed itself. ISMI has used 

error propagation in its assessment of industry-average and facility-level emissions. In both instances, 

emission factors representing sources are being averaged and we are then estimating the relative error

of the overall emissions estimate. In this context, it is an appropriate approach without regard to the 

underlying data distributions. 

The previous error propagation work presented in November of 2011 used a form of the worst case 

error. Error in emissions factors (EFs) and gas usage were both combined, at their 95% confidence 

levels, to provide upper and lower bounds for error. That is the 95% error for emission factors was 

combined with the 95% error for gas usage to provide a form of worst case error. A standard value of 

1.96 was used to calculate the 95% confidence intervals. We now switch to expected error calculations

for the propagation of error calculations. 

Expected errors in mtCO2e are propagated using a calculation of the square root of the sum of the 

squares of the standard errors for the components of interest; input gas emissions factors, byproduct 

emission factors, gas usage. The final, effective standard error for any given leg of a model is given by: 

  

    	

The individual standard errors s1, s2 ... are calculated as: 

 






 

6

 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 1: General Guidance and Reporting, 

Chapter 3 Uncertainties, p. 3.27. 
February 28, 2012 

21 

The lower and upper 95% confidence limits are then given by: 

 Lower 95% CL = mean - t (.025,DOFe) * sef 

 Upper 95% CL = mean + t (.025,DOFe) * sef 

Where: 

DOFe is the effective degrees of freedom 

t is the t statistic. 

Because the degrees of freedom (N-1) differ between the components of interest, the Welch–

Satterthwaite (WS) equation is used to compute the effective degrees of freedom and the confidence 

intervals of the result. The WS equation is given by the form 











 





 	 









  1











  1

 	

where: 

si is the standard error of the component of interest

Ni is the number of data points for the component of interest 

Additional considerations are needed for EFs that are missing (N=0) and represented by default values,

and for EFs where insufficient information is available to compute a standard deviation (N=1). For 

missing EFs, in this most recent work, we have set the average value to 0.50 with an error range from 0 

to 1. (In the earlier work the factor for missing Efs was set at 1.0 to give the most conservative 

treatment possible.) For an EF with N=1, we use the one available measurement as the average EF, and 

adopt the approach of symmetric errors. For example, if the one available measurement has the value 

0.20, we assume the true value could be as low as 0.00 or as high as 0.40. 

The following specific rules apply to missing input gas emissions factors or missing error terms. 

• Missing emissions factors: 

– Emission factor is set to 0.5 

– Upper possible emission factor is set to 1 

– Lower possible emission factor is set to 0 

– Std. Err. is set to the Emission Factor / 1.96 

– N is set to 2, Degrees of Freedom is set to 1. 

• Emission factor exists but missing lower and upper due to no error terms: 

– For emission factors > 0.50 
February 28, 2012 

22 

— Upper Possible = 1 

— Lower Possible = 2 * Emission Factor – 1 

— Std. Err. is set to (1 - Emission Factor) / 1.96 

– For emission factors <= 0.50 

— Upper possible = 2 * Emission Factor 

— Lower possible = 0 

— Std. Err. is set to the Emission Factor / 1.96 

– N is set to 2, Degrees of Freedom is set to 1. 

The following rules apply to missing byproduct emission factors or error terms: 

• Missing emissions factors: 

– Emission factor is set to 0 (consistent with the Final Rule). 

– Upper possible emission factor is set to 0 

– Lower possible emission factor is set to 0 

• Emission factor exists but missing lower and upper due to no error terms: 

– Same procedure as used for the input gas emission factors 

3.4.2 Monte Carlo Error Analysis 

Monte Carlo is the second of two methods for combining uncertainties that is discussed by IPCC in 

Section 3.2.3.1 of its Chapter 3 on Uncertainties. In this, it identifies Monte Carlo as being “...suitable for 

detailed category-by-category assessment of uncertainty, particularly where uncertainties are large, 

distribution is non-normal, the algorithms are complex functions and/or there are correlations between

some of the activity sets, emissions factors, or both.”7

 While the Monte Carlo method has some 

advantages for the assessment of emission factor errors, we have used it in the error analysis primarily 

for the purpose of simulating the emissions effect of gas apportioning errors and how they interact with 

emission factor errors. 

The Monte Carlo analysis performs straightforward emission calculations on matrices of gas usages (U) 

and emission factors (EF) into which random errors have been introduced to represent the effect of gas

apportioning errors in U and emission factor errors in EF. The dimensions of U and EF depend on the 

structure of the emission estimation method (whether SIA 2-Factor or 3-Factor) and add a final 

dimension for the number of independent trials performed. In this analysis, T=1000 independent, 

parallel emissions calculations were performed from which the standard deviation and the lower/upper 

95% confidence intervals of the distribution of results were determined. The error standard deviation

 

7

 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 1: General Guidance and Reporting, 

Chapter 3 Uncertainties, p. 3.32. 
February 28, 2012 

23 

was calculated by the usual statistical formula, while the lower and upper 95% confidence interval 

bounds were calculated as the empirical 2.5th and 97.5th of the distribution of results, respectively. 

The Monte Carlo was structured to permit the analysis of emission factor errors alone, gas apportioning 

errors alone, or the combination of both error sources. To consider emission factor errors alone, the gas 

use matrix U was generated to use the same average values, without error, for each of the T=1000 trials. 

To consider gas apportioning errors along, the emission factor matrix EF was generated to use the same

average values, without error, for each of the T=1000 trials. To consider both error sources, the U and 

EF matrices were generated to use independently chosen random estimates for the gas use quantity U 

(in a manner consistent with sharing total gas use) and the emission factor value EF for each cell in the 

emission estimation method. 

Emission factor errors were generated by sampling randomly from the EF distributions derived from the 

SIA database, following Eq. 3-1 and Eq. 3-2a and b above. For cells where an error analysis could not be 

done, the Monte Carlo method assumed that a missing EF (N=0) can range from zero to one with 

uniform probability and an average value of 0.50, and assumed that an EF with N=1 can similarly range 

from its symmetric lower to upper limits with an average value equal to the one available measurement.

Gas apportioning errors deal with the uncertainty in the gas usage quantity U in a process cell of the SIA 

2-Factor model or in an etch film type or non-etch process cell in the SIA 3-Facgtor model. These errors 

are more difficult to characterize in the analysis, because there is little available data on which to base 

the assessment. The following section describes the treatment of gas apportioning errors in the 

analysis. 

3.4.3 Approach to Gas Apportioning Error 

It is difficult to estimate the quantities of gas used in individual production processes and steps in a 

semiconductor facility, and it is not possible to measure actual usage because the points at which gases 

are used are widely distributed throughout a fab. The only information known to exist on the 

magnitude of gas apportioning errors comes from a modeling exercise conducted by one of SIA’s 

member companies to support internal emission estimation efforts at its facilities. The data and the 

methods used to generate the data were described to EPA at the November 17, 2011 meeting in 

Washington DC and in SIA responses to EPA Question 2 in EPA Etch Question Responses 02032012.pdf 

submitted by SIA on February 3, 2012. 

In brief, the modeling exercise consists of a bottom-up approach to estimate F-Gas use at fabs 

considering factors for the typical gas usage in each production process and counts of the number of the 

processes performed in each of two calendar years. Predicted gas use at the process level was totaled

to the fab level and then compared to actual gas use as determined from accounting records. This 

process produced a total of 105 data points for the percent difference between the modeled (predicted)

F-Gas use per facility and the actual (purchased) F-Gas use per facility for 10 F-Gases over two years at 8 

facilities (see Table 3-6). The values given in the table represent the errors encountered in estimating 
February 28, 2012 

24 

total gas use at the facility level, based on the difficulty of, and errors encountered in, modeling gas use 

at more-detailed process and production step levels. They are used as inputs to drive the error analysis. 

In implementing a reporting rule, individual fabs would need to develop methods to translate their 

estimates of total gas usage at the fab (from purchase records and other measurements) into estimates 

of allocated gas usage at a level that depends on the estimation being used. For example: 

• Gas usage would be allocated to each of the five EPA processes (etch, wafer cleaning, and three 

chamber cleaning processes) if the SIA 2-Factor model were used. 

• Gas usage would be allocated to the four etch film types and the four non-etch EPA processes if 

the SIA 3-Factor model were used. 

Once a model was developed and adopted, its estimates for total gas usage at the fab would be turned 

into estimated shares of gas use at more detailed levels and then applied to allocate total gas usage 

down to the level required for emission calculations. It is the errors in the shares that matter for 

purposes of the emission calculations, not simply the errors in estimating total gas use at a facility. 

The methods developed for modeling gas usage would vary from fab to fab, and will not necessarily be 

the same as the methods used in the modeling exercise that produced the error estimates in the table 

below. Thus, we have taken a simple, generic approach to translate the values in the table into 

estimates of the errors in the shares used for gas apportioning. 

Table 3-6: Gas Use Apportioning Errors: 

Error Standard Deviation Across Facilities 

Including both 200mm and 300mm Fabs 

Input Gas ± 1 Std Dev (%) 

 SF6 31 

 CHF3 40 

 CF4 46 

 C2F6 42 

 NF3 31 

 C4F8 20 

 CH2F2 59 

 C5F8 69 

 CH3F 71 

 C4F6 27 

Source: Modeling of F-Gas use over two years at 

eight facilities. 
February 28, 2012 

25 

The starting point is an estimate of the error in estimating total gas use at the facility level. Then, if the 

error in modeling total gas use is ± 10%, for example, and the gas is used in four different processes in 

the SIA 2-Factor model, we assume that the estimates of total gas use by process have errors of ± 20% in 

each process cell – that is, ± 10% * sqrt(4) – to be consistent with the facility-level result. If the gas 

were used in etch and three other non-etch processes in the SIA 3-Factor model, then we would assume 

that the estimates of total gas use by film type and process would have errors of ± 26% in each cell –

that is, ± 10% * sqrt(7). Effectively, we are assuming equal percentage errors at the more-detailed 

levels of a size consistent with the input error at the facility level. Other methods of assigning errors at 

the process cell level would give different results at those levels, but we have seen in the analysis that 

these values are simply starting points that are modified when modeled estimates are turned into 

shares. We believe that the basic results and conclusions of the error analysis for gas apportioning are 

robust and would not be substantially changed if different assumptions were used to assign the starting 

point errors. 

As implemented in the Monte Carlo model, we first estimate gas use by cell by drawing from the 

distribution that has an average value equal to the known total gas use (kg) in that cell and a standard 

deviation equal to the inferred error percentage for the cell times the average gas use. Processes using 

little gas have small gas-use errors in kg terms, while processes that use large quantities of gas have 

large gas-use errors in kg terms. The estimated gas usage values by cell are then turned into shares, 

which are used to allocate the known total gas use down to the cells. Then, each of the 1000 trials is 

turned into an estimate for total Etch F-Gas Emissions and Total F-Gas Emissions including non-etch 

processes. The standard deviation of the emission estimates is the measure of the effect of 

apportioning error on the emission estimates. 

The gas apportioning problem is one in which the gas usage error in any one process is correlated with

the errors in the other processes for that gas because the total kg usage error must be zero when 

summed across the cells to the facility level. From this, emission errors induced in one process by errors 

in apportioning gas use are correlated with errors in the other processes. For example, if apportioned 

gas use in etch has erred on the high side in one Monte Carlo trial, this error will push etch emissions up. 

But that also means that apportioned gas use in the other processes must (in the net) err on the low 

side and therefore push non-etch emissions down, so that the errors are correlated across processes. 

The correlation of errors is properly accounted for by the methods used in the Monte Carlo model to 

apportion gas use to the cells and then turned into emissions estimates. Where results of the analysis 

report the errors in Etch Emissions, the values for etch will be magnified because they will show only

one side of the correlated errors. In every case, however, we also report the errors in Total F-Gas 

Emissions, including etch and non-etch processes. These results properly characterize the net effect of 

gas apportioning errors on total emissions, accounting for the net effect of the correlated errors. 

As a prelude to the presentation of results in Section 5, we note that the gas apportioning problem is

complex because it depends on a number of factors that may defy easy intuition, but that the Monte 

Carlo takes full account of. Two general observations may be helpful in understanding the gas 

apportioning problem and its results. 
February 28, 2012 

26 

1. The distribution of gas use by cell and the process of computing shares across the cells has as 

much (or more) influence on the size of the gas-use errors in each cell than the input values for 

the errors or the method for assigning errors to the cells. Once the shares are calculated and 

total gas use is allocated, the kg errors in one cell must offset the net kg errors in all other cells. 

If a cell’s gas usage is smaller than average, its percentage error will be larger than average. We 

have used one particular method for assigning the input errors to the cells (equal percentage 

errors), but this is just a starting point that the share calculation modifies. Other methods for 

assigning the errors – e.g., allocating a larger percentage error to etch than to non-etch 

processes – would be unlikely to change the results in a major way. 

2. Gas use and emissions are typically larger in non-etch processes than in etch, particularly in 

200mm fabs. As a result, the apportioning errors in non-etch have a much larger effect on 

overall emissions. If, hypothetically, it were possible to allocate gas use to etch without error, 

then the overall error would fall only by 10-20% (according to an approximate calculation). 

Therefore, errors in allocating gas use to non-etch processes dominate the overall effect of 

apportioning error on emissions. Since the non-etch part contributes in the same way to both 

the SIA 2-Factor and 3-Factor models, its presence tends to mute the appearance of the 

differences that will introduced by the greater degree of gas apportioning that is required to 

support the 3-Factor model. 

3.5. Assessing the Accuracy of Alternative F-Gas Emissions Estimating Methods 

To assess the accuracy of the estimation methods requires reference to some method that is believed to

be more accurate than the methods being considered. Three of the facilities participating in the SIA 

survey prepare emission estimates using a Tier 3 methodology. Because the Tier 3 methods utilize 

facility-specific emission factors, we have used the Tier 3 emissions estimates for the three facilities as 

the reference point against which the estimates from the other methods can be compared to determine 

accuracy. This facility-specific comparison provides a meaningful assessment of the relative accuracy of 

the alternative methods and the robustness of the industry level factors. It is accompanied by a proforma error analysis, considering only the uncertainties of the underlying emission factors, to provide a 

benchmark for whether the observed differences in emissions are large or small. 

