February 28, 2012

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### Report to EPA on Etch Factor Proposal for Fab GHG E missions ReportingFebruary 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 manufactu ring, including semiconductor fabs. The procedures in that rule present significant problem s for the semiconductor industry. The rule specifi es 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 technic ally infeasible and are otherwise costly and difficult t o accomplish. Further, this method requires the us e and disclosure of Confidential Business Information (CBI).
For these and other reasons, on January 30, 2011, S IA filed a petition for review and a petition for reconsideration of the rule. SIA and EPA are now w orking together to find a better approach to reporting F-Gas emissions that will be practical an d accurate and will minimize the need to use confidential information.
SIA is advocating the adoption of two alternative m ethods and has both collected data and conducted analyses in support of those two alternative method s. One of the two is referred to as the Etch Facto r Method. This approach is a refinement of the Subpa rt I Tier 2c method, with industry-level etch proce ss emissions factors developed using a greatly expande d database of etch emission factor data (differentiated by gas and wafer size), and with e tch and wafer clean processes combined. The factor s in 40CFR Part 98 Subpart I would be used for non-et ch activities. SIA and ISMI have carried out an extensive data gathering effort to support the deve lopment of new etch emissions factors.
At the time the Subpart I rule was adopted, EPA had relatively limited data on emissions from etch too ls, the gas use in etch tools, and the performance of a batement devices. After months of data gathering and analysis, we are now in a position to create an d 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, whe re emission factors are a function of input gas and wafer size, and a 3-Factor model, which adds film t ype as the third factor. These models were selecte d through detailed analysis of the statistical relati onships of 13 attributes of the etch process to etc h 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 m odels and an initial analysis of the precision and accuracy of the Etch Factor Method as compared to t he IPCC Tier 2b, EPA Tier 2c, and Tier 3 methods. On November 30, 2011, there was a similar presentat ion on the alternative Stack Test Method. EPA and SIA have developed and agreed to a Work Plan addres sing these alternatives and the other issues associated with modifying the Subpart I reporting r ule. That Work Plan included a set of questions fr om EPA as follow up to the November 17 th meeting. Most of those questions were addressed i n a February 3 rd submittal to EPA. This submittal addresses the re maining questions related to the Etch Factor Method. (Questions and comments related to the Sta ck 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 indu stry-level. Further precision and accuracy analysi s of the 2-Factor model was then done at the facility-le vel to demonstrate the viability of the model forFebruary 28, 2012

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facility level reporting. This work is a major refi nement of the work presented in November. It inclu des some corrections to data, an assessment of precisio n based on expected error rather than worst case error, the extension of gas apportioning error to t he 2-Factor model and the non-etch sources of emissions affected by gas apportioning, the precisi on 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 statistica l analysis of the emissions estimation model options, building on the additional data that we ha ve 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

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2. The 2-Factor Model is more accurate and precise tha n the available alternative Tier 2 models (IPCC Tier 2B, EPA Tier 2C)

3. The 3-Factor Model offers no benefits over the 2-Fa ctor model and the 2-factor model provides a slightly more conservative estimation of semicond uctor 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 page s present and briefly discuss selected results of o ur analyses. The complete analytical results are pres ented and explained in detail in Section 5 of this report.

1 Due to the technical challenges of the recipe appr oach in the current Subpart I, we have not been abl e to apply it to a fab for a direct comparison with the 2-Factor Model.February 28, 2012

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Table E-1 shows facility-level 2 emissions estimates created using five methods. A s stated above, we are using the Tier 3 model to be the closest representa tion 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 emis sions.

Table E-1: Facility Level Accuracy of Etch F-Gas E mission Estimates By Various Methods, (Based on a Comparison to Tier 3 Emissions Estimate s For One 200mm and Two 300mm Fabs, using Expected Error via the Error Propagation Meth od, and without Considering Abatement)

Year Estimation Method Etch F-Gas Emissions Total F

- Gas

Emissions (Etch + Non-Etch) Emissions (mtCO 2 e) Emissions

(mtCO 2 e)

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

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Table E-2 compares the industry level 3 performance of the SIA 2-Factor Model, the SIA 3-F actor Model and the two IPCC and EPA Tier-2 models, when the er ror 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 prov ide 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 P ropagation Method, Not Considering Gas Apportioning Error or Abatement )

Year Estimation Method Etch F-Gas Emissions Total F

- Gas Emissions(Etch + NonEtch)

Etch Emissions (mtCO 2 e) Error

Standard Deviation (mtCO 2 e) 95%

Confidence Interval (Percent) Total

Emissions (mtCO 2 e)

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% 428,60 0 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 analysi s is based on the composite gas use of 12 fabs, sev en 200mm and five 300 mm, that are representative of the ran ge 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 included 4 . Its precision in predicting etch emissions has a 95% confidence interval that i s less than ± 30%. For total F-Gas emissions, the 95% confidence interval is less than ± 20%.

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

(Expected error; Monte Carlo Method; Accounting for Gas Apportioning and Etch Emission F actor Errors and Abatement in 300mm Fabs)

Etch F-Gas Emissions Total F-Gas Emissions (Etch + Non-Etch)

Wafer Size Year Etch

Emissions (mtCO 2 e) Error

Standard Deviation (mtCO 2 e) 95% Confidence

Interval (Percent) Total

Emissions (mtCO 2 e) 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 apportionm ent error was analyzed, the results are based on Mo nte Carlo simulations.February 28, 2012

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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 o ne fab with a higher interval is a fab that is full y 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 Emiss ion 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 ha s identified a few gas and tool combinations where added data is expected to reduce emissions factor v ariability. Additional data is being sought from f ab operators and tool manufacturers and those data wil l be available in the first half of 2012. The incl usion of this data should further improve the precision a chieved 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 electro nics manufacturing facilities, including semiconduc tor fabs, requires fabs to calculate and report emissio ns 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 fiv e primary reasons:

• Technical infeasibility – The rule calls for the me asurement 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 mat ter.

• 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 collaboratio n 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 a ll sectors, despite the industry’s very small contribution to overall emissions.