3.6 Determining Emissions Error: Industry Level Analysis 

The emissions estimation method ultimately adopted by EPA in a final rule will be used by individual 

semiconductor facility to estimate the GHG emissions from a facility. EPA will then total the facilityspecific emission estimates to estimate the GHG inventory for the semiconductor industry as a whole. 

This report evaluates the several emission estimation methods based, first, on their accuracy and 

precision for estimating the industry-level inventory, and, second, on the precision achieved by the best 

methods when used for estimating facility-specific emissions. 

The performance of the methods for industry-level estimates are evaluated here using composite 

information representing the twelve fabs who participated in the SIA gas use survey and provided 

estimates of gas use at the EPA process level (etch, wafer cleaning, and three chamber cleaning 

processes) and also by the four etch film type. The twelve fabs include seven 200mm fabs and five 

300mm fabs. The industry-level analysis uses the total gas use for the appropriate subset of these 
February 28, 2012 

27 

twelve fabs as a representative surrogate for the industry. The industry-level analysis also treats them 

in the composite by wafer size, rather than individually, as if they were a single fab that used the same 

F-Gas quantities of the relevant subset of the twelve fabs. This approach ignores the fact that 

independent errors in the reporting of individual fabs would be partially attenuated when combined by 

EPA into an industry-wide inventory. Therefore, the actual precision of industry inventories should be 

better than the precision values estimated in the analysis presented in this report. 

The aggregated gas use distribution for all of the 200mm fabs and all of the 300mm fabs was provided to 

EPA in the earlier February 3, 2012 submittal. 

Industry-level analyses have been conducted using both the error propagation and the Monte Carlo 

methods. As a first step, the accuracy and precision analysis conducted by SIA has compared the 

performance of four different estimation methods based on the precision achieved in industry-level 

emission estimates based on the uncertainties of the underlying emission factors. Error propagation 

methods have been used for this purpose, in which the uncertainty of the underlying emission factors is 

assessed using Eqs 3-1 and 3-2 and depends on the number of data points underlying each emission 

factor. In this analysis of emission factor errors, gas usage values for the individual cells in the models 

are assumed to be known without error. 

The SIA assessment of the four estimation methods, based on the error propagation method, shows that 

the SIA 2-Factor and 3-Factor models achieve the best precision at the industry-level, considering the

uncertainties in the underlying emission factors. From this point, a more detailed extensive analysis of 

the two SIA models was conducted using the Monte Carlo method, considering both emission factor and 

gas apportioning errors. The result of this more detailed and extensive analysis shows that the two SIA 

models perform similarly in terms of the precision of emission estimates, with the SIA 2-Factor model 

giving somewhat more precise estimates in some cases due to its lesser demands for emission factor 

data. The analysis shows no instance in which the SIA 3-Factor model performs substantially better than 

the 2-Factor model and, therefore, would be preferred based on the precision of estimate. 

3.7. Determining Emissions Error: Facility Level Analysis 

At the end of the industry-level analysis, the SIA 2-Factor model was selected for further evaluation to 

determine its precision in estimating facility specific emissions. The precision achieved by the SIA 2-

Factor model at the industry level is determined by the precision of the underlying emission factors and 

by the effect of gas apportioning error based on the overall distribution of gas usage and the difficulty of 

allocating usage of each gas. Both considerations change when the model is applied to make emission 

estimates at the facility level. The distribution of gas usage will vary from fab to fab, leading to 

differences in the sources of emissions and the importance of apportioning errors for each gas. More 

importantly, however, the precision of the emission factors underlying facility estimates will differ from 

that achieved at the industry level. In general, there will be more variation in the underlying emission 

factors from fab to fab than there will be in the industry average, leading to a lower level of precision 

than can be achieved. However, as shown in the Results section, the performance of the SIA 2-Factor 

model is very good at the facility level and, in many cases will approach the level of performance 

achieved at the industry level. 
February 28, 2012 

28 

Facility-level emission estimates are the product of the usage of each input gas times the average 

emission factor for the gas that is specific to the facility. The precision of a facility-specific average 

emission factor depends on the number of distinct tool types and etch processes that are performed in 

the facility during a reporting period, which we will denote as NF or Facility N. Each distinct tool type / 

process combination has a characteristic emission factor that can be found somewhere in the overall 

distribution of emission factors for the gas in question that has been characterized by the mean and 

standard deviation of emission factor data in the SIA database. It is not necessary that the database

contain a matching test result for all of the tool types in use at a particular fab because the statistical 

treatment of the problem makes a hypothetical draw from the overall EF distribution. 

Conceptually, the precision of an industry-level estimate depends on the number of EF data points and 

their standard deviation according to the usual formula for the standard error: 

 Standard Error = σEF / sqrt( NDB ) (Eq 3-1) 

where σEF is the standard deviation of the emission factor distribution for tool type / process 

combinations found in the industry and NDB is the number of data points in the database. By similar 

reasoning, the precision of a facility-level estimate will depend on the number NF of emission factors 

needed to describe the NF distinct tool types and etch processes that are performed in the facility: 

Standard Error = σEF / sqrt( NF ) (Eq 3-2) 

If NF is small, then the facility-specific average EF can vary widely from the industry-average EF, but the

facility-specific average EF must approach the industry-average as the number of distinct tool type / 

process combinations increases. An emissions estimate based on industry-average EFs may be poor for 

a facility with only a few distinct tool type / process combinations, but it will approach the precision of 

the industry-level estimate as the number of distinct combinations increases. In practice, however, the 

improvement in precision will be limited for any given input gas or cell in the model by our knowledge of 

the industry-average EF. That is, it is the smaller of NF and NDB that should be used in Eq. 3-2 to estimate 

the precision of a facility-level average EF. 

Table 3-7. Facility Counts for Tool Type / Etch Process Combinations (Four Sample Fabs) 

(Each Distinct Combination is Represented by One Distinct EF) 

Input 

Gas 

200mm 300mm

Database 

NDB

*

Industry

Gas Use 

(% of kg) 

Fab B Fab C Database 

NDB

*

Industry

Gas Use 

(% of kg) 

Fab A Fab E 

C2F6 40 40% 4 7 1 0% 0 0

C2HF5 31 0% 0 0 0 0% 0 0

C3F8 0 9% 0 0 0 0% 0 0

C4F6 14 0% 2 0 37 1% 0 7

C5F8 0 0% 3 0 12 0% 4 3
February 28, 2012 

29 

c-C4F8 44 3% 11 13 77 3% 6 8

CF4 126 12% 22 23 75 12% 11 16

CH2F2 43 0% 6 4 38 1% 8 11

CH3F 1 0% 4 0 0 0% 7 3

CHF3 131 2% 20 26 65 3% 9 14

NF3 22 28% 7 0 52 77% 0 4

SF6 129 6% 12 13 37 2% 7 6

Total 581 100% 91 86 394 100% 52 72

*

 NDB values given are for primary emission factors. 

To assess the precision achieved by the SIA 2-Factor model at the facility level, two 200mm and two 

300mm fabs agreed to count the number of distinct tool types / etch process combinations using the 

same distinctions for tool type and etch process made in the SIA database. As Table 3-7 shows, each 

sample fab has a subset of the tool type / process combinations that are found in the SIA database. For 

example, the sample 200mm fabs contain 22 to 23 distinct tool type / process combinations that use 

CF4, compared to the 126 combinations that are characterized in the SIA database, while the sample 

300mm fabs contain 11 to 16 combinations compared to the 75 CF4 data points found in the database. 

The subsets of combinations present in the fabs give an appreciable coverage of the database (up to 

one-quarter in some cases) and are large enough to give good precision in the emission estimates for 

the major gases. 

The precision estimate developed in the facility-level analysis is one that accounts for the expected 

variation of facility estimates, based on industry-wide emission factors, around the true (but unknown) 

emission values for the facility. The chief differences compared to the industry-level analysis are that 

the facility level analysis recognizes that facility-specific average emission factors will vary around the 

industry average and that the profile of gas usage will differ from the industry average. Both emission 

factor errors and gas apportioning errors are accounted for in the analysis. 

3.8. Determining Emissions Error: Including F-Gas Abatement 

Most of the analysis presented in this report examines estimates of F-Gas emissions on an unabated 

basis, since abatement varies by fab and does not influence the relative comparison of the performance

of the estimation methods. However, the performance achieved in practice will be improved by the 

reduction in emissions due to abatement, which occurs primarily in the 300mm fabs. Each of the five 

300mm fabs participating in the gas usage survey abates one or more F-Gases. 

To better understand how the best performing model (SIA 2-Factor) would perform in practice, the five 

300mm fabs were asked to provide the information needed to estimate the net effect of abatement on 

emissions. Because abatement is uncommon in 200mm fabs, we have assumed no abatement for them 

in the analysis. 
February 28, 2012 

30 

The information provided by the 300mm fabs has been compiled to the industry level to estimate the 

fraction of F-Gas usage that is abated in each of the five processes. For the fraction of gas that is abated, 

we have applied the default DREs that were proposed by SIA in Table B-2 of the report “120110 SIA 

abatement briefing paper.pdf” submitted on January 10, 2012 and reproduced as Table 3-8 below. The 

default DREs are: 60% for CF4 used in etch; 99% for NF3 and other gases used in etch; 90% for NF3 used

in any of the chamber cleaning processes; and 60% for CF4 and any other gas used in chamber cleaning. 

One of the five fabs is fully abated and abates the small amount of NF3 used for wafer cleaning with the 

same equipment used to abate etch gases, which has been assigned the 99% DRE proposed for etch in 

this analysis. Although none of the other four fabs abates wafer cleaning gases at present, wafer 

cleaning is better viewed as an integral part of the etch process (as SIA has explained in Section 3.2.4) 

and, where abated, will be abated with the same equipment used for etch. 

F-Gas abatement in 300mm fabs is considered for the SIA 2-Factor model in the final industry-level 

analysis and in the facility-level analysis for the four sample fabs. 

Table 3-8. Proposed Default DREs Used in 

the Assessment of F-Gas Abatement in 300mm Fabs 

Process Gas 

Default 

DRE 

Basis 

Plasma Etch 

 Etch CF4 60% 

EPA rule, supported 

by SIA database 

 Etch 

NF3, SF6

All Other PFCs 

99% SIA database 

Chamber Cleaning 

 All Sub-processes NF3 90% SIA database 

 All Sub-processes Other Gases 60% EPA rule a/

N2O 

 All Processes N2O 60% EPA rule a/

a/ SIA database does not contain information characterizing abatement 

performance for these gases in chamber cleaning. 

###
February 28, 2012 

31 

4. Data 

In order to improve the emissions factors estimates, reduce the associated uncertainties, and provide 

representative facility gas use data, SIA and ISMI performed the following data collection activities:

1. ISMI has collected 976 etch emissions factors, including the IPCC 2006 and NODA datasets. This 

involved the collection of over 883 new data points from equipment manufacturers and seven 

facilities. 

2. ISMI collected gas use data for the 2009 and 2010 calendar years from 12 facilities, five 300mm 

facilities and seven 200mm facilities. 

In the next sections, data coverage and samples of input gas emissions factors are presented for the 

2=Factor and 3-Factor models as are actual F-Gas usage levels for 2009 and 2010. 

4.1 Etch emissions factors 

The data amounts (N) associated with the 2-Factor and 3-Factor models for input gas emission factors 

are given in Tables 4-1 and 4-2. 

Table 4-1: Data sets for the 2 factor model – 

input gas factors 

Input Gas Name Wafer 

Diameter Data Sets 

C2F6 200 40

C2HF5 200 31

C3F8 200 

C4F6 200 14

C5F8 200 

c-C4F8 200 44

CF4 200 126

CH2F2 200 43

CH3F 200 1

CHF3 200 131

NF3 200 22

SF6 200 129

C2F6 300 1

C2HF5 300 

C3F8 300 

C4F6 300 37

C5F8 300 12

c-C4F8 300 77

CF4 300 75

CH2F2 300 38
February 28, 2012 

32 

CH3F 300 

CHF3 300 65

NF3 300 52

SF6 300 37

Table 4-2: Data set for the 3 factor model – 

input gas factors 

Principal 

Film Type 

Input 

Gas 

Name 

Wafer 

Diameter 

Data 

Sets 

Nitride C2F6 200 1 

Oxide C2F6 200 39 

Oxide C2HF5 200 31 

Oxide C4F6 200 14 

Oxide c-C4F8 200 44 

Nitride CF4 200 24 

Oxide CF4 200 101 

Silicon CF4 200 1 

Nitride CH2F2 200 1 

Oxide CH2F2 200 42 

Nitride CH3F 200 1 

Metal CHF3 200 1 

Nitride CHF3 200 3 

Oxide CHF3 200 126 

Silicon CHF3 200 1 

Oxide NF3 200 20 

Silicon NF3 200 2 

Metal SF6 200 20 

Nitride SF6 200 61 

Oxide SF6 200 4 

Silicon SF6 200 4 

Oxide C2F6 300 1 

Oxide C4F6 300 37 

Nitride C5F8 300 12 

Nitride c-C4F8 300 4 

Oxide c-C4F8 300 73 

Metal CF4 300 2 

Nitride CF4 300 25 

Oxide CF4 300 27 

Silicon CF4 300 21 
February 28, 2012 

33 

Nitride CH2F2 300 25 

Oxide CH2F2 300 13 

Metal CHF3 300 1 

Nitride CHF3 300 6 

Oxide CHF3 300 43 

Silicon CHF3 300 15 

Metal NF3 300 4 

Nitride NF3 300 19 

Oxide NF3 300 18 

Silicon NF3 300 11 

Nitride SF6 300 2 

Oxide SF6 300 10 

Silicon SF6 300 25 

4.1.2 Sample factors – 2 factor and 3 factor models

Tables 4-3 and 4-4 show a sample of the input gas emission factors calculated for the 2 and 3-Factor 

models. Full sets of emission factors are in Attachment A. A blank data line indicates no data for that 

combination. Note that 200mm and 300mm manufacturing technologies will not use some of the gases 

in their etch processing steps. 

Table 4-3: Input gas emission factors for the 2 factor model - sample 

Input 

Gas 

Name 

Wafer 

Diameter

Data 

Sets 

Emission 

Factor Std Dev Std Err GWP100 

C2F6 200 40 0.76175 0.167839 0.026538 9200 

C2HF5 200 31 0.064387 0.029157 0.005237 2800 

C3F8 200 7000 

C4F6 200 14 0.16645 0.220904 0.059039 0.027 

C5F8 200 100 

c-C4F8 200 44 0.139814 0.09066 0.013668 8700 

 
February 28, 2012 

34 

Table 4-4: Input gas emission factors for the 3 factor model – sample 

Principal 

Film 

Type

Input 

Gas 

Name

Wafer 

Diameter

Data 

Sets

Emission 

Factor Std Dev Std Err GWP100

Metal C2F6 200 9200 

Silicon C2F6 200 9200 

Nitride C2F6 200 1 0.559 9200 

Oxide C2F6 200 39 0.766949 0.166738 0.0267 9200 

Nitride C2HF5 200 2800 

Metal C2HF5 200 2800 

Silicon C2HF5 200 2800 

Oxide C2HF5 200 31 0.064387 0.029157 0.005237 2800 

Nitride C3F8 200 7000 

Oxide C3F8 200 7000 

Metal C3F8 200 7000 

Silicon C3F8 200 7000 

Nitride C4F6 200 0.027 

Metal C4F6 200 0.027 

Silicon C4F6 200 0.027 

Oxide C4F6 200 14 0.16645 0.220904 0.059039 0.027 

4.2 F-Gas Usage 

4.2.1 Industry Level 

The industry level usages as represented by the sum of individual facility usage for five 300 mm facilities 

and seven 200mm facilities are shown in Tables 5 and 6. All units are in kg. Table 6 takes the Etch Usage 

category from Table 5 and breaks it down by principal film type. 

Table 4-5 – F-Gas usage 

Wafer 

Size Year Input Gas 

Number 

of FABS 

CVD In 

Situ 

Plasma 

CVD 

Remote 

Plasma 

CVD InSitu 

Thermal 

Wafer 

Clean 

Usage(kg) 

Etch 

Usage(kg)

200 2009 C2F6 7 90962.1 7038.0 6471.3 

200 2009 C2HF5 7 

200 2009 C3F8 7 20021.0 

200 2009 C4F6 7 3.3 
February 28, 2012 

35 

200 2009 C5F8 7 78.3 

200 2009 c-C4F8 7 442.0 4970.7 

200 2009 CF4 7 15290.8 1560.4 15676.1 

200 2009 CH2F2 7 126.9 

200 2009 CH3F 7 106.3 

200 2009 CHF3 7 82.2 3536.8 

200 2009 NF3 7 29562.2 50310.1 57.5 37.2 3991.7 

200 2009 SF6 7 391.2 0.8 12395.0 

200 2010 C2F6 7 88192.8 0.0 1105.6 7194.7 

200 2010 C2HF5 7 0.0 0.0 0.0 

200 2010 C3F8 7 24755.0 0.0 0.0 

200 2010 C4F6 7 0.0 4.6 

200 2010 C5F8 7 0.0 85.3 

200 2010 c-C4F8 7 4557.5 0.0 6783.3 

200 2010 CF4 7 8443.5 606.0 712.8 19956.0 

200 2010 CH2F2 7 0.0 0.0 270.7 

200 2010 CH3F 7 0.0 0.0 138.3 

200 2010 CHF3 7 0.0 0.0 152.7 4619.1 

200 2010 NF3 7 16998.9 35117.6 64.1 49.6 2491.2 

200 2010 SF6 7 765.5 0.0 14.5 17900.3 

300 2009 C2F6 5 258.6 0.0 0.0 0.0 0.0 

300 2009 C2HF5 5 0.0 0.0 0.0 0.0 0.0 

300 2009 C3F8 5 0.0 0.0 0.0 0.0 45.0 

300 2009 C4F6 5 0.0 0.0 0.0 0.0 1446.6 

300 2009 C5F8 5 0.0 0.0 0.0 0.0 647.8 

300 2009 c-C4F8 5 0.0 0.0 0.0 0.0 4897.1 

300 2009 CF4 5 0.0 0.0 0.0 1206.3 20019.9 

300 2009 CH2F2 5 0.0 0.0 0.0 0.0 1082.6 

300 2009 CH3F 5 0.0 0.0 0.0 0.0 214.8 

300 2009 CHF3 5 0.0 0.0 0.0 0.0 4002.4 

300 2009 NF3 5 30178.3 75215.7 558.7 559.4 22273.7 

300 2009 SF6 5 0.0 0.0 0.0 479.0 4433.3 

300 2010 C2F6 5 0.0 0.0 0.0 0.0 0.0 

300 2010 C2HF5 5 0.0 0.0 0.0 0.0 0.0 

300 2010 C3F8 5 0.0 0.0 0.0 0.0 0.0 

300 2010 C4F6 5 0.0 0.0 0.0 0.0 2257.2 

300 2010 C5F8 5 0.0 0.0 0.0 0.0 971.0 

300 2010 c-C4F8 5 0.0 0.0 0.0 0.0 5731.3 

300 2010 CF4 5 0.0 0.0 0.0 1567.6 24521.2 

300 2010 CH2F2 5 0.0 0.0 0.0 0.0 1715.9 

300 2010 CH3F 5 0.0 0.0 0.0 0.0 266.5 
February 28, 2012 

36 

300 2010 CHF3 5 0.0 0.0 0.0 0.0 6165.1 

300 2010 NF3 5 44458.1 99018.7 780.5 471.4 21334.8 

300 2010 SF6 5 0.0 0.0 0.0 261.8 4017.7 

Table 4-6: F-Gas usage by principal film type 

Wafer 

Size Year Input Gas 

Number 

of FABS Silicon Oxide Nitride Metal 

200 2009 C2F6 7 3760.0 2178.3 533.0 

200 2009 C2HF5 7 

200 2009 C3F8 7 

200 2009 C4F6 7 2.1 1.2 

200 2009 C5F8 7 78.3 

200 2009 c-C4F8 7 90.1 4662.4 218.2 

200 2009 CF4 7 3641.3 8571.1 2205.7 1259.0 

200 2009 CH2F2 7 1.2 69.5 56.2 

200 2009 CH3F 7 68.5 37.7 

200 2009 CHF3 7 435.1 2100.8 854.9 146.0 

200 2009 NF3 7 811.6 2545.3 634.8 

200 2009 SF6 7 5599.6 2924.1 1770.4 2101.0 

200 2010 C2F6 7 2553.2 4140.7 500.8 0.0 

200 2010 C2HF5 7 

200 2010 C3F8 7 0.0 0.0 0.0 0.0 

200 2010 C4F6 7 3.0 1.6 

200 2010 C5F8 7 85.3 

200 2010 c-C4F8 7 622.6 5784.3 353.8 22.5 

200 2010 CF4 7 5689.2 8420.1 3985.9 1861.8 

200 2010 CH2F2 7 35.9 170.3 64.5 0.0 

200 2010 CH3F 7 96.8 41.5 

200 2010 CHF3 7 611.5 2528.2 1231.0 364.5 

200 2010 NF3 7 707.2 1479.4 304.6 0.0 

200 2010 SF6 7 8139.2 3918.5 3097.6 2803.0 

300 2009 C2F6 5 0.0 0.0 0.0 0.0 

300 2009 C2HF5 5 0.0 0.0 0.0 0.0 

300 2009 C3F8 5 0.0 45.0 0.0 0.0 

300 2009 C4F6 5 0.0 1224.1 222.5 0.0 

300 2009 C5F8 5 0.0 613.3 34.5 0.0 

300 2009 c-C4F8 5 24.6 3996.8 875.7 0.0 

300 2009 CF4 5 7398.4 7256.4 5083.5 281.6 

300 2009 CH2F2 5 392.0 396.8 293.8 0.0 

300 2009 CH3F 5 0.0 163.8 51.0 0.0 
February 28, 2012 

37 

300 2009 CHF3 5 1255.7 1774.0 853.8 118.9 

300 2009 NF3 5 3821.5 8969.2 8485.0 998.0 

300 2009 SF6 5 2254.9 943.2 1162.3 73.0 

300 2010 C2F6 5 0.0 0.0 0.0 0.0 

300 2010 C2HF5 5 0.0 0.0 0.0 0.0 

300 2010 C3F8 5 0.0 0.0 0.0 0.0 

300 2010 C4F6 5 0.0 1903.2 354.1 0.0 

300 2010 C5F8 5 0.0 851.3 119.7 0.0 

300 2010 c-C4F8 5 69.8 4684.9 976.6 0.0 

300 2010 CF4 5 9087.3 9185.6 6064.5 183.8 

300 2010 CH2F2 5 611.2 654.3 450.4 0.0 

300 2010 CH3F 5 0.0 207.9 58.6 0.0 

300 2010 CHF3 5 2299.2 2640.2 1076.3 149.4 

300 2010 NF3 5 3847.0 7823.9 7926.5 1737.4 

300 2010 SF6 5 2033.4 913.4 1011.3 59.6 
February 28, 2012 

38 

5. Results 

This section presents the results of SIA’s analysis of the several emission estimation methods. Section 

5.1 compares the methods for accuracy of the emissions estimate, by comparison to the Tier 3 emission 

values for three fabs, and then for precision of estimate, considering the structure and data of the 

models and the uncertainties of the underlying emission factors. This is an industry-level analysis done 

to determine the relative performance of the methods for predicting industry-level emissions. The 

comparisons show that the SIA 2-Factor and 3-Factor models perform substantially better than the 

earlier IPCC Tier 2B and EPA Tier 2C methods. 

Based on this finding, the two SIA models were evaluated in more depth to determine their 

performance when the effect of gas apportioning errors are considered in addition to the uncertainties

in emission factors. Section 5.2 presents the results of this assessment. The SIA 2-Factor model is 

shown to perform as well or better than the more-complex SIA 3-Factor model. To demonstrate the 2-

Factor model’s performance in a more realistic setting, the effect of abatement in 300mm fabs is also 

considered. 

The performance of the SIA 2-Factor model in predicting facility-level emissions is then assessed. 

Section 5.3 presents the results of this analysis, which is based on a sample of two 200mm and two 

300mm fabs that provided information on the extent of F-Gas abatement at their fabs. The updated 

default DREs proposed by SIA to EPA were used in the calculation of emission reductions due to 

abatement. 

5.1 Accuracy and Precision of IPCC, EPA, and SIA Estimation Methods 

5.1.1 Accuracy of F-Gas Emission Estimates 

Without direct measurements of emissions for individual fabs, the accuracy of the various emission 

estimation methods can be judged only in comparison to the estimation method that is considered to 

have the best available accuracy. Three of the twelve fabs that participated in the ISMI/SIA survey have 

prepared Tier 3 emission estimates and agreed to share their results for 2009 and 2010, including one 

200mm fab and two 300mm fabs. To assess accuracy, we use the Tier 3 results as the benchmark to 

which the other methods are compared. 

For these three fabs, we have developed emission estimates for the two years using four other 

methods: IPCC Tier 2B (a 1 factor model), EPA Tier 2C (a 2-factor model), the SIA 2-Factor Model, and 

the SIA 3-Factor Model. The emission estimates are based on the industry-average emission factors and 

the facility-specific gas usage in 2009 and 2010 for these three fabs. Facility-specific gas usage was 

estimated using best available monitoring methods. This typically involved applying heel factors to gas 

disbursements. When apportionment of gas usage to different process categories was required, 

apportioning was determined through use of engineering calculations and estimates. 

The emission estimates are accompanied by a pro-forma estimate of their expected errors (95% 

confidence interval) to provide a gauge for the significance, if any, of the differences that are observed 
February 28, 2012 

39 

among the methods. The error analysis is based on the expected errors in the industry-average 

emission factors, as determined through conventional error propagation methods and described in 

Section 3.4. The expected error gives the uncertainty in the emissions calculations due to uncertainties 

in the applicable emission factors. This is not the same as a later analysis (termed the facility-level error 

analysis) where we estimate the degree to which an estimation method applied at the facility level will 

give a result that varies from the true facility-specific value. No error analysis is included for the Tier 3 

estimates, which are used here as the standard for comparison. 

Table 5-1 summarizes the estimates by the various methods for Etch Emissions and Total F-Gas 

Emissions. Across both years and both wafer sizes, the SIA 2-Factor model generally comes closer to the 

Tier 3 values than the other three methods. Considering Etch Emissions, the IPCC Tier 2B, EPA Tier 2C, 

and SIA 3-Factor models give consistently lower estimates than Tier 3 for the one 200mm fab – 

approximately 23,000 to 24,000 mtCO2e per year for Etch (depending on the model) compared to 

30,000 mtCO2e for Tier 3. The SIA 2-Factor model is much closer to the Tier 3 result – giving 

approximately 28,000 mtCO2e in each of the years or 5-8% percent less than the Tier 3 result. For the

two 300mm fabs, the SIA 2-Factor and 3-Factor model estimates compare favorably to the Tier 3 result, 

while the estimates of the IPCC Tier 2B and the EPA Tier 2C methods are much lower. The SIA 3-Factor 

model is slightly closer to the Tier 3 result in each of the years, but the advantage over the 2-Factor 

model is very small. A similar result is seen when the SIA 2-Factor model is compared to the Tier 3 

values on the basis of Total F-Gas Emissions. The 2-Factor comes closer to the Tier 3 values for the 

200mm, being only 2-5% lower than the Tier 3 estimates. Both SIA models compare favorably to the 

Tier 3 values for the 300mm fabs, with the SIA 3-Factor model being slightly closer but with both being 

more conservative than the Tier 3 values. 