• Disclosure of proprietary intellectual property – B y 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 reconsiderat ion of the rule on January 30, 2011. The petition for rec onsideration sets forth in considerable detail a nu mber of concerns with the rule, including the concerns i dentified above. SIA’s goal is to work with EPA to develop alternative, more reasonable and technicall y sound methods of reporting.

2.2 SIA legal challenge and this ongoing cooperative e ffort 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 agreemen t on alternative reporting methods. SIA and member companies, in partnership with ISMI, have un dertaken a significant effort to collect and analyz e data from semiconductor fabs and fab tool manufactu rers 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 hopef ul that, working together with EPA, the Agency willFebruary 28, 2012

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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 outstandin g 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 E PA with additional information and to respond to th e Agency’s questions about the reporting alternatives , other concerns with the current rule, and the supporting data and analyses. EPA is reviewing thi s information, and we hope to develop the broad outlines of a proposed rule by April 2012 that ensu res technical feasibility considering burden and co st. 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 submitta l refers to additional analyses that would be provi ded in February. This document addresses the additiona l 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 factor s and the performance of abatement systems. This was a significant concern when the rule was adopted. SIA and ISMI have undertaken the following data dev elopment activities in order to improve the emissions factors for fab F-Gas emissions, to creat e a data base on the performance of abatement devices, and to provide a basis of evaluating the a ppropriateness 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 se miconductor device manufacturers provide etch emissions characterization data so that more robust industry emission factors could be developed. ISM I has combined this emission factor data and the IPCC 2006 and NODA datasets to create the ISMI emission factor database. The combined database i ncludes 976 input gas data points of which 883 are new data points from etch equipment manufacturers a nd seven facilities operated by one semiconductor device manufacturer.
3.1.2 Abatement equipment destruction efficiency da ta

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 c alendar 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 fo r automotive and networking, embedded flash memory, DRAM and NAND flash memory devices, high en d main frame, server and graphic intensive game devices, and leading edge logic devices. Line widths of devices manufactured in responding fabs range from 250nm down to 32 nm.

a. 3 of these facilities, one 200mm and two 300 mm, ha ve 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 17 th presentation and the February 3 rd 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 provid ed information to ISMI on the level of FGas abatement installed.February 28, 2012

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3.2 The 2-Factor and 3-Factor models
SIA, working with ISMI, used the data detailed abov e to perform a statistical analysis of the expanded emissions factor data base to achieve the following objectives:

• Determine the modeling approach which best characte rizes 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 pre cision and accuracy.

The ISMI 2011 database consists of F-Gas emission c haracterization data covering steps performed in semiconductor plasma etch equipment. This data wa s used to model emission factors for both main input gases and byproduct gasses.

The ISMI 2011 database consists of both etch and “n on-etch” steps performed in plasma etch equipment. The following definitions apply:

• Etch Step : Step where a pattern is being etched in a produc t 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 invo lve etching a patterned wafer. Non-etch processing include aging / cleaning / cond itioning / seasoning / dry clean season. Nonetch processing typically occurs infrequently (seve ral 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 i nput 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 S ubpart I.

• Refined Emission Factors : Default factors developed using this ISMI 2011 da ta 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 i nitial 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 fun ction of the F-Gas used and wafer size, those being the two factors. The “3-Factor” model has emission s factors as a function of these two factors plus f ilm 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 relationsh ips between the key etch process parameters and the emissions factors.February 28, 2012

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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 dat a is shown in Figure 3-1.
Figure 3-1: Distribution of Process Type: E tch vs. Non-Etch StepsFebruary 28, 2012

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Statistical analysis of variance (ANOVA) modeling w as conducted using the JMP software system. Models were constructed for each parameter individu ally as well as for all parameters and their interactions together. Several of these input para meters had too much missing data to be used in the modeling. Models that were built using interaction s tended to have extensive singularities and were discarded. Some of these parameters are highly co rrelated over parts of the process space. For high ly 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 Fa ctors.
The exploration of possible models resulted in the summary shown in Table 3-3. For individual factor models, input gas gave the largest R 2 value, followed by power and wafer diameter. Howe ver, there are correlations amongst these parameters.
Detailed exploration of the distribution of the fit s 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 distribution s were used.February 28, 2012

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The R 2 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 fil m type in these multi-factor models. Adding additi onal parameters beyond these 3-factor models made no app reciable difference to the R 2 values.
A decision was made to select two possible models f or further consideration: a two factor model and a more complex three factor model with the potential of higher R 2 . With power and principal film type both playing a roughly equal but small role; a choi ce 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, p rincipal film type was chosen
A second analysis was then conducted by adding proc ess 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 et ch 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 mod el
Two models were carried forward in the analysis. H owever, there are difficulties with emission factor calculations when the data is broken into the small er and smaller distinct groups that come with additional model parameters. The number of distinc t 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% R 2

.

• 3-Factor model (Gas, Wafer Size, Film) has 43 categ ories with associated gas use and gives 60% R 2 ; this is much more complex and impacts only the et ch portion of industry’s F-Gas emissions estimate.

Table 3-3: Summary of Models Considered – Etch Dat a Set Model Parameter(s) Model R 2
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 mod el 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 et ch 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 proc ess using fluorinated GHG reagents to selectively r emove materials from a substrate during electronics manufacturing.” Waf er cleaning is similar to etch in that etch F-Gas es 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 re presents 1% or less of the gas use for the 12 facil ities analyzed here as repre sentative 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 gas es: 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 e mission factor data over the categories in the 2 Factor and 3- Factor models. This approach was chosen for simpli city and its Two types of emission factors were calculate factors. A total of 976 etch and non Byproduct emission fact ors 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 emissi on 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 mod el 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 et ch 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 r emove materials from a substrate during electronics manufacturing.” Waf er cleaning is similar to etch in that

es are used to remove contaminants or residues from the wafer surfac e. The tools specifically designated for wafer cleaning use the same gases in plasma to remove these materials and emissions are similar. Wafer cleaning currently re presents 1% or less of the gas use for the 12 facil ities

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 gas es: 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 e mission factor data over the categories in the 2

Factor models. This approach was chosen for simpli city and its direct linkage to the data. of emission factors were calculate d: input gas emission factors, and byproduct gas emiss ion

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 wit h standard erro . The diamonds represent the standard error. Note that, in general, 300mm emission factors are lower than 200 mm emissi on 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 et ch 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 r emove materials from a substrate during electronics manufacturing.” Waf er 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 re presents 1% or less of the gas use for the 12 facil ities 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 e mission factor data over the categories in the 2

- direct linkage to the data. input gas emission factors, and byproduct gas emiss ion

for the input gas emission factors. Byproduct factors were calculated in

h standard erro rs, are Note that, in general, 300mm emission factors are lower than 200 mm emissi on factors. Newer technologies present inFebruary 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 standa rd error diamonds

3.3.2 Treatment of byproducts
Byproduct emission factors were calculated for the 2 considerations when working with byproducts.