With the Tier 3 model as our benchmark, we conclude that the SIA 2-Factor model has better overall 

accuracy for estimating F-Gas emissions than the other methods, including the SIA 3-Factor model. The

advantage is clear and large compared to the IPCC Tier 2B and EPA Tier 2C models. The advantage is 

clear and large in comparison to the SIA 3-Factor model for the 200mm fab, while the SIA 3-Factor 

model has a very slight advantage for the two 300mm fab. However, as was discussed earlier, the 

application of the 3-Factor model creates technical implementation problems and, as shown later in this 

section, introduces additional sources of error. 

While a wider range of results might be seen if this comparison could be made for more fabs, we believe 

that the available data show the SIA 2-Factor model to have good accuracy for facility-level emission 

estimates. 

 
February 28, 2012 

40 

Table 5-1: Accuracy of Etch F-Gas Emission Estimates 

By Various Methods, Based on a Comparison to 

Tier 3 Emission Estimates For One 200mm and Two 300mm Fabs 

(With the Expected Error Via the Error Propagation Method, Not Considering Abatement) 

 Year Estimation Method 

Etch F-Gas Emissions 

Total F-Gas 

Emissions 

(Etch + Non-Etch) 

Emissions 

(mtCO2e) 

95% 

Confidence 

Interval 

Emissions 

(mtCO2e) 

200mm 2009 IPCC Tier 2B (1 factor) 22,700 76% 45,900 

 2009 EPA Tier 2C (2 factor) 23,000 77% 46,100 

 2009 SIA 2-Factor 28,100 24% 51,300 

 2009 SIA 3-Factor 22,700 39% 45,800 

 2009 Tier 3 29,500 — 52,700 

 2010 IPCC Tier 2B (1 factor) 23,300 77% 44,600 

 2010 EPA Tier 2C (2 factor) 24,100 75% 45,400 

 2010 SIA 2-Factor 28,000 24% 49,300 

 2010 SIA 3-Factor 22,400 39% 43,700 

 2010 Tier 3 30,600 — 51,800 

300mm 2009 IPCC Tier 2B (1 factor) 28,100 44% 54,600 

 2009 EPA Tier 2C (2 factor) 24,700 56% 51,300 

 2009 SIA 2-Factor 15,600 6% 42,100 

 2009 SIA 3-Factor 15,200 8% 41,700 

 2009 Tier 3 14,600 — 41,200 

 2010 IPCC Tier 2B (1 factor) 50,800 35% 86,400 

 2010 EPA Tier 2C (2 factor) 44,100 44% 79,700 

 2010 SIA 2-Factor 27,000 6% 62,600 

 2010 SIA 3-Factor 26,700 9% 62,300 

 2010 Tier 3 25,600 — 61,200 
February 28, 2012 

41 

5.1.2 Precision of F-Gas Emission Estimates by Method

Throughout the development of the SIA 2-Factor and 3-Factor models, SIA conducted error analysis 

based on error propagation techniques to obtain rapid feedback on the relative precision of the 

different methods. For this report, the worst-case error analysis conducted earlier and presented at the 

November 17, 2011 briefing has been converted to an expected error basis in order to estimate the 95% 

confidence intervals for the emission estimates. Only uncertainties in the emission factors underlying 

the estimation methods are considered in this following analysis. The error propagation methodology 

was described previously in Section 3.4. 

The error propagation method has been used to estimate the precision of industry-level emission 

estimates based on the four estimation methods considered here: IPCC Tier 2B, EPA Tier 2C, the SIA 2-

Factor, and the SIA 3-Factor models. These estimates aggregate gas usage data from the twelve fabs 

who participated in the SIA survey as representative of the 200mm and 300mm fabs industry-wide. The 

calculations treat the industry as if it were one large fab by applying the industry-average emission 

factors developed for the estimation methods to the total gas use for the seven 200mm fabs and the 

total gas use for the five 300mm fabs. In these calculations, the default emission factors established in 

the MRR for non-etch processes for semiconductors have been used with the SIA 2-Factor and 3-Factor 

models for etch to complete the calculation of Total F-Gas emissions. The results of this analysis are

summarized in Table 5-2. While many different comparisons can be made from these data, two are 

most important: 

1. The level of precision achieved by the SIA 2-Factor and 3-Factor models far outstrips the 

precision the IPCC Tier 2B and EPA Tier 2C models. The SIA methods achieve 95% confidence 

intervals that are smaller than ± 20% (200mm) and ± 10% (300mm). The earlier methods, based 

on much smaller databases, have 95% confidence intervals of ± 50% or worse. The increased 

precision comes about largely from the vastly increased size of the ISMI/SIA database, which 

permits emission factors to be estimated with better precision in almost all cells. 

2. The SIA 2-Factor model consistently estimates higher values for both Etch Emissions and Total FGas Emissions than the other models, including the SIA 3-Factor Model. In 200mm, the 

differences in Etch are large (14-18 percent) compared to all methods, while in 300mm, the 

differences are large only in comparison to the IPCC and EPA methods. Given the similar result 

in the Tier 3 comparisons, we believe that the higher estimate of the 2-Factor model is the 

better estimate. The two SIA models give similar emission estimates for etch in 300mm, with 

the SIA 3-Factor model being about 4% below the SIA 2-Factor model. Similar comparisons are 

seen for Total F-Gas Emissions, with the SIA 2-Factor model estimating substantially greater 

emissions in 200mm than the other methods and substantially lower emissions in 300mm than 

the IPCC and EPA methods. 

Given these results, and the preceding Tier 3 comparisons based on accuracy, we conclude that the SIA 

2-Factor and 3-Factor models clearly improve upon the earlier IPCC Tier 2B and EPA Tier 2C methods 

because they appear to be more accuracy and they clearly have better precision. The improvement is 

due to the large expansion of the emission factor database and, we believe, to the introduction of the

wafer size distinction, which captures the significant evolution of etch technology toward lower GHG 
February 28, 2012 

42 

emissions in 300mm fabs. There is no evidence in these results to delineate either the SIA 2-Factor or 3-

Factor model as having the better overall precision of estimate in percentage terms, although the Tier 3 

comparisons in the earlier previous section indicate that the 2-Factor has better overall accuracy. 

Table 5-2: Precision of Etch F-Gas Emission Estimates 

Based on Emission Factor Expected Errors: Industry-Level Analysis 

(Error Propagation Method, Not Considering Gas Apportioning Error or Abatement) 

 Year Estimation Method 

Etch F-Gas Emissions 

Total F-Gas 

Emissions 

(Etch + NonEtch) 

Etch 

Emissions 

(mtCO2e) 

Error 

Standard 

Deviation 

(mtCO2e) 

95% 

Confidence 

Interval 

(Percent) 

Total 

Emissions 

(mtCO2e) 

200mm 2009 IPCC Tier 2B (1 factor) 339,300 41,800 ± 41% 1,242,900 

 2009 EPA Tier 2C (2 factor) 342,400 40,700 ± 41% 1,246,000 

 2009 SIA 2-Factor 402,200 14,200 ± 18% 1,305,800 

 2009 SIA 3-Factor 346,700 21,400 ± 16% 1,250,300 

 2010 IPCC Tier 2B (1 factor) 435,100 55,900 ± 51% 1,236,700 

 2010 EPA Tier 2C (2 factor) 438,700 54,700 ± 51% 1,240,400 

 2010 SIA 2-Factor 530,300 20,100 ± 19% 1,331,900 

 2010 SIA 3-Factor 456,500 29,000 ± 17% 1,258,100 

300mm 2009 IPCC Tier 2B (1 factor) 392,800 63,600 ± 58% 570,400 

 2009 EPA Tier 2C (2 factor) 374,200 63,200 ± 62% 551,800 

 2009 SIA 2-Factor 264,200 10,600 ± 8% 441,800 

 2009 SIA 3-Factor 251,000 8,900 ± 7% 428,600 

 2010 IPCC Tier 2B (1 factor) 464,600 68,200 ± 52% 713,000 

 2010 EPA Tier 2C (2 factor) 438,000 67,800 ± 56% 686,300 

 2010 SIA 2-Factor 297,900 10,900 ± 7% 546,300 

 2010 SIA 3-Factor 285,200 9,700 ± 7% 533,600 
February 28, 2012 

43 

Based on these findings, SIA narrowed the estimation methods to include only the SIA 2-Factor and 3-

Factor models. Further analysis was conducted using Monte Carlo methods to provide a broader and 

more detailed assessment of precision, including contributions from both emission factor errors and gas 

apportioning errors. 

5.2 Comparison of SIA 2-Factor and 3-Factor Models: Industry-level Analysis 

To improve on the understanding of the SIA models, a more detailed and extensive analysis was 

conducted using the Monte Carlo method, considering both emission factor and gas apportioning errors. 

This analysis begins with the re-consideration of effect of emission factor errors using the Monte Carlo 

method and then moves to consider the effect of gas apportioning errors and both error sources. 

5.2.1 Effect of Emission Factor Errors on SIA 2-Factor and 3-Factor Models 

Table 5-3 summarizes the results of the Monte Carlo analysis for the effect of emission factor errors on 

F-Gas emission estimates, when the SIA 2- and 3-factor models for etch are combined with the EPA 

default emission factors for non-etch processes. In this, gas apportioning errors are not considered, and 

the EPA default EFs for non-etch processes are treated as having no errors (the uncertainties are 

unknown to us). The errors shown for Total F-Gas Emissions (Etch + Non-etch) consider only the errors

in the etch EFs. 

Looking first at the SIA 2-Factor model, the effect of EF errors on Etch Emissions is similar between 

200mm and 300mm fabs on percentage basis, with a somewhat wider range for 200mm, although the 

absolute errors (mtCO2e basis) in 300mm fabs are only one-half to one-third as large due to their lower 

overall level of etch emissions. These estimates do not account for the effect of F-Gas abatement in 

300mm fabs, which will further reduce Etch and Total F-Gas emissions (and the emission errors) of the 

SIA models. The greater percentage range for 200mm has been traced to relatively large uncertainties 

in byproduct EFs for SF6 and CF4 (due to small data samples), which are important gases in 200mm. 

For Total F-Gas Emissions, the 95% confidence intervals of the SIA 2-Factor model are relatively small – 

ranging from ± 3 to 8 percent – and very consistent across the four cases. Etch emissions are a smaller 

part of total emissions for 200mm fabs (30-40%) than for 300mm fabs (55-60%) due to the replacement 

of C2F6 with NF3 as the primary chamber clean gas in 300 mm technologies. Therefore, the somewhat 

larger percentage errors for etch in 200mm are attenuated more when non-etch emissions are added in, 

leading to the result that all of the cases for 200mm and 300mm end up in the same error range. 

Comparing the SIA 2-Factor and 3-Factor models, we find that the emission estimates and the effect of 

EF errors are very similar. The 2-Factor model estimates somewhat higher emissions in all cases, but the 

differences in its estimates for etch lead to only a 4% difference in 200mm fabs and 2-3% difference in 

300mm fabs on a Total Emissions basis. This comparison does not say which model is more accurate on 

an industry level, but it shows clearly that any difference in accuracy between the models is very small 

and that the 2-Factor is a more conservative approach because it estimates somewhat higher emissions. 

Considering the precision of emissions estimates, the 2-Factor model has slightly better precision in 

200mm for both Etch and Total Emissions, while the two models have equivalent precision in 300mm. 
February 28, 2012 

44 

The fact that the precision of the 2- and 3- Factor models are nearly the same supports our conclusion, 

drawn during the emission factor analysis, that differences in emission factors are largely driven by the 

properties of the gas and are relatively independent of other parameters, such as film type and power.

Table 5-3: Comparison of SIA 2-Factor and 3-Factor Models: Industry Level Analysis 

Accounting for Emission Factor Errors Only 

(Monte Carlo Method, Not Considering Abatement) 

 Etch F-Gas Emissions 

Total F-Gas Emissions 

(Etch + Non-Etch) 

Wafer 

Size 

Year 

Etch 

Emissions 

(mtCO2e) 

Error 

Standard 

Deviation 

(mtCO2e) 

95% 

Confidence 

Interval 

(Percent) 

Total 

Emissions 

(mtCO2e) 

95% Confidence

Interval 

(Percent) 

SIA 2-Factor Model 

200mm 2009 402,200 24,500 -9% to +17% 1,305,800 -3% to +5% 

 2010 530,300 36,100 -10% to +19% 1,331,900 -4% to +8% 

300mm 2009 264,200 10,800 -8% to +6% 441,800 -5% to +5% 

 2010 297,900 11,300 -7% to +8% 546,300 -4% to +4% 

SIA 3-Factor Model 

200mm 2009 346,700 25,000 -15% to +15% 1,250,300 -4% to +4% 

 2010 479,800 35,500 -15% to +16% 1,281,400 -6% to +6% 

300mm 2009 251,000 10,300 -8% to +8% 428,600 -5% to +5% 

 2010 285,200 11,100 -7% to +8% 533,600 -4% to +4% 

The similar level of precision that is seen here runs counter to our initial assessment that the subdivision 

of the SIA EF database into film types would significantly degrade the precision of the 3-Factor model

estimates. A small degradation can be seen in 200mm, but none is apparent in 300mm when comparing 

the 2-Factor and 3-Factor models. We conclude that this similarity comes about because the chief 

differences in the 3-Factor model have offsetting effects on precision: 

1. The 3-Factor model was shown to have modestly greater explanatory power (ΔR2 ~ 3%). 

Although there are only 4 cells where a statistically significant difference exists due to principle 

film type, the use of independent EFs by film type will reduce the overall error in those cells and 
February 28, 2012 

45 

improve the precision of the 3-Factor model. While the R2 might improve by only ~3% in the EF 

analysis, the improvement in GWP-weighted emissions may be somewhat greater because the 

statistically-significant EF differences tend to occur for gases that are important to emissions. 

2. Offsetting this, the sample size N is diluted in all cells by dividing the database into film types, 

and the reduction in sample sizes necessarily degrades the EF precision in each of the film type 

cells. However, we find that the cells that matter most to emissions are generally filled with 

large sample sizes, which works to mute the effect of splitting the database into smaller pieces. 

As an example, the 52 EF data points for NF3 in 300mm are split in the 3-Factor model into 21 

data points for Nitride, 18 data points for Oxide, and 11 data points for silicon, with 4 data 

points for the low-gas use Metal film type (4% of total NF3). Although the precision of each film 

EF is less than the precision of the overall NF3 EF, the degradation is relatively small in going 

from 52 data points in the 2-Factor model down to 11-21 data points for the major gas-use film 

types in the 3-Factor model. It would be a different outcome if there were 12-20 data points in 

the cell split into 4 groups of 3-5 each. 

Both considerations are at work in the 3-Factor model and have been accounted for in this analysis. We 

find that they approximately offset each other, leading to the generally similar level of precision. 

Considering both the accuracy and precision of the EFs, there is no reason to prefer the more complex 3-

Factor model over the 2-Factor model. The 2-Factor model precision is essentially the same (300 mm) 

as, or better (200mm) than, the 3-Factor model. 

5.2.2 Effect of Gas Apportioning Error on SIA 2-Factor and 3-Factor Models 

In the 2-Factor model, gas use must be allocated among the five EPA processes (etch and four wafer and

chamber cleaning processes), while much more apportioning work must be done in the 3-Factor model 

to the film-type level in etch. The gas apportioning problem is complex because the emissions effect of 

gas apportioning errors depends upon the differences among the EFs by process and the difficulty of the 

gas allocation problem by gas. If the EFs do not differ appreciably by process or film-type, then errors in 

allocating gas use will have little consequence for emissions. No allocation error will be present for 

gases used only in one process and little allocation error will result if only small portions of gas use are 

allocated to processes other than the dominant one. Finally, the overall impact of apportioning errors 

will depend on each gas’s GWP value and its contribution to Total Emissions on an mtCO2e basis. 

Table 5-4 summarizes the effect of gas apportioning errors on F-Gas emission estimates, when the SIA 2- 

and 3-factor models for etch are combined with the EPA default emission factors for non-etch 

processes. In this, the errors in the emission factors themselves are not counted, so that the results 

reflect only the effect of gas apportioning errors. The results reported for the errors in Etch Emissions 

are magnified because they reflect only the apportioning errors that occur in Etch and not the correlated 

and partially offsetting errors in Non-Etch emissions. The reported results for Total F-Gas Emissions

properly characterize the net effect of gas apportioning errors after accounting for the net effect of the 

correlated errors. 
February 28, 2012 

46 

Table 5-4: Comparison of SIA 2-Factor and 3-Factor Models: Industry Level Analysis 

Accounting for Gas Apportioning Errors 

(Monte Carlo Method, Not Considering Abatement in 300mm Fabs) 

 Etch F-Gas Emissions 

Total F-Gas Emissions 

(Etch + Non-Etch) 

Wafer 

Size 

Year 

Etch 

Emissions 

(mtCO2e) 

Error 

Standard 

Deviation 

(mtCO2e) 

95% 

Confidence 

Interval 

(Percent) 

Total 

Emissions 

(mtCO2e) 

95% Confidence

Interval 

(Percent) 

SIA 2-Factor Model 

200mm 2009 402,200 52,700 -22% to +29% 1,305,800 -5% to +6% 

 2010 530,300 55,100 -18% to +24% 1,331,900 -3% to +4% 

300mm 2009 264,200 45,700 -26% to +41% 441,800 -25% to +27% 

 2010 297,900 47,400 -22% to +38% 546,300 25% to +29% 

SIA 3-Factor Model 

200mm 2009 346,700 48,900 -25% to +29% 1,250,300 -6% to +6% 

 2010 479,800 56,100 -21% to +26% 1,281,400 -5% to +5% 

300mm 2009 251,000 41,400 -26% to +35% 428,600 -24% to +24% 

 2010 285,200 43,600 -23% to +33% 533,600 -25% to +25% 

Looking first at the SIA 2-Factor model, we see that gas apportioning error has a much larger effect in 

300mm fabs than in 200mm fabs. Many factors contribute to this result, including differences among 

the EFs by process, but it is fundamentally related to the nature of the gas allocation problems in 

200mm and 300mm. Table 5-5 compiles information related to apportioning to show the following: 

• In 200mm, six different gases must be allocated, and emissions generation is distributed among 

a number of gases. The NF3 allocation problem is still the most difficult, but NF3 is only one-half 

as important to GWP emissions as in 300mm. While not all of the apportioning errors shown in 

the table are independent (due to byproduct emissions), there are more gases over which 

independent errors can be combined and attenuated to reach the total. 

• In 300mm, emissions generation and the gas allocation problem is almost entirely related to 

NF3, which is 87% of GWP-weighted gas use and the most difficult gas to allocate because it is 

spread across all five processes. Only the small independent errors for CF4 and SF6 can help to 

offset the error in NF3 (and these would disappear when wafer cleaning is combined with etch). 
February 28, 2012 

47 

These fundamental differences also affect the 3-Factor model, which differs only in regard to the greater 

effort required to apportion gas use to the film level. Our chief conclusions on the emissions effect of 

gas apportioning are the following: 

• The effect of apportioning error is small in 200mm. Six gases require allocation among the five 

EPA processes, but the ability to average the errors across the different gases brings the overall 

effect down to no more than -5% to +6% (95% confidence range). 

• The effect is larger in 300mm, where almost the entire allocation problem (and the largest part 

of emissions generation) is concentrated in NF3. There is much less allocation work to be done, 

but where it is necessary (NF3) the allocation problem is difficult and the result very important 

to emissions. 

These results are related to the general patterns of gas use and emissions in 200mm and 300mm. The 

specific input values used to represent the errors in modeling gas use range from 20% to 46%, 

depending on gas, with NF3 in the middle at 31%. Different choices for these inputs would lead to 

numerically different results for the errors, but we believe that the basic conclusions should be similar. 

Looking next at the SIA 3-Factor model, we see very little difference in the effect of gas apportioning in 

comparison to the SIA 2-Factor model. Although there is much more work to be done in apportioning 

gas to the film type level for the 3-Factor model, two aspects of the film-level EFs combine to minimize 

the incremental effect of apportioning error on etch. 

One aspect is related to the variability of the EFs by film type. As shown in Table 5-6: 

• For 200mm fabs, the EF standard deviations are average or below average for the two gases 

(CF4 and SF6) that have the greatest impact on emissions. 

• For 300mm fabs, the EF standard deviations are well below average for the two gases (CF4 and 

NF3) that have the greatest impact on emissions. 

Where it matters most to emissions, the film-level EFs have smaller standard deviations, which mute the 

incremental impact of apportioning error on Etch Emissions and are the primary reason that gas 

apportioning has minimal incremental impact on Etch Emissions in 300mm. This result is consistent with 

our view that etch EFs vary primarily by input gas and not by film type. 

 
February 28, 2012 

48 

Table 5-5. Decomposition of Gas Allocation Error in the SIA 2-Factor Model 

Based on Average Gas Use 2009-2010 

(Not all emission errors shown are independent errors due to byproduct emissions) 

200mm Fabs 

GWP-Wtd 

Gas Use 

Pct Dist 

Gas Use 

Allocated 

To N Processes 

Concentration 

Of Gas Use 

In Processes 

Gas Apportioning Error 

Input 

Value 

Total F-Gas Emissions 

(Etch+Non-Etch) 

± 1 Std Dev ± mtCO2e 

C2F6 31% 3 89% In-Situ Clean 0.42 3% 17,390 

C2HF5 0% 

C3F8 5% 

C4F6 0% 

C5F8 0% 

c-C4F8 2% 2 70% Etch 0.20 2% 185 

CF4 7% 4 57% Etch 0.46 4% 11,949 

CH2F2 0% 

CH3F 0% 

CHF3 2% 2 97% Etch 0.40 31% 14,340 

NF3 40% 5 62% Remote Clean 0.31 28% 27,642 

SF6 13% 3 96% Etch 0.31 < 1% 536 

Total 100% 2.8% 37,554 

300mm Fabs 

GWP-Wtd 

Gas Use 

Pct Dist 

Gas Use 

Allocated 

to N Processes 

Concentration 

Of Gas Use 

In Processes 

Gas Apportioning Error 

Input 

Error 

Total F-Gas Emissions 

(Etch+Non-Etch) 

± 1 Std Dev ± mtCO2e 

C2F6 0% 0.42 34% 14,387 

C2HF5 0% 0.41 - 

C3F8 0% 0.41 - 

C4F6 0% 0.27 - 

C5F8 0% 0.69 - 

c-C4F8 2% 0.20 1% 74 

CF4 5% 2 94% Etch 0.46 5% 6,905 

CH2F2 0% 0.59 - 

CH3F 0% 0.71 - 

CHF3 2% 0.40 21% 7,971 

NF3 87% 5 59% RemoteClean 0.31 27% 67,103 

SF6 4% 2 92% Etch 0.31 < 1% 111 

Total 100% 15% 72,234 
February 28, 2012 

49 

Table 5-6. Impact of Film Type Emission Factors on

the Emission Consequences of Gas Apportioning Errors 

in the SIA 3-Factor Model 

200mm 

GWP-Wtd Gas 

Use 

(Average 2009-

2010) 

Primary Emission Factors 

Emissions 

Factor 

Std Dev Metal Nitride Oxide Silicon 

C2F6 13% 0.56 0.77 0.50 0.22 

C2HF5 0% n/a 

C3F8 0% n/a 

C4F6 0% 0.50 0.17 0.59 

C5F8 0% 0.50 0.50 0.50 0.00 

c-C4F8 11% 0.50 0.50 0.14 0.50 0.43 

CF4 33% 0.50 0.64 0.85 0.82 0.15 

CH2F2 0% 0.50 0.34 0.12 0.50 0.45 

CH3F 0% 0.50 0.66 0.50 0.50 0.17 

CHF3 8% 0.52 0.60 0.50 0.62 0.06 

NF3 6% 0.50 0.20 0.23 0.45 

SF6 28% 0.84 0.59 0.21 0.46 0.26 

Overall 0.26 

300mm 

GWP-Wtd Gas 

Use 

(Average 

2009-2010) 

Primary Emission Factors 

Emission 

Factor 

Std Dev Metal Nitride Oxide Silicon 

C2F6 0% n/a 

C2HF5 0% n/a 

C3F8 0% 0.50 0.00 

C4F6 3% 0.50 0.18 0.58 

C5F8 1% 0.13 0.50 0.61 

c-C4F8 10% 0.28 0.17 0.50 0.45 

CF4 40% 0.47 0.55 0.71 0.63 0.11 

CH2F2 3% 0.12 0.19 0.50 0.49 

CH3F 0% 0.50 0.50 0.00 

CHF3 9% 0.16 0.30 0.41 0.36 0.11 

NF3 39% 0.30 0.14 0.13 0.26 0.09 

SF6 8% 0.50 0.27 0.18 0.24 0.39 

Overall 0.25 

Note: Gray shading means that no gas is used in that cell. 
February 28, 2012 

50 

A second aspect is related to the absence of emission factors for some cells, which are assigned the 

default value 0.50 in the analysis and flagged in the table using red font. The gases with the largest 

impact on GWP-weighted emissions are well represented in the database, with only one film-type cell 

containing no data. Among the other gases, with lesser impacts on GWP-weighted emissions, there is 

one cell in 200mm where the EF standard deviation is zero because all EFs are given the value 0.50, and 

there are two such cells in 300mm. Beyond this in all of the cells where an EF is set to 0.50, the 

calculations performed in the apportioning model negate a part of the apportioning error because the 

EFs are the same. 

For these reasons, there is no appreciable difference between the SIA 2-Factor and 3-Factor models in 

the emissions consequences of gas apportioning errors, whether in terms of Etch Emissions or Total FGas Emissions. However, as SIA has documented previously, a much greater effort must be made to 

apportion input gases to the film type level to apply the 3-Factor model, and the 3-Factor model raises 

major CBI concerns as well. As will be seen throughout this section, there is no reporting benefit of the 

3-Factor model – whether in terms of accuracy or precision of estimate – that supports its adoption in

place of the 2-Factor model, and certainly no benefit that could outweigh its substantial cost and CBI

burden. 

5.2.3 Combined Effect of Emissions Factor Error and Gas Apportioning Error on the SIA 2-Factor and 3-

Factor Models 

The performance of the models for reporting purposes will be determined by the combined effect of 

emission factor errors and gas apportioning errors on emissions. These error sources will be additive, as 

independent errors. This section presents the combined effect of both errors sources on the SIA 2- and 

3-Factor models. 

Table 5-7 summarizes the effect of combining gas apportioning and emission factor errors on F-Gas 

emissions estimates, using the SIA 2-Factor and 3-Factor models for etch. When apportioning and 

emission factor errors are combined, we see that the two models perform nearly the same for 

estimating Etch Emissions, with the SIA 2-Factor model having a slight advantage over the 3-Factor 

model in each case. The precision for Etch Emissions estimates is ± 30% or smaller (95% confidence 

interval) in each case, while the absolute errors are smaller in 300mm due to the overall lower F-Gas 

emissions for these fabs. As will be seen later, the use of abatement in 300mm further reduces this 

error. 