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

2. When a test results in no byproduct for a particula r 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 part 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 close between 200mm and 300mm.

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

3: Three factor model emission factors, with standa rd 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 particula r 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 particula r gas, is this treated as zero byproduct or is

Gas emissions in mtCO2e for theFebruary 28, 2012

17

• All byproduct data is used; both for single input g as 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 hav e a large effect. Therefore, all byproduct data fo r both single and multi-gas tests was used to estimat e the byproduct emission factors used in the analys is 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 o f 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 curren t approach was adopted to be consistent with previous work done by the EPA and IPCC.

Table 3-5: Byproduct methodology comparisons – 2-Fa ctor Model

This methodology has implications for the error cal culations in later analysis as well as in the estim ation of byproduct emissions, because byproduct emissions may be overestimated. The current method, in effect, assigns the byproduct to the input gas in a ll cases where that input gas is used, but applies an emission factor that was computed only from tests w here the byproduct was detected. Tests where the byproduct was not detected or was too low to measur e are ignored, possibly inflating the result. Error calculations will be effected because of the artifi cially low value of N, the number of data points available. Fewer data points generally result in h igher expected errors. In general, we think this

5 As previously discussed and documented in “ISMI Re sponse to EPA Etch Data Questions” submitted to EPA in November, 2011, for multi F-gas processes, ISMI ass igns byproducts to the total mass of all input F-ga ses. 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 acco unt 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 a re formed in plasma reactions that would not otherw ise be formed following conventional thermodynamic pathway s.February 28, 2012

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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 cont aining 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 g ases
This data is considered representative of the indus try as a whole because of its size, the presence of a measured EF for almost every gas, and the use by to ol 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 f or a product type

• Memory and logic functions are increasingly integra ted

• 300 mm and above process development results from c ross industry collaboration
Looking at the data specifically by gas, wafer size and tool supplier, Figure 3-4 shows the current da ta gaps. One can see that there are two gases for whi ch there is presently no data: C5F8 for 200mm and C3F8 for 300mm. Both are used in very small amount s. 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 manufacturer s and a device manufacturer to generate additional data points to close most of the identif ied data gaps. This is discussed further in Sectio n 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 d ata in ISMI database. *

- Aggregated total usage for 2009

- 2010. Process Gas Process Gas 300 mm 200 mmFebruary 28, 2012

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3.4. Determining Error The analysis presented in this report places partic ular emphasis on the determination of the likely er rors in emission estimates of the several estimation met hods. Two primary sources of error are considered: (1) uncertainties in the emission factors used in t he 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 estima tes.
Emission calculations are performed in the later st ages of the analysis to show the effect of F-Gas abatement on emissions and error estimates, to prov ide a better estimate of the performance of the preferred estimation method in actual practice. Be cause the consideration of abatement is not central to the comparison of the alternative estimation met hods, and because its overall effect is to signific antly reduce the CO 2 e emissions estimate, no effort has been made to ac count for uncertainties in the DREs that are used.
While the early work on precision and accuracy of t he Etch Alternative that was presented in November used a concept of “worst case” error for this purpo se, 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 “r oot mean square” basis. Two forms of error analysis ha ve been employed in this work – error propagation and Monte Carlo analysis. The forms of analysis gi ve comparable (but not identical answers) and so th e error results presented here clearly identify the m ethod used.
The analysis was structured to follow two paths. F irst, a precision and accuracy analysis was conduct ed to examine the performance of four different emissi on 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 estimat e. In this, only the errors introduced by uncertai nty in the underlying emission factors were considered, because this error source will be the major determining factor of the relative performance of t he 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 Car lo methods involve the explicit simulation of variability in a process or calculation by enumerat ing the different outcomes of random processes. Th e 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 Mon te Carlo method.

3.4.1 Error Propagation
Error propagation has been used from the start of I SMI’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 rang e of datasets, different methods for preparing emission factors, alternative statistical models for emissio n factors, and different estimation methods. Error p ropagation has allowed us to track the relativeFebruary 28, 2012

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precisions of the numerous cases involved and to up date 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 a nd engineering. It is the first of two methods for combining uncertainties discussed by IPCC in Sectio n 3.2.3.1 of its Chapter 3 on Uncertainties (Monte Carlo being the other). In that discussion, IPCC n otes that certain assumptions are normally made in taking this approach, specifically that the uncerta inties being combined are uncorrelated and that the relative errors (standard deviation divided by the mean) are 0.3 or less. It also notes: “In practic e, however, the approach will give informative results even if this criterion is not strictly met and som e 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 li mit theorem” is one of the reasons that conventiona l statistics, in general, and error propagation, in p articular, are useful research tools. The theorem says that, except when dealing with small samples, the m ean 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-ave rage and facility-level emissions. In both instance s, emission factors representing sources are being ave raged and we are then estimating the relative error of the overall emissions estimate. In this context , it is an appropriate approach without regard to t he underlying data distributions.
The previous error propagation work presented in No vember of 2011 used a form of the worst case error. Error in emissions factors (EFs) and gas us age 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 provid e a form of worst case error. A standard value of 1.96 was used to calculate the 95% confidence inter vals. We now switch to expected error calculations for the propagation of error calculations.
Expected errors in mtCO2e are propagated using a ca lculation of the square root of the sum of the squares of the standard errors for the components o f interest; input gas emissions factors, byproduct emission factors, gas usage. The final, effective standard error for any given leg of a model is give n by:

           

The individual standard errors s 1 , s 2 ... are calculated as:

  

6 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Volume 1: General Guidance and Reportin g, Chapter 3 Uncertainties, p. 3.27.February 28, 2012

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

Lower 95% CL = mean - t (.025,DOFe) * s ef
Upper 95% CL = mean + t (.025,DOFe) * s ef

Where:

DOF e 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:

s i is the standard error of the component of interest

N i is the number of data points for the component of interest

Additional considerations are needed for EFs that a re missing (N=0) and represented by default values, and for EFs where insufficient information is avail able 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 th e one available measurement as the average EF, and adopt the approach of symmetric errors. For exampl e, 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 > 0.50February 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 emis sion factors or error terms:

• Missing emissions factors:

– Emission factor is set to 0 (consistent with the Fi nal 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 f actors

3.4.2 Monte Carlo Error Analysis

Monte Carlo is the second of two methods for combin ing 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 “...suit able for detailed category-by-category assessment of uncerta inty, particularly where uncertainties are large, distribution is non-normal, the algorithms are comp lex functions and/or there are correlations between some of the activity sets, emissions factors, or bo th.” 7 While the Monte Carlo method has some advantages for the assessment of emission factor er rors, we have used it in the error analysis primari ly for the purpose of simulating the emissions effect of gas apportioning errors and how they interact wi th emission factor errors.

The Monte Carlo analysis performs straightforward e mission 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 (whethe r SIA 2-Factor or 3-Factor) and add a final dimension for the number of independent trials perf ormed. 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 res ults were determined. The error standard deviation

7 2006 IPCC Guidelines for National Greenhouse Gas I nventories, Volume 1: General Guidance and Reporting , Chapter 3 Uncertainties, p. 3.32.February 28, 2012

23

was calculated by the usual statistical formula, wh ile the lower and upper 95% confidence interval bounds were calculated as the empirical 2.5 th and 97.5 th of the distribution of results, respectively.

The Monte Carlo was structured to permit the analys is of emission factor errors alone, gas apportionin g errors alone, or the combination of both error sour ces. 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 trial s. To consider gas apportioning errors along, the emis sion factor matrix EF was generated to use the same average values, without error, for each of the T=10 00 trials. To consider both error sources, the U a nd EF matrices were generated to use independently cho sen 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 r andomly 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, a nd assumed that an EF with N=1 can similarly range from its symmetric lower to upper limits with an av erage value equal to the one available measurement.

Gas apportioning errors deal with the uncertainty i n 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 err ors are more difficult to characterize in the analysis, because there is little available data on which to base the assessment. The following section describes th e 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 u sed in individual production processes and steps in a semiconductor facility, and it is not possible to m easure actual usage because the points at which gas es are used are widely distributed throughout a fab. The only information known to exist on the magnitude of gas apportioning errors comes from a m odeling exercise conducted by one of SIA’s member companies to support internal emission estim ation 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 botto m-up approach to estimate F-Gas use at fabs considering factors for the typical gas usage in ea ch production process and counts of the number of t he 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 us e 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 th e table represent the errors encountered in estimat ingFebruary 28, 2012

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total gas use at the facility level, based on the d ifficulty of, and errors encountered in, modeling g as use at more-detailed process and production step levels . They are used as inputs to drive the error analy sis.
In implementing a reporting rule, individual fabs w ould need to develop methods to translate their estimates of total gas usage at the fab (from purch ase records and other measurements) into estimates of allocated gas usage at a level that depends on t he estimation being used. For example:

• Gas usage would be allocated to each of the five EP A processes (etch, wafer cleaning, and three chamber cleaning processes) if the SIA 2-Factor mod el 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 estimat es for total gas usage at the fab would be turned into estimated shares of gas use at more detailed l evels and then applied to allocate total gas usage down to the level required for emission calculation s. It is the errors in the shares that matter for purposes of the emission calculations, not simply t he 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 exerci se that produced the error estimates in the table below. Thus, we have taken a simple, generic appro ach 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

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The starting point is an estimate of the error in e stimating total gas use at the facility level. The n, if the error in modeling total gas use is ± 10%, for examp le, and the gas is used in four different processes in the SIA 2-Factor model, we assume that the estimate s 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 t he gas were used in etch and three other non-etch processe s in the SIA 3-Factor model, then we would assume that the estimates of total gas use by film type an d process would have errors of ± 26% in each cell – that is, ± 10% * sqrt(7). Effectively, we are ass uming equal percentage errors at the more-detailed levels of a size consistent with the input error at the facility level. Other methods of assigning er rors 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 mo dified when modeled estimates are turned into shares. We believe that the basic results and conc lusions of the error analysis for gas apportioning are robust and would not be substantially changed if di fferent assumptions were used to assign the startin g point errors.
As implemented in the Monte Carlo model, we first e stimate 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 standa rd deviation equal to the inferred error percentage fo r the cell times the average gas use. Processes us ing little gas have small gas-use errors in kg terms, w hile processes that use large quantities of gas hav e large gas-use errors in kg terms. The estimated g as 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 i s turned into an estimate for total Etch F-Gas Emissi ons 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 ga s usage error in any one process is correlated with the errors in the other processes for that gas beca use the total kg usage error must be zero when summed across the cells to the facility level. Fro m this, emission errors induced in one process by e rrors in apportioning gas use are correlated with errors in the other processes. For example, if apportione d gas use in etch has erred on the high side in one M onte Carlo trial, this error will push etch emissio ns 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 analysi s 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, account ing for the net effect of the correlated errors.
As a prelude to the presentation of results in Sect ion 5, we note that the gas apportioning problem is complex because it depends on a number of factors t hat may defy easy intuition, but that the Monte Carlo takes full account of. Two general observati ons may be helpful in understanding the gas apportioning problem and its results.February 28, 2012

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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 th e cells. Once the shares are calculated and total gas use is allocated, the kg errors in one ce ll 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 i nput 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 pe rcentage 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-e tch processes than in etch, particularly in 200mm fabs. As a result, the apportioning errors i n non-etch have a much larger effect on overall emissions. If, hypothetically, it were pos sible to allocate gas use to etch without error, then the overall error would fall only by 10-20% (a ccording 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-etc h 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 deg ree of gas apportioning that is required to support the 3-Factor model.