Similar conclusions can be drawn for Total F-Gas Emissions, where the SIA 2-Factor model has a slight 

advantage over the 3-Factor model in 200mm and a slight disadvantage in 300mm. For the 2-Factor 

model, the precision of Total F-Gas Emissions is ± 5-7% for 200mm and ± 25-30% for 300mm. For the 3-

Factor model, the precision of estimate is ± 7-8% for 200mm and ± 24-26% for 300mm. It is the 

difference in gas apportioning errors that causes the precision to be better in 200mm, compared to 

300mm, since there is little difference in the effect of emission factor errors. Although the relative 

(percentage) errors differ, the absolute (mtCO2e) errors are lower in 300mm because of the overall

lower level of F-Gas emissions from these fabs. 
February 28, 2012 

51 

Table 5-7: Comparison of SIA 2-Factor and 3-Factor Models: Industry Level Analysis 

 Accounting for Gas Apportioning and Emission Factor Errors 

(Monte Carlo Method, Not Considering Abatement) 

 Etch F-Gas Emissions Total F-Gas Emissions 

(Etch + Non-Etch) 

Wafer 

Size Year 

Etch 

Emissions 

(mtCO2e) 

Error 

Standard 

Deviation 

(mtCO2e) 

95% Confidence 

Interval 

(Percent) 

Total 

Emissions 

(mtCO2e) 

95% Confidence 

Interval 

(Percent) 

SIA 2-Factor Model 

200mm 2009 402,200 56,700 -25% to +30% 1,305,800 -6% to +7% 

 2010 530,300 64,200 -21% to +27% 1,331,900 -5% to +7% 

300mm 2009 264,200 48,100 -26% to +45% 441,800 -25% to +29% 

 2010 297,900 48,900 -23% to +40% 546,300 -26% to +30% 

SIA 3-Factor Model 

200mm 2009 346,700 56,200 -28% to +35% 1,250,300 -8% to +8% 

 2010 479,800 65,200 -25% to +28% 1,281,400 -7% to +7% 

300mm 2009 251,000 43,400 -27% to +38% 428,600 -24% to +26% 

 2010 285,200 46,500 -25% to +40% 533,600 -25% to +26% 

The near equivalence of model performance is not a surprise, because we have previously seen that the 

2-Factor model has only a small advantage over the 3-Factor model based on emission factor errors, and

there is no material difference in the effect of gas apportioning error between the two models. When 

both errors are considered together, the 2-Factor model has a small advantage over the 3-Factor model,

and there is no reason to prefer the more complex 3-Factor model based on the precision of emissions 

estimates. 

5.2.4 Precision of the SIA 2-Factor Model Considering Abatement in 300mm Fabs 

The estimates of model performance presented above did not consider the effect of F-Gas abatement 

on emission levels and errors so that the comparisons would focus on the underlying error sources. 

However, the performance achieved in practice will be improved by the reduction in emissions due to 

abatement, the majority of which occurs in 300mm fabs. To show this effect, the five 300mm fabs 

participating in the gas usage survey provided the information needed to estimate the reduction in 
February 28, 2012 

52 

emissions due to abatement. Because abatement is uncommon in 200mm fabs, we do not account for it 

in the analysis presented below. We look only at the SIA-2 Factor model because abatement will not 

change the relative performance of the 2- and 3-Factor models. 

The information provided by the 300mm fabs has been compiled to the industry level to estimate the 

fraction of F-Gas usage that is abated in each of the five processes. For the fraction of gas that is abated, 

we have applied the default DREs that were proposed by SIA the paper on DRE abatement recently 

delivered to EPA and documented in Section 3.7. The default DREs are: 60% for CF4 used in etch; 99% 

for NF3 and other gases used in etch; 90% for NF3 used in any of the chamber cleaning processes; and 

60% for CF4 and any other gas used in chamber cleaning. 

Table 5-8: Precision of Etch F-Gas and Total F-Gas Emission Estimates at the Industry Level 

Using SIA 2-Factor Model for Etch 

Accounting for Gas Apportioning and Etch Emission Factor Errors 

With Abatement in 300mm Fabs 

 Etch F-Gas Emissions Total F-Gas Emissions 

(Etch + Non-Etch) 

Wafer 

Size 

Year 

Etch 

Emissions 

(mtCO2e) 

Error 

Standard 

Deviation 

(mtCO2e) 

95% Confidence 

Interval 

(Percent) 

Total 

Emissions 

(mtCO2e) 

95% Confidence 

Interval 

(Percent) 

200mm 2009 402,194 57,500 -25% to +30% 1,305,764 -6% to + 7% 

 2010 530,270 63,600 -21% to +27% 1,331,926 -5% to + 7% 

300mm 2009 188,526 24,100 -19% to +31% 276,565 -16% to +19% 

 2010 222,935 28,300 -19% to +31% 334,606 -16% to +19% 

Table 5-8 summarizes the expected performance of the SIA 2-Factor Model when F-Gas abatement in 

300mm fabs is considered. All of the 300mm fabs abate NF3, three of the fabs also abate CF4 and SF6, 

and one of the fabs abates all gases. Abatement reduces Etch Emissions by 25-29% and Total F-Gas 

emissions by 37-39% in 300mm fabs. Because NF3 is the most commonly abated gas and its emissions 

are subject to the greatest error, abatement also improves the precision of emission estimates in 

300mm. Absolute (mtCO2e) emission errors fall to less than one-half those in 200mm, while the relative 

precision of Etch Emission estimates is improved to match the level in 200mm. For Total F-Gas 

Emissions, the precision of estimate is improved to the level of -16% to +19% (95% confidence interval) 

compared to -25% to +30% without abatement. 
February 28, 2012 

53 

These industry-level results indicate clearly that the SIA 2-Factor model will achieve a precision in 

emission estimates that far exceeds that of the earlier IPCC and EPA methods. This is true for both Etch 

Emissions and Total F-Gas Emissions when the SIA 2-Factor model is combined with EPA default 

emission factors for the other processes 

5.3 Precision of the SIA 2-Factor Model for Facility-level Emissions 

The effects of gas apportioning errors and emission factor errors change when the 2-Factor model is 

applied to make emission estimates at the facility level. The distribution of gas usage will vary from fab 

to fab, leading to differences in the sources of emissions and in the importance of apportioning errors 

for each gas. More importantly, however, the precision of the emission factors underlying facility 

estimates will differ from that achieved at the industry level. In general, there will be more variation in 

the true average emission factor from fab to fab than there will be in the industry average, leading to a 

lower level of precision that can be achieved. However, as shown in this section, the performance of the 

SIA 2-Factor model is very good at the facility level and, in many cases, will approach the level of 

performance achieved at the industry level. As explained in Section 3.7, the precision of a facilityspecific average emission factor depends on the number of distinct tool types and etch processes that 

are used in the facility during a reporting period (denoted as NF or Facility N). 

To assess the precision achieved by the SIA 2-Factor model at the facility level, two 200mm and two 

300mm fabs agreed to count the number of distinct tool types / etch process combinations using the 

same distinctions for tool type and etch process made in the SIA database (documented in Section 3.6).

Although each fab has fewer combinations than in the database, the subsets given an appreciable 

coverage of the database (up to one-quarter in some cases) and are large enough to give good precision

in the facility-level emission estimates for the major gases. 

Table 5-9 summarizes the expected performance of the SIA 2-Factor Model for the four sample fabs in 

comparison to the industry-level performance that was demonstrated in the previous section. For the 

two 300mm fabs, results are given both without and with F-Gas abatement. In 200mm, we see that 

facility-level precision for Etch Emissions is close to, but not as good as, the industry-level precision. For 

Total F-Gas Emissions, the facility-level precision can be somewhat better than the industry-level 

precision (Fab B) or somewhat less (Fab C). In 300mm, we again see that facility level precision can be 

better than the industry-level precision (Fab A) or somewhat less (Fab E) for the same reasons as in 

200mm; these comparisons remain the same when F-Gas abatement is considered. These differences 

are driven both by the count of tool type / process combinations and the mix of gases used by the fabs. 

While going from the number of tool type / process combinations in the database to the number that 

are used in a single fab must degrade precision, a shift of gas use to gases with lower emission factor or 

gas apportioning errors can improve the comparison relative to the industry average. 

• Note that three of the sample fabs show an improved precision in the comparison, because of 

their mix of gases or because they primarily abate NF3, which has generally the largest emission 

errors. While the fully-abated Fab E shows larger percent errors (on a reduced emissions base), 

its across-the-board 
February 28, 2012 

54 

Table 5-9. Facility N Analysis:

Combined Effect of Apportioning Error and Emission Factor Errors 

On Facility Level Etch F-Gas and Total F-Gas Emission Estimates 

Using SIA 2-Factor Model for Etch 

 Etch F-Gas Emissions Total F-Gas Emissions

Etch + Non-Etch 

Wafer Size Level Year ± 95% Confidence 

Interval mtCO2e ± 95% ConfidenceInterval (Range) 

Unabated 

200mm Industry 2009 -25% to +30% -6% to + 7% 

Fab B “ -28% to +48% 187,400 -4% to +4% 

Fab C “ -28% to +37% 149,100 -7% to + 9% 

 Industry 2010 -21% to +27% -5% to + 7% 

Fab B “ -26% to +39% 259,100 -3% to +4% 

Fab C “ -26% to +29% 174,400 -10% to +12% 

300mm Industry 2009 -26% to +45% -25% to +29% 

Fab A “ -16% to +18% 47,700 -20% to +29% 

Fab E “ -43% to +56% 145,500 -30% to +37% 

 Industry 2010 -23% to +40% -26% to +30% 

Fab A “ -18% to +17% 61,400 -22% to +33% 

Fab E “ -37% to +56% 156,900 -27% to +32% 

With Abatement in 300mm Fabs 

300mm Industry 2009 -19% to +31% -16% to +19% 

Fab A “ -17% to +16% 32,200 -13% to +15% 

Fab E “ -40% to +53% 42,700 -33% to +45% 

 Industry 2010 -19% to +31% -16% to +19% 

Fab A “ -17% to +18% 38,100 -13% to +16% 

Fab E “ -33% to +46% 46,700 -27% to +39% 

abatement reduces total F-Gas emissions by 71% and the absolute error (mtCO2e) in its emissions 

estimates by about the same percentage. 

We find it remarkable how little difference there is in the precision achieved by the SIA 2-Factor model 

at the industry and the facility levels. This is partly because there is a tendency to have more tool type / 

process combinations for the gases used in larger quantities (and are most important to emissions), but 

we believe that the role played by a few cells in the database with very few data points (often zero or 

one) is also important. The precision of the 2-Factor model is determined by the combination of: 
February 28, 2012 

55 

• A group of cells that have large N in the database. These cells generally have larger gas use and 

will have high precision in an industry-level analysis. While their precision is degraded in moving 

to a facility-level analysis, the errors are still relatively small (if larger than before). 

A group of cells that have very small N in the database. These cells have poor precision in an industrylevel analysis, and their precision level will not be changed at all in the facility-level analysis, if NF is 

greater than NDB. They will have large influence on the overall precision at both industry and facility 

levels. 

With current data, the 2-Factor model performs very well at the facility level, with a precision level that 

is similar to that achieved at the industry level, being somewhat better in some cases and somewhat less 

in other cases. For 200mm Fabs B and C, the precision of facility-level estimates is approximately ± 10% 

or less (95% confidence). When abatement is accounted for, the precision for 300mm Fab A is 

approximately ± 15% (95% confidence), and only the fully-abated 300mm Fab E has a precision less than 

± 30% (95% confidence) . Improved precision in the emission factors should be achieved when the 

additional etch tool testing (described below) is completed in midyear 2012 and the data points added 

to the database. We see these results as demonstrating that the SIA 2-Factor will be a very reliable basis 

for emissions reporting under the MRR rule. 

6. Anticipated Expansion of Etch Emissions Factor Database 

The current ISMI etch emissions characterization database contains data from all four of the major 

equipment suppliers’ toolsets. Using the current ISMI database, 2-factor model emission factors can be 

established for 99.7% of industry F-Gas usage. To further strengthen the representativeness of this 

database, a semiconductor manufacturer and key equipment suppliers have committed to conduct 

additional etch emissions testing. The additional data will covers 8 supplier/gas use combinations found 

in 200mm fabs and 7 supplier/gas use combinations found in 300mm for which the ISMI database does 

not currently contain data. This will improve the supplier gas/use coverage of the database and reduce

the emissions factor uncertainty in several wafer size/gas use cells. Testing will be completed in the 

first half of 2012 and these data will be shared with EPA. 

### 

 
February 28, 2012 

56 

Attachments 

A. Changes Since the Presentation of Results in November 2011 

Several corrections were made to the calculations after the 17 November 2011 presentation. The 

following are the details on the changes: 

• Correction of the gas usage for the 300mm industry analysis using 2010 in the 2-Factor model 

analysis 

• Originally, C3F8 was not carried through the calculations, both for etch emissions and other 

emissions. It is now included. 

• Lower and upper emission factors were calculated using 1.92 * standard error. This is corrected 

to 1.96 * standard error. 

• The 2010 300 mm gas usage data input source for the 2 factor model had a formatting error, 

causing incorrect gas usage input values. The original data had missing gas usage. This has been 

corrected. 

• There were two typos in the use of the byproduct emission factors for Wafer cleaning and CVD. 

These have been corrected. The total F-Gas use went up with these corrections. This means 

that the percentage that is etch and etch byproducts went down. 

There were also several changes in methodology that are described in detail in the body of the report 

including: the use of expected error instead of worst case error, the extension of the gas propagation 

error analysis to the 2-Factor model and its impact on the estimation of emissions for non-Etch sources, 

a different approach to assumptions for data cells that are empty, and the quantification of the facility 

level N for determining the precision of the 2-Factor model as it will apply to a single facility. 

 
February 28, 2012 

57 

B. Current Etch Emissions factors for the 2-Factor and 3-Factor Models 

The tables below list the current input gas etch emission factors for the 2-Factor and the 3-Factor 

models. Note that for a particular wafer diameter and gas combination, the data sets for the 2 and 3 

factor models may not match. This is because some data points in the database for non-etch steps are 

not unique to a film and thus are not categorized to a principal film type. These points are not used in 

the 3 factor models.