3.5. Assessing the Accuracy of Alternative F-Gas Em issions Estimating Methods To assess the accuracy of the estimation methods re quires 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 me thodology. Because the Tier 3 methods utilize facility-specific emission factors, we have used th e Tier 3 emissions estimates for the three faciliti es as the reference point against which the estimates fro m the other methods can be compared to determine accuracy. This facility-specific comparison provid es a meaningful assessment of the relative accuracy of the alternative methods and the robustness of the i ndustry level factors. It is accompanied by a proforma error analysis, considering only the uncertai nties of the underlying emission factors, to provid e a benchmark for whether the observed differences in e missions are large or small.

3.6 Determining Emissions Error: Industry Level An alysis The emissions estimation method ultimately adopted by EPA in a final rule will be used by individual semiconductor facility to estimate the GHG emission s from a facility. EPA will then total the facilit yspecific emission estimates to estimate the GHG inv entory for the semiconductor industry as a whole. This report evaluates the several emission estimati on methods based, first, on their accuracy and precision for estimating the industry-level invento ry, and, second, on the precision achieved by the b est methods when used for estimating facility-specific emissions.
The performance of the methods for industry-level e stimates are evaluated here using composite information representing the twelve fabs who partic ipated 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. Th e twelve fabs include seven 200mm fabs and five 300mm fabs. The industry-level analysis uses the t otal gas use for the appropriate subset of theseFebruary 28, 2012

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twelve fabs as a representative surrogate for the i ndustry. The industry-level analysis also treats t hem in the composite by wafer size, rather than individ ually, as if they were a single fab that used the s ame F-Gas quantities of the relevant subset of the twel ve fabs. This approach ignores the fact that independent errors in the reporting of individual f abs would be partially attenuated when combined by EPA into an industry-wide inventory. Therefore, th e actual precision of industry inventories should b e better than the precision values estimated in the a nalysis presented in this report.
The aggregated gas use distribution for all of the 200mm fabs and all of the 300mm fabs was provided t o EPA in the earlier February 3, 2012 submittal.
Industry-level analyses have been conducted using b oth the error propagation and the Monte Carlo methods. As a first step, the accuracy and precisi on analysis conducted by SIA has compared the performance of four different estimation methods ba sed on the precision achieved in industry-level emission estimates based on the uncertainties of th e underlying emission factors. Error propagation methods have been used for this purpose, in which t he uncertainty of the underlying emission factors i s assessed using Eqs 3-1 and 3-2 and depends on the n umber 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 be st 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 Ca rlo method, considering both emission factor and gas apportioning errors. The result of this more d etailed and extensive analysis shows that the two S IA models perform similarly in terms of the precision of emission estimates, with the SIA 2-Factor model giving somewhat more precise estimates in some case s due to its lesser demands for emission factor data. The analysis shows no instance in which the SIA 3-Factor model performs substantially better th an the 2-Factor model and, therefore, would be preferr ed based on the precision of estimate.

3.7. Determining Emissions Error: Facility Level An alysis 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 spec ific emissions. The precision achieved by the SIA 2- Factor model at the industry level is determined by the precision of the underlying emission factors a nd by the effect of gas apportioning error based on th e overall distribution of gas usage and the difficu lty 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 imp ortance 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, t here will be more variation in the underlying emiss ion factors from fab to fab than there will be in the i ndustry average, leading to a lower level of precis ion than can be achieved. However, as shown in the Res ults section, the performance of the SIA 2-Factor model is very good at the facility level and, in ma ny cases will approach the level of performance achieved at the industry level.February 28, 2012

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Facility-level emission estimates are the product o f 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 av erage emission factor depends on the number of distinct t ool types and etch processes that are performed in the facility during a reporting period, which we wi ll denote as N F or Facility N. Each distinct tool type / process combination has a characteristic emission f actor that can be found somewhere in the overall distribution of emission factors for the gas in que stion that has been characterized by the mean and standard deviation of emission factor data in the S IA 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 statis tical treatment of the problem makes a hypothetical draw from the overall EF distribution.
Conceptually, the precision of an industry-level es timate depends on the number of EF data points and their standard deviation according to the usual for mula for the standard error:

Standard Error = σ EF / sqrt( N DB ) (Eq 3-1)

where σ EF is the standard deviation of the emission factor d istribution for tool type / process combinations found in the industry and N DB is the number of data points in the database. By similar reasoning, the precision of a facility-level estima te will depend on the number N F of emission factors needed to describe the N F distinct tool types and etch processes that are pe rformed in the facility:

Standard Error = σ EF / sqrt( N F ) (Eq 3-2)

If N F is small, then the facility-specific average EF ca n vary widely from the industry-average EF, but the facility-specific average EF must approach the indu stry-average as the number of distinct tool type / process combinations increases. An emissions estim ate based on industry-average EFs may be poor for a facility with only a few distinct tool type / pro cess combinations, but it will approach the precisi on of the industry-level estimate as the number of distin ct combinations increases. In practice, however, t he improvement in precision will be limited for any gi ven input gas or cell in the model by our knowledge of the industry-average EF. That is, it is the smalle r of N F and N DB 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 Pr ocess Combinations (Four Sample Fabs)

(Each Distinct Combination is Represented by One Di stinct EF)

Input Gas 200mm 300mm

Database N DB * Industry

Gas Use (% of kg) Fab B Fab C Database N DB * 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 3February 28, 2012

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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

* N DB values given are for primary emission factors.

To assess the precision achieved by the SIA 2-Facto r model at the facility level, two 200mm and two 300mm fabs agreed to count the number of distinct t ool types / etch process combinations using the same distinctions for tool type and etch process ma de 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. F or example, the sample 200mm fabs contain 22 to 23 dis tinct tool type / process combinations that use CF4, compared to the 126 combinations that are char acterized in the SIA database, while the sample 300mm fabs contain 11 to 16 combinations compared t o the 75 CF4 data points found in the database. The subsets of combinations present in the fabs giv e 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-le vel 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 differ ences compared to the industry-level analysis are t hat the facility level analysis recognizes that facilit y-specific average emission factors will vary aroun d the industry average and that the profile of gas usage will differ from the industry average. Both emissi on factor errors and gas apportioning errors are accou nted for in the analysis.