Table B-1. 2-Factor Model – input gas 

Input 

Gas 

Name 

Wafer 

Diameter

Data 

Sets 

Emission 

Factor Std Dev Std Err 

C2F6 200 40 0.76175 0.167839 0.026538

C2HF5 200 31 0.064387 0.029157 0.005237

C3F8 200 

C4F6 200 14 0.16645 0.220904 0.059039

C5F8 200 

c-C4F8 200 44 0.139814 0.09066 0.013668

CF4 200 126 0.805868 0.27163 0.024199

CH2F2 200 43 0.126565 0.104605 0.015952

CH3F 200 1 0.6587 

CHF3 200 131 0.502405 0.258593 0.022593

NF3 200 22 0.200268 0.132986 0.028353

SF6 200 129 0.552081 0.275439 0.024251

C2F6 300 1 0.811 

C2HF5 300 

C3F8 300 

C4F6 300 37 0.1753 0.164699 0.02745 

C5F8 300 12 0.13335 0.07978 0.023031

c-C4F8 300 77 0.173786 0.101852 0.011607

CF4 300 75 0.626837 0.211748 0.024451

CH2F2 300 38 0.144961 0.099668 0.016168

CH3F 300 

CHF3 300 65 0.386291 0.242589 0.030089

NF3 300 52 0.171608 0.140556 0.019492

SF6 300 37 0.225802 0.125774 0.020677
February 28, 2012 

58 

Table 3-2. 3-Factor model – input gas 

Principal Film 

Type 

Input 

Gas 

Name 

Wafer 

Diameter

Data 

Sets 

Emission 

Factor Std Dev Std Err 

Metal C2F6 200 

Silicon C2F6 200 

Nitride C2F6 200 1 0.559 

Oxide C2F6 200 39 0.766949 0.166738 0.0267 

Nitride C2HF5 200 

Metal C2HF5 200 

Silicon C2HF5 200 

Oxide C2HF5 200 31 0.064387 0.029157 0.005237

Nitride C3F8 200 

Oxide C3F8 200 

Metal C3F8 200 

Silicon C3F8 200 

Nitride C4F6 200 

Metal C4F6 200 

Silicon C4F6 200 

Oxide C4F6 200 14 0.16645 0.220904 0.059039

Nitride C5F8 200 

Oxide C5F8 200 

Metal C5F8 200 

Silicon C5F8 200 

Nitride c-C4F8 200 

Metal c-C4F8 200 

Silicon c-C4F8 200 

Oxide c-C4F8 200 44 0.139814 0.09066 0.013668

Metal CF4 200 

Nitride CF4 200 24 0.638308 0.105781 0.021592

Oxide CF4 200 101 0.845594 0.28517 0.028376

Silicon CF4 200 1 0.815 

Metal CH2F2 200 

Silicon CH2F2 200 

Nitride CH2F2 200 1 0.336 

Oxide CH2F2 200 42 0.121579 0.100567 0.015518

Oxide CH3F 200 

Metal CH3F 200 

Silicon CH3F 200 

Nitride CH3F 200 1 0.6587 
February 28, 2012 

59 

Metal CHF3 200 1 0.516 

Nitride CHF3 200 3 0.6 0.34641 0.2 

Oxide CHF3 200 126 0.499079 0.259389 0.023108

Silicon CHF3 200 1 0.615 

Nitride NF3 200 

Metal NF3 200 

Oxide NF3 200 20 0.1975 0.125125 0.027979

Silicon NF3 200 2 0.22795 0.268771 0.19005 

Metal SF6 200 20 0.8426 0.036789 0.008226

Nitride SF6 200 61 0.588443 0.314637 0.040285

Oxide SF6 200 4 0.21175 0.021109 0.010554

Silicon SF6 200 4 0.463375 0.226342 0.113171

Nitride C2F6 300 

Metal C2F6 300 

Silicon C2F6 300 

Oxide C2F6 300 1 0.811 

Nitride C2HF5 300 

Oxide C2HF5 300 

Metal C2HF5 300 

Silicon C2HF5 300 

Nitride C3F8 300 

Oxide C3F8 300 

Metal C3F8 300 

Silicon C3F8 300 

Nitride C4F6 300 

Metal C4F6 300 

Silicon C4F6 300 

Oxide C4F6 300 37 0.1753 0.164699 0.02745 

Oxide C5F8 300 

Metal C5F8 300 

Silicon C5F8 300 

Nitride C5F8 300 12 0.13335 0.07978 0.023031

Metal c-C4F8 300 

Silicon c-C4F8 300 

Nitride c-C4F8 300 4 0.282175 0.15209 0.076045

Oxide c-C4F8 300 73 0.167847 0.096426 0.011286

Metal CF4 300 2 0.46695 0.029486 0.02085 

Nitride CF4 300 25 0.545381 0.172166 0.034433

Oxide CF4 300 27 0.711041 0.130065 0.025031

Silicon CF4 300 21 0.630775 0.296402 0.06468 

Metal CH2F2 300 
February 28, 2012 

60 

Silicon CH2F2 300 

Nitride CH2F2 300 25 0.119976 0.083434 0.016687

Oxide CH2F2 300 13 0.193008 0.113601 0.031507

Nitride CH3F 300 

Oxide CH3F 300 

Metal CH3F 300 

Silicon CH3F 300 

Metal CHF3 300 1 0.158 

Nitride CHF3 300 6 0.297086 0.231031 0.094318

Oxide CHF3 300 43 0.412213 0.244959 0.037356

Silicon CHF3 300 15 0.362883 0.245285 0.063332

Metal NF3 300 4 0.297425 0.228439 0.114219

Nitride NF3 300 19 0.134963 0.057119 0.013104

Oxide NF3 300 18 0.127656 0.118902 0.028025

Silicon NF3 300 11 0.261076 0.184467 0.055619

Metal SF6 300 

Nitride SF6 300 2 0.26688 0.026243 0.018556

Oxide SF6 300 10 0.178124 0.017261 0.005458

Silicon SF6 300 25 0.241587 0.149134 0.029827

Table B-3. 2-Factor model – byproducts 

Input 

Gas 

Name 

Wafer 

Diameter

Byproduct 

Name N 

Byproduct 

Mass 

Emission 

Factor 

Mean 

Byproduct 

Mass 

Emission 

Factor Std 

Dev 

Byproduct 

Mass 

Emission 

Factor Std 

Err 

C2F6 200 CF4 36 0.10538056 0.0968198 0.016136626

C2HF5 200 C2F6 31 0.02432258 0.0164365 0.002952086

C2HF5 200 CF4 31 0.07651613 0.0385001 0.006914823

C4F6 200 C2F6 12 0.11414167 0.1702086 0.049135 

C4F6 200 CF4 12 0.13167833 0.1787531 0.051601577

C4F6 200 CHF3 1 0.0657 

C4F6 300 C2F6 22 0.08965455 0.1582254 0.033733765

C4F6 300 CF4 33 0.06617273 0.1329131 0.023137202

C4F6 300 CHF3 30 0.02166333 0.0354537 0.006472937

C5F8 300 C2F6 9 0.08260503 0.0926028 0.030867608

C5F8 300 CF4 12 0.14538166 0.078004 0.022517811
February 28, 2012 

61 

C5F8 300 CHF3 8 0.01034409 0.0170478 0.006027293

c-C4F8 200 C2F6 38 0.03709211 0.0667111 0.010821964

c-C4F8 200 CF4 41 0.11856829 0.1148871 0.017942356

c-C4F8 300 C2F6 50 0.02999 0.0222442 0.003145802

c-C4F8 300 C4F6 2 0.0177 0.0019799 0.0014 

c-C4F8 300 CF4 71 0.04609859 0.0577529 0.006854014

c-C4F8 300 CHF3 65 0.02759692 0.0357803 0.004437996

CF4 200 C2F6 96 0.04789008 0.0686744 0.00700905 

CF4 200 CHF3 60 0.10670562 0.0667775 0.008620937

CF4 300 C2F6 60 0.09201694 0.1110254 0.014333321

CF4 300 c-C4F8 3 0.00063333 5.774E-05 3.33333E-05 

CF4 300 CHF3 40 0.01134105 0.0172077 0.002720782

CH2F2 200 C2F6 34 0.02545 0.0166147 0.0028494 

CH2F2 200 CF4 40 0.0805575 0.054503 0.008617679

CH2F2 200 CHF3 1 0.0657 

CH2F2 300 C2F6 15 0.06786 0.0788422 0.020356957

CH2F2 300 CF4 27 0.0586963 0.0840543 0.016176248

CH2F2 300 CHF3 27 0.05202963 0.0475198 0.00914519 

CHF3 200 C2F6 65 0.03127846 0.0309782 0.003842377

CHF3 200 CF4 72 0.08526944 0.0433518 0.005109064

CHF3 300 C2F6 47 0.07840417 0.1252889 0.018275263

CHF3 300 C4F6 1 0.0001 

CHF3 300 c-C4F8 2 0.0008 0.0001414 0.0001 

CHF3 300 CF4 48 0.10017072 0.1946045 0.028088742

NF3 200 CF4 1 0.004 

NF3 300 C2F6 11 0.05726364 0.0623368 0.018795247

NF3 300 CF4 31 0.05246774 0.0594741 0.010681868

NF3 300 CHF3 9 0.03476667 0.0417014 0.01390047 

SF6 200 C2F6 1 0.167 

SF6 200 CF4 2 0.1465 0.1746554 0.1235 

SF6 300 C2F6 8 0.0674816 0.0537639 0.019008407

SF6 300 CF4 15 0.04509885 0.0680194 0.017562538

 
February 28, 2012 

62 

Table B-4. 3-Factor Model – byproducts 

Principal 

Film 

Type 

Input 

Gas 

Name 

Wafer 

Diameter

Byproduct 

Name N 

Byproduct 

Mass 

Emission 

Factor Mean 

Byproduct 

Mass 

Emission 

Factor Std 

Dev 

Byproduct 

Mass 

Emission 

Factor Std 

Err 

Metal CF4 300 C2F6 2 0.134 0.0504874 0.0357 

Metal CF4 300 CHF3 2 0.0067 0.0038184 0.0027 

Metal CHF3 200 CF4 1 0.018 

Metal NF3 300 CF4 3 0.0039 0.0057376 0.0033126 

Nitride C5F8 300 C2F6 9 0.082605026 0.0926028 0.03086761

Nitride C5F8 300 CF4 12 0.145381659 0.078004 0.02251781

Nitride C5F8 300 CHF3 8 0.010344088 0.0170478 0.00602729

Nitride c-C4F8 300 C2F6 1 0.0339 

Nitride c-C4F8 300 CF4 1 0.0627 

Nitride c-C4F8 300 CHF3 3 0.008633333 0.0089002 0.00513853

Nitride CF4 200 C2F6 20 0.0142235 0.0045696 0.00102179

Nitride CF4 200 CHF3 20 0.1069905 0.0261674 0.00585121

Nitride CF4 300 C2F6 21 0.164264644 0.0796156 0.01737355

Nitride CF4 300 CHF3 18 0.017505556 0.0235135 0.00554218

Nitride CH2F2 300 C2F6 6 0.136533333 0.0682956 0.02788154

Nitride CH2F2 300 CF4 18 0.0841 0.0930712 0.02193709

Nitride CH2F2 300 CHF3 18 0.072977778 0.0447172 0.01053995

Nitride CHF3 300 C2F6 4 0.213264379 0.152866 0.07643299

Nitride NF3 300 CF4 6 0.059866667 0.0415835 0.01697639

Nitride NF3 300 CHF3 5 0.05378 0.0482037 0.02155735

Nitride SF6 300 C2F6 2 0.001623413 0.000993 0.00070215

Nitride SF6 300 CF4 2 0.00663536 0.0048364 0.00341989

Oxide C2F6 200 CF4 36 0.105380556 0.0968198 0.01613663

Oxide C2HF5 200 C2F6 31 0.024322581 0.0164365 0.00295209

Oxide C2HF5 200 CF4 31 0.076516129 0.0385001 0.00691482

Oxide C4F6 200 C2F6 12 0.114141667 0.1702086 0.049135 

Oxide C4F6 200 CF4 12 0.131678333 0.1787531 0.05160158

Oxide C4F6 200 CHF3 1 0.0657 

Oxide C4F6 300 C2F6 22 0.089654545 0.1582254 0.03373377

Oxide C4F6 300 CF4 33 0.066172727 0.1329131 0.0231372 

Oxide C4F6 300 CHF3 30 0.021663333 0.0354537 0.00647294

Oxide c-C4F8 200 C2F6 38 0.037092105 0.0667111 0.01082196

Oxide c-C4F8 200 CF4 41 0.118568293 0.1148871 0.01794236

Oxide c-C4F8 300 C2F6 49 0.029910204 0.0224675 0.00320964

Oxide c-C4F8 300 C4F6 2 0.0177 0.0019799 0.0014 
February 28, 2012 

63 

Oxide c-C4F8 300 CF4 70 0.045861429 0.0581351 0.00694847

Oxide c-C4F8 300 CHF3 62 0.028514516 0.03636 0.00461773

Oxide CF4 200 C2F6 75 0.056146373 0.0750613 0.00866733

Oxide CF4 200 CF4 1 0.271 

Oxide CF4 200 CHF3 40 0.106563175 0.0800773 0.01266133

Oxide CF4 300 C2F6 21 0.06992381 0.1395639 0.03045533

Oxide CF4 300 c-C4F8 3 0.000633333 5.774E-05 3.3333E-05 

Oxide CF4 300 CHF3 18 0.006683333 0.0070901 0.00167116

Oxide CH2F2 200 C2F6 34 0.02545 0.0166147 0.0028494 

Oxide CH2F2 200 CF4 40 0.0805575 0.054503 0.00861768

Oxide CH2F2 200 CHF3 1 0.0657 

Oxide CH2F2 300 C2F6 9 0.022077778 0.0454746 0.0151582 

Oxide CH2F2 300 CF4 9 0.007888889 0.0140803 0.00469345

Oxide CH2F2 300 CHF3 9 0.010133333 0.0112981 0.00376604

Oxide CHF3 200 C2F6 64 0.030173438 0.0299041 0.00373801

Oxide CHF3 200 CF4 71 0.086216901 0.0429031 0.00509166

Oxide CHF3 300 C2F6 29 0.067944828 0.1380088 0.02562758

Oxide CHF3 300 C4F6 1 0.0001 

Oxide CHF3 300 c-C4F8 2 0.0008 0.0001414 0.0001 

Oxide CHF3 300 CF4 37 0.070329522 0.1083424 0.01781138

Oxide NF3 300 C2F6 6 0.05395 0.0575506 0.02349493

Oxide NF3 300 CF4 11 0.0705 0.0648868 0.0195641 

Oxide NF3 300 CHF3 1 0.0294 

Oxide SF6 200 C2F6 1 0.167 

Oxide SF6 200 CF4 1 0.27 

Oxide SF6 300 CF4 2 0.000342211 6.598E-06 4.6658E-06 

Silicon CF4 200 C2F6 1 0.102 

Silicon CF4 300 C2F6 16 0.020941194 0.016675 0.00416875

Silicon CF4 300 CHF3 2 0.002421092 0.0017931 0.0012679 

Silicon CHF3 200 C2F6 1 0.102 

Silicon CHF3 300 C2F6 14 0.061538478 0.0546094 0.01459498

Silicon CHF3 300 CF4 11 0.200545651 0.3483658 0.10503625

Silicon NF3 200 CF4 1 0.004 

Silicon NF3 300 C2F6 5 0.06124 0.0744202 0.03328172

Silicon NF3 300 CF4 11 0.043645455 0.0659261 0.01987747

Silicon NF3 300 CHF3 3 0.004866667 0.0070465 0.00406831

Silicon SF6 200 CF4 1 0.023 

Silicon SF6 300 C2F6 6 0.089434334 0.0416336 0.01699683

Silicon SF6 300 CF4 11 0.060229777 0.0743411 0.02241469

C. Non-Etch Emissions Factors Used in Analysis 
February 28, 2012 

64 

The primary and byproduct emission factors given in Table I-3 and I-4 for Chamber Cleaning and Wafer 

Cleaning were used in this analysis to represent those processes. 