3.8. Determining Emissions Error: Including F-Gas A batement
Most of the analysis presented in this report exami nes estimates of F-Gas emissions on an unabated basis, since abatement varies by fab and does not i nfluence 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 occu rs primarily in the 300mm fabs. Each of the five 300mm fabs participating in the gas usage survey ab ates 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 ne eded 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 t he 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% f or 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 t he 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 fab s abates wafer cleaning gases at present, wafer cleaning is better viewed as an integral part of th e etch process (as SIA has explained in Section 3.2 .4) and, where abated, will be abated with the same equ ipment 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/

N 2 O
All Processes N 2 O 60% EPA rule a/

a/ SIA database does not contain information characte rizing 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, incl uding 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 c alendar 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 us age levels for 2009 and 2010.

4.1 Etch emissions factors
The data amounts (N) associated with the 2-Factor a nd 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 38February 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 21February 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 e mission factors calculated for the 2 and 3-Factor models. Full sets of emission factors are in Attac hment A. A blank data line indicates no data for t hat combination. Note that 200mm and 300mm manufacturi ng technologies will not use some of the gases in their etch processing steps.

Table 4-3: Input gas emission factors for the 2 fac tor 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 8700February 28, 2012

34 Table 4-4: Input gas emission factors for the 3 fac tor 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 facil ities and seven 200mm facilities are shown in Tables 5 an d 6. All units are in kg. Table 6 takes the Etch Usage category from Table 5 and breaks it down by princip al 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 3.3February 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 0.0 266.5February 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 0.0February 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 59.6February 28, 2012

38 5. Results
This section presents the results of SIA’s analysis of the several emission estimation methods. Secti on 5.1 compares the methods for accuracy of the emissi ons estimate, by comparison to the Tier 3 emission values for three fabs, and then for precision of es timate, considering the structure and data of the models and the uncertainties of the underlying emis sion factors. This is an industry-level analysis d one to determine the relative performance of the method s 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 eval uated in more depth to determine their performance when the effect of gas apportioning err ors are considered in addition to the uncertainties in emission factors. Section 5.2 presents the resu lts of this assessment. The SIA 2-Factor model is shown to perform as well or better than the more-co mplex SIA 3-Factor model. To demonstrate the 2- Factor model’s performance in a more realistic sett ing, the effect of abatement in 300mm fabs is also considered.
The performance of the SIA 2-Factor model in predic ting 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 th e calculation of emission reductions due to abatement.

5.1 Accuracy and Precision of IPCC, EPA, and SIA E stimation Methods
5.1.1 Accuracy of F-Gas Emission Estimates

Without direct measurements of emissions for indivi dual 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 twe lve fabs that participated in the ISMI/SIA survey h ave prepared Tier 3 emission estimates and agreed to sh are 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 es timates 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 fo r these three fabs. Facility-specific gas usage wa s estimated using best available monitoring methods. This typically involved applying heel factors to g as disbursements. When apportionment of gas usage to different process categories was required, apportioning was determined through use of engineer ing calculations and estimates.
The emission estimates are accompanied by a pro-for ma estimate of their expected errors (95% confidence interval) to provide a gauge for the sig nificance, if any, of the differences that are obse rvedFebruary 28, 2012

39

among the methods. The error analysis is based on the expected errors in the industry-average emission factors, as determined through conventiona l error propagation methods and described in Section 3.4. The expected error gives the uncertai nty in the emissions calculations due to uncertaint ies in the applicable emission factors. This is not th e same as a later analysis (termed the facility-lev el error analysis) where we estimate the degree to which an estimation method applied at the facility level wil l give a result that varies from the true facility-sp ecific value. No error analysis is included for th e Tier 3 estimates, which are used here as the standard for comparison.
Table 5-1 summarizes the estimates by the various m ethods for Etch Emissions and Total F-Gas Emissions. Across both years and both wafer sizes, the SIA 2-Factor model generally comes closer to t he Tier 3 values than the other three methods. Consid ering Etch Emissions, the IPCC Tier 2B, EPA Tier 2C , and SIA 3-Factor models give consistently lower est imates than Tier 3 for the one 200mm fab – approximately 23,000 to 24,000 mtCO 2 e per year for Etch (depending on the model) compar ed to 30,000 mtCO 2 e for Tier 3. The SIA 2-Factor model is much close r 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 ea ch of the years, but the advantage over the 2-Facto r 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 estima tes. Both SIA models compare favorably to the Tier 3 values for the 300mm fabs, with the SIA 3-Fa ctor model being slightly closer but with both bein g 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 ot her methods, including the SIA 3-Factor model. The advantage is clear and large compared to the IPCC T ier 2B and EPA Tier 2C models. The advantage is clear and large in comparison to the SIA 3-Factor m odel 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 thi s section, introduces additional sources of error.
While a wider range of results might be seen if thi s comparison could be made for more fabs, we believ e 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 Estimat es By Various Methods, Based on a Comparison to
Tier 3 Emission Estimates For One 200mm and Two 300 mm 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 (mtCO 2 e) 95%

Confidence Interval Emissions

(mtCO 2 e)

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 — 61,200February 28, 2012

41

5.1.2 Precision of F-Gas Emission Estimates by Met hod
Throughout the development of the SIA 2-Factor and 3-Factor models, SIA conducted error analysis based on error propagation techniques to obtain rap id 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. O nly uncertainties in the emission factors underlyin g the estimation methods are considered in this follo wing analysis. The error propagation methodology was described previously in Section 3.4.

The error propagation method has been used to estim ate the precision of industry-level emission estimates based on the four estimation methods cons idered here: IPCC Tier 2B, EPA Tier 2C, the SIA 2- Factor, and the SIA 3-Factor models. These estimat es aggregate gas usage data from the twelve fabs who participated in the SIA survey as representativ e of the 200mm and 300mm fabs industry-wide. The calculations treat the industry as if it were one l arge 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 ca lculations, the default emission factors establishe d in the MRR for non-etch processes for semiconductors h ave been used with the SIA 2-Factor and 3-Factor models for etch to complete the calculation of Tota l F-Gas emissions. The results of this analysis are summarized in Table 5-2. While many different comp arisons 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 inte rvals of ± 50% or worse. The increased precision comes about largely from the vastly incre ased size of the ISMI/SIA database, which permits emission factors to be estimated with bette r precision in almost all cells.