 
February 28, 2012 

65 

D. Fab level gas use distribution 

Table D-1: Fab Level Gas Use Distribution (200mm) 

FAB Year Wafer 

Size 

Input 

Gas 

Gas Use (kg) 

Etch 

Usage 

Wafer 

Clean 

Usage 

CVD In 

Situ 

Plasma 

CVD 

Remote 

Plasma 

CVD

In-Situ 

Thermal

B 2009 200 C2F6 1,135 21,563

B 2009 200 C2HF5

B 2009 200 C3F8

B 2009 200 C4F6

B 2009 200 C5F8 78

B 2009 200 c-C4F8 833 442

B 2009 200 CF4 872 120 98

B 2009 200 CH2F2 4

B 2009 200 CH3F 13

B 2009 200 CHF3 912

B 2009 200 NF3 410 13 3,603 2,484

B 2009 200 SF6 540 391

B 2010 200 C2F6 1,122 27,019

B 2010 200 C2HF5 0

B 2010 200 C3F8 0

B 2010 200 C4F6 0

B 2010 200 C5F8 85

B 2010 200 c-C4F8 1,329 4,557

B 2010 200 CF4 1,350 200 314

B 2010 200 CH2F2 12

B 2010 200 CH3F 17

B 2010 200 CHF3 1,452

B 2010 200 NF3 409 16 6,443 2,095

B 2010 200 SF6 1,039 766

C 2009 200 C2F6 1,101 34 8,300

C 2009 200 C2HF5

C 2009 200 C3F8 272

C 2009 200 C4F6

C 2009 200 C5F8

C 2009 200 c-C4F8 186

C 2009 200 CF4 605 85 6,280

C 2009 200 CH2F2 12

C 2009 200 CH3F

C 2009 200 CHF3 382 70

C 2009 200 NF3 1,730 6,470
February 28, 2012 

66 

C 2009 200 SF6 2,157

C 2010 200 C2F6 1,200 7,901 0

C 2010 200 C2HF5 0 0 0

C 2010 200 C3F8 0 9,049 0

C 2010 200 C4F6 0 0

C 2010 200 C5F8 0 0

C 2010 200 c-C4F8 317 0 0

C 2010 200 CF4 272 73 722 606

C 2010 200 CH2F2 23 0 0

C 2010 200 CH3F 0 0 0

C 2010 200 CHF3 589 117 0 0

C 2010 200 NF3 0 1,142 11,989

C 2010 200 SF6 3,612 0 0

Table D-2: Fab Level Gas Use Distribution (300mm) 

FAB Year Wafer 

Size 

Input 

Gas 

Gas Use (kg) 

Etch 

Usage 

Wafer 

Clean 

Usage 

CVD In 

Situ 

Plasma 

CVD 

Remote 

Plasma 

CVD

In-Situ 

Thermal

A 2009 300 C2F6

A 2009 300 C2HF5

A 2009 300 C3F8

A 2009 300 C4F6

A 2009 300 C5F8 450

A 2009 300 c-C4F8 273

A 2009 300 CF4 2,581 143

A 2009 300 CH2F2

A 2009 300 CH3F 100

A 2009 300 CHF3 317

A 2009 300 NF3 4,427 17,706

A 2009 300 SF6 454

A 2010 300 C2F6 0

A 2010 300 C2HF5

A 2010 300 C3F8

A 2010 300 C4F6

A 2010 300 C5F8 525

A 2010 300 c-C4F8 220

A 2010 300 CF4 2,573 222

A 2010 300 CH2F2 100

A 2010 300 CH3F 120

A 2010 300 CHF3 345

A 2010 300 NF3 0 6,636 26,545
February 28, 2012 

67 

A 2010 300 SF6 318

E 2009 300 C2F6 0 0 259 0 0

E 2009 300 C2HF5 0 0 0 0 0

E 2009 300 C3F8 0 0 0 0 0

E 2009 300 C4F6 44 0 0 0 0

E 2009 300 C5F8 163 0 0 0 0

E 2009 300 c-C4F8 544 0 0 0 0

E 2009 300 CF4 3,972 423 0 0 0

E 2009 300 CH2F2 159 0 0 0 0

E 2009 300 CH3F 115 0 0 0 0

E 2009 300 CHF3 585 0 0 0 0

E 2009 300 NF3 16,502 559 6,125 8,526 0

E 2009 300 SF6 2,449 0 0 0 0

E 2010 300 C2F6 0 0 0 0 0

E 2010 300 C2HF5 0 0 0 0 0

E 2010 300 C3F8 0 0 0 0 0

E 2010 300 C4F6 79 0 0 0 0

E 2010 300 C5F8 326 0 0 0 0

E 2010 300 c-C4F8 1,089 0 0 0 0

E 2010 300 CF4 5,570 581 0 0 0

E 2010 300 CH2F2 222 0 0 0 0

E 2010 300 CH3F 147 0 0 0 0

E 2010 300 CHF3 1,012 0 0 0 0

E 2010 300 NF3 13,907 471 8,528 11,599 0

E 2010 300 SF6 2,359 0 0 0 0
February 28, 2012 

68 

E. F-Gas Abatement in 300mm Fabs 

Table E-1: Fraction of Gas Use Subject to Abatement 

FAB Year Wafer 

Size 

Input 

Gas 

 Etch 

Usage 

 Wafer 

Clean 

Usage 

 CVD In 

Situ 

Plasma 

 CVD 

Remote 

Plasma 

 CVD 

In-Situ 

Thermal 

A 2009 300 C2F6 0.000 0.000 0.000 0.000 0.000

A 2009 300 C2HF5 0.000 0.000 0.000 0.000 0.000

A 2009 300 C3F8 0.000 0.000 0.000 0.000 0.000

A 2009 300 C4F6 0.000 0.000 0.000 0.000 0.000

A 2009 300 C5F8 0.000 0.000 0.000 0.000 0.000

A 2009 300 c-C4F8 0.000 0.000 0.000 0.000 0.000

A 2009 300 CF4 0.000 0.000 0.000 0.000 0.000

A 2009 300 CH2F2 0.000 0.000 0.000 0.000 0.000

A 2009 300 CH3F 0.000 0.000 0.000 0.000 0.000

A 2009 300 CHF3 0.000 0.000 0.000 0.000 0.000

A 2009 300 NF3 0.000 0.000 0.750 0.750 0.000

A 2009 300 SF6 0.000 0.000 0.000 0.000 0.000

A 2010 300 C2F6 0.000 0.000 0.000 0.000 0.000

A 2010 300 C2HF5 0.000 0.000 0.000 0.000 0.000

A 2010 300 C3F8 0.000 0.000 0.000 0.000 0.000

A 2010 300 C4F6 0.000 0.000 0.000 0.000 0.000

A 2010 300 C5F8 0.000 0.000 0.000 0.000 0.000

A 2010 300 c-C4F8 0.000 0.000 0.000 0.000 0.000

A 2010 300 CF4 0.000 0.000 0.000 0.000 0.000

A 2010 300 CH2F2 0.000 0.000 0.000 0.000 0.000

A 2010 300 CH3F 0.000 0.000 0.000 0.000 0.000

A 2010 300 CHF3 0.000 0.000 0.000 0.000 0.000

A 2010 300 NF3 0.000 0.000 0.750 0.750 0.000

A 2010 300 SF6 0.000 0.000 0.000 0.000 0.000

E 2009 300 C2F6 0.000 0.000 1.000 0.000 0.000

E 2009 300 C2HF5 0.000 0.000 0.000 0.000 0.000

E 2009 300 C3F8 0.000 0.000 0.000 0.000 0.000

E 2009 300 C4F6 1.000 0.000 0.000 0.000 0.000

E 2009 300 C5F8 1.000 0.000 0.000 0.000 0.000

E 2009 300 c-C4F8 1.000 0.000 0.000 0.000 0.000

E 2009 300 CF4 1.000 1.000 0.000 0.000 0.000

E 2009 300 CH2F2 1.000 0.000 0.000 0.000 0.000

E 2009 300 CH3F 1.000 0.000 0.000 0.000 0.000

E 2009 300 CHF3 1.000 0.000 0.000 0.000 0.000

E 2009 300 NF3 1.000 1.000 1.000 1.000 0.000

E 2009 300 SF6 1.000 0.000 0.000 0.000 0.000
February 28, 2012 

69 

E 2010 300 C2F6 0.000 0.000 0.000 0.000 0.000

E 2010 300 C2HF5 0.000 0.000 0.000 0.000 0.000

E 2010 300 C3F8 0.000 0.000 0.000 0.000 0.000

E 2010 300 C4F6 1.000 0.000 0.000 0.000 0.000

E 2010 300 C5F8 1.000 0.000 0.000 0.000 0.000

E 2010 300 c-C4F8 1.000 0.000 0.000 0.000 0.000

E 2010 300 CF4 1.000 1.000 0.000 0.000 0.000

E 2010 300 CH2F2 1.000 0.000 0.000 0.000 0.000

E 2010 300 CH3F 1.000 0.000 0.000 0.000 0.000

E 2010 300 CHF3 1.000 0.000 0.000 0.000 0.000

E 2010 300 NF3 1.000 1.000 1.000 1.000 0.000

E 2010 300 SF6 1.000 0.000 0.000 0.000 0.000

 

All 300mm 2009 300 C2F6 0.000 0.000 1.000 0.000 0.000

All 300mm 2009 300 C2HF5 0.000 0.000 0.000 0.000 0.000

All 300mm 2009 300 C3F8 0.000 0.000 0.000 0.000 0.000

All 300mm 2009 300 C4F6 0.030 0.000 0.000 0.000 0.000

All 300mm 2009 300 C5F8 0.252 0.000 0.000 0.000 0.000

All 300mm 2009 300 c-C4F8 0.111 0.000 0.000 0.000 0.000

All 300mm 2009 300 CF4 0.215 0.351 0.000 0.000 0.000

All 300mm 2009 300 CH2F2 0.147 0.000 0.000 0.000 0.000

All 300mm 2009 300 CH3F 0.534 0.000 0.000 0.000 0.000

All 300mm 2009 300 CHF3 0.146 0.000 0.000 0.000 0.000

All 300mm 2009 300 NF3 0.741 1.000 0.686 0.566 0.000

All 300mm 2009 300 SF6 0.569 0.000 0.000 0.000 0.000

All 300mm 2010 300 C2F6 0.000 0.000 0.000 0.000 0.000

All 300mm 2010 300 C2HF5 0.000 0.000 0.000 0.000 0.000

All 300mm 2010 300 C3F8 0.000 0.000 0.000 0.000 0.000

All 300mm 2010 300 C4F6 0.035 0.000 0.000 0.000 0.000

All 300mm 2010 300 C5F8 0.336 0.000 0.000 0.000 0.000

All 300mm 2010 300 c-C4F8 0.190 0.000 0.000 0.000 0.000

All 300mm 2010 300 CF4 0.248 0.371 0.000 0.000 0.000

All 300mm 2010 300 CH2F2 0.130 0.000 0.000 0.000 0.000

All 300mm 2010 300 CH3F 0.550 0.000 0.000 0.000 0.000

All 300mm 2010 300 CHF3 0.164 0.000 0.000 0.000 0.000

All 300mm 2010 300 NF3 0.652 1.000 0.735 0.614 0.000

All 300mm 2010 300 SF6 0.618 0.000 0.000 0.000 0.000

 
February 28, 2012 

70 

F. Revision of charts presented in November 2011 

The briefing on preliminary results given to EPA in November 2011 has been revised to account for 

results presented in this report. The added and corrected slides are available as a separate document. 

Its title page is given below. 

Accelerating Sustainable Manufacturing

Copyright ©2010 

Advanced Materials Research Center, AMRC, International SEMATECH Manufacturing Initiative, and ISMI are servicemarks of SEMATECH, and the SEMATECH logo are registered servicemarks of 

SEMATECH, Inc. All other servicemarks and trademarks are the property of their respective owners.

Additions & Corrections: 

ISMI/SIA Alternative to RecipeSpecific Testing of 17 Nov 2011

Laurie Beu

Consultant to ISMI

Michael Haslam

Predictum, Consultant to ISMI

February 13, 2012

SIA v. EPA, (D.C. Cir. No. 1024)

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