2. The SIA 2-Factor model consistently estimates highe r 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) compa red to all methods, while in 300mm, the differences are large only in comparison to the IPC C and EPA methods. Given the similar result in the Tier 3 comparisons, we believe that the high er estimate of the 2-Factor model is the better estimate. The two SIA models give similar e mission 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-Fact or model estimating substantially greater emissions in 200mm than the other methods and subst antially lower emissions in 300mm than the IPCC and EPA methods.
Given these results, and the preceding Tier 3 compa risons based on accuracy, we conclude that the SIA 2-Factor and 3-Factor models clearly improve upon t he earlier IPCC Tier 2B and EPA Tier 2C methods because they appear to be more accuracy and they cl early have better precision. The improvement is due to the large expansion of the emission factor d atabase and, we believe, to the introduction of the wafer size distinction, which captures the signific ant evolution of etch technology toward lower GHGFebruary 28, 2012

42

emissions in 300mm fabs. There is no evidence in t hese results to delineate either the SIA 2-Factor o r 3- Factor model as having the better overall precision of estimate in percentage terms, although the Tier 3 comparisons in the earlier previous section indicat e that the 2-Factor has better overall accuracy.

Table 5-2: Precision of Etch F-Gas Emission Estima tes Based on Emission Factor Expected Errors: Industry -Level Analysis (Error Propagation Method, Not Considering Gas Appo rtioning Error or Abatement)

Year Estimation Method Etch F-Gas Emissions Total F-Gas Emissions(Etch + NonEtch)

Etch Emissions (mtCO 2 e) Error

Standard Deviation (mtCO 2 e) 95%

Confidence Interval (Percent) Total

Emissions (mtCO 2 e)

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% 5 70,400 2009 EPA Tier 2C (2 factor) 374,200 63,200 ± 62% 5 51,800 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% 6 86,300 2010 SIA 2-Factor 297,900 10,900 ± 7% 546,300 2010 SIA 3-Factor 285,200 9,700 ± 7% 533,600February 28, 2012

43

Based on these findings, SIA narrowed the estimatio n methods to include only the SIA 2-Factor and 3- Factor models. Further analysis was conducted usin g Monte Carlo methods to provide a broader and more detailed assessment of precision, including co ntributions from both emission factor errors and ga s 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 e ffect of emission factor errors using the Monte Car lo 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-Fa ctor 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-fac tor models for etch are combined with the EPA default emission factors for non-etch processes. I n this, gas apportioning errors are not considered, and the EPA default EFs for non-etch processes are trea ted as having no errors (the uncertainties are unknown to us). The errors shown for Total F-Gas E missions (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 so mewhat wider range for 200mm, although the absolute errors (mtCO2e basis) in 300mm fabs are on ly one-half to one-third as large due to their lowe r overall level of etch emissions. These estimates d o not account for the effect of F-Gas abatement in 300mm fabs, which will further reduce Etch and Tota l 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 inter vals 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 small er part of total emissions for 200mm fabs (30-40%) tha n for 300mm fabs (55-60%) due to the replacement of C2F6 with NF3 as the primary chamber clean gas i n 300 mm technologies. Therefore, the somewhat larger percentage errors for etch in 200mm are atte nuated more when non-etch emissions are added in, leading to the result that all of the cases for 200 mm 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 est imates 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 i n 300mm fabs on a Total Emissions basis. This compar ison does not say which model is more accurate on an industry level, but it shows clearly that any di fference in accuracy between the models is very sma ll and that the 2-Factor is a more conservative approa ch because it estimates somewhat higher emissions. Considering the precision of emissions estimates, t he 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- Facto r models are nearly the same supports our conclusio n, drawn during the emission factor analysis, that dif ferences in emission factors are largely driven by the properties of the gas and are relatively independen t 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 (mtCO 2 e) Error

Standard Deviation (mtCO 2 e) 95%

Confidence Interval (Percent) Total

Emissions (mtCO 2 e) 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 ru ns counter to our initial assessment that the subdi vision of the SIA EF database into film types would signif icantly degrade the precision of the 3-Factor model estimates. A small degradation can be seen in 200m m, 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 e ffects on precision:

1. The 3-Factor model was shown to have modestly great er explanatory power (ΔR 2 ~ 3%). Although there are only 4 cells where a statistical ly significant difference exists due to principle film type, the use of independent EFs by film type will reduce the overall error in those cells andFebruary 28, 2012

45

improve the precision of the 3-Factor model. While the R 2 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 oc cur for gases that are important to emissions.

2. Offsetting this, the sample size N is diluted in al l cells by dividing the database into film types, and the reduction in sample sizes necessarily degra des 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 300 mm 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 t otal 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 1 1-21 data points for the major gas-use film types in the 3-Factor model. It would be a differe nt 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 mod el and have been accounted for in this analysis. W e find that they approximately offset each other, lea ding 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-Fa ctor and 3-Factor Models
In the 2-Factor model, gas use must be allocated am ong the five EPA processes (etch and four wafer and chamber cleaning processes), while much more apport ioning work must be done in the 3-Factor model to the film-type level in etch. The gas apportioni ng problem is complex because the emissions effect of gas apportioning errors depends upon the difference s among the EFs by process and the difficulty of th e gas allocation problem by gas. If the EFs do not d iffer appreciably by process or film-type, then err ors in allocating gas use will have little consequence for emissions. No allocation error will be present fo r gases used only in one process and little allocatio n error will result if only small portions of gas u se are allocated to processes other than the dominant one. Finally, the overall impact of apportioning error s will depend on each gas’s GWP value and its contrib ution to Total Emissions on an mtCO 2 e 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 fa ctors themselves are not counted, so that the resul ts reflect only the effect of gas apportioning errors. The results reported for the errors in Etch Emiss ions are magnified because they reflect only the apporti oning errors that occur in Etch and not the correla ted and partially offsetting errors in Non-Etch emissio ns. The reported results for Total F-Gas Emissions properly characterize the net effect of gas apporti oning 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 3 00mm Fabs)

Etch F-Gas Emissions Total F-Gas Emissions (Etch + Non-Etch)

Wafer Size Year Etch

Emissions (mtCO 2 e) Error

Standard Deviation (mtCO 2 e) 95%

Confidence Interval (Percent) Total

Emissions (mtCO 2 e) 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% t o +25%

Looking first at the SIA 2-Factor model, we see tha t gas apportioning error has a much larger effect i n 300mm fabs than in 200mm fabs. Many factors contri bute 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 re lated to apportioning to show the following:

• In 200mm, six different gases must be allocated, an d emissions generation is distributed among a number of gases. The NF3 allocation problem is s till the most difficult, but NF3 is only one-half as important to GWP emissions as in 300mm. While n ot all of the apportioning errors shown in the table are independent (due to byproduct emissio ns), there are more gases over which independent errors can be combined and attenuated t o reach the total.

• In 300mm, emissions generation and the gas allocati on problem is almost entirely related to NF3, which is 87% of GWP-weighted gas use and the m ost difficult gas to allocate because it is spread across all five processes. Only the small i ndependent 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-Fac tor model, which differs only in regard to the grea ter effort required to apportion gas use to the film le vel. 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 error s across the different gases brings the overall effect down to no more than -5% to +6% (95% confide nce range).

• The effect is larger in 300mm, where almost the ent ire allocation problem (and the largest part of emissions generation) is concentrated in NF3. T here is much less allocation work to be done, but where it is necessary (NF3) the allocation prob lem is difficult and the result very important to emissions.
These results are related to the general patterns o f 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%. D ifferent choices for these inputs would lead to numerically different results for the errors, but w e believe that the basic conclusions should be simi lar.
Looking next at the SIA 3-Factor model, we see very little difference in the effect of gas apportionin g in comparison to the SIA 2-Factor model. Although the re 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 minimi ze the incremental effect of apportioning error on etc h.
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 aver age or below average for the two gases (CF4 and SF6) that have the greatest impact on emis sions.

• 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 th e incremental impact of apportioning error on Etch Em issions and are the primary reason that gas apportioning has minimal incremental impact on Etch Emissions in 300mm. This result is consistent wit h 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 i n the SIA 2-Factor Model

Based on Average Gas Use 2009-2010 (Not all emission errors shown are independent erro rs 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% 72,234February 28, 2012

49 Table 5-6. Impact of Film Type Emission Factors on the Emission Consequences of Gas Apportioning Error s

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 th at cell.February 28, 2012

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A second aspect is related to the absence of emissi on factors for some cells, which are assigned the default value 0.50 in the analysis and flagged in t he table using red font. The gases with the larges t impact on GWP-weighted emissions are well represent ed in the database, with only one film-type cell containing no data. Among the other gases, with le sser impacts on GWP-weighted emissions, there is one cell in 200mm where the EF standard deviation i s zero because all EFs are given the value 0.50, an d 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 ne gate a part of the apportioning error because the EFs are the same.
For these reasons, there is no appreciable differen ce between the SIA 2-Factor and 3-Factor models in the emissions consequences of gas apportioning erro rs, whether in terms of Etch Emissions or Total FGas Emissions. However, as SIA has documented prev iously, a much greater effort must be made to apportion input gases to the film type level to app ly the 3-Factor model, and the 3-Factor model raise s major CBI concerns as well. As will be seen throug hout this section, there is no reporting benefit of the 3-Factor model – whether in terms of accuracy or pr ecision of estimate – that supports its adoption in place of the 2-Factor model, and certainly no benef it that could outweigh its substantial cost and CBI burden.

5.2.3 Combined Effect of Emissions Factor Error an d Gas Apportioning Error on the SIA 2-Factor and 3- Factor Models
The performance of the models for reporting purpose s 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 comb ined effect of both errors sources on the SIA 2- an d 3-Factor models.
Table 5-7 summarizes the effect of combining gas ap portioning and emission factor errors on F-Gas emissions estimates, using the SIA 2-Factor and 3-F actor models for etch. When apportioning and emission factor errors are combined, we see that th e two models perform nearly the same for estimating Etch Emissions, with the SIA 2-Factor mo del having a slight advantage over the 3-Factor model in each case. The precision for Etch Emissio ns estimates is ± 30% or smaller (95% confidence interval) in each case, while the absolute errors a re smaller in 300mm due to the overall lower F-Gas emissions for these fabs. As will be seen later, t he use of abatement in 300mm further reduces this

error.

Similar conclusions can be drawn for Total F-Gas Em issions, where the SIA 2-Factor model has a slight advantage over the 3-Factor model in 200mm and a sl ight 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% f or 200mm and ± 24-26% for 300mm. It is the difference in gas apportioning errors that causes t he precision to be better in 200mm, compared to 300mm, since there is little difference in the effe ct of emission factor errors. Although the relativ e (percentage) errors differ, the absolute (mtCO 2 e) 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 Facto r Errors (Monte Carlo Method, Not Considering Abatement)

Etch F-Gas Emissions Total F-Gas Emissions (Etch + Non-Etch)

Wafer Size Year Etch

Emissions (mtCO 2 e) Error

Standard Deviation (mtCO 2 e) 95% Confidence

Interval (Percent) Total

Emissions (mtCO 2 e) 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% t o +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% t o +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 ga s apportioning error between the two models. When both errors are considered together, the 2-Factor m odel 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 Consider ing 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 compariso ns 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 fa bs. To show this effect, the five 300mm fabs participating in the gas usage survey provided the information needed to estimate the reduction inFebruary 28, 2012

52

emissions due to abatement. Because abatement is u ncommon in 200mm fabs, we do not account for it in the analysis presented below. We look only at t he SIA-2 Factor model because abatement will not change the relative performance of the 2- and 3-Fac tor 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 t he 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. Th e default DREs are: 60% for CF4 used in etch; 99% for NF3 and other gases used in etch; 90% for NF3 u sed in any of the chamber cleaning processes; and 60% for CF4 and any other gas used in chamber clean ing.

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 F actor Errors

With Abatement in 300mm Fabs

Etch F-Gas Emissions Total F-Gas Emissions (Etch + Non-Etch)

Wafer Size Year Etch

Emissions (mtCO 2 e) Error

Standard Deviation (mtCO 2 e) 95% Confidence

Interval (Percent) Total

Emissions (mtCO 2 e) 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 -1 6% to +19%

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

Table 5-8 summarizes the expected performance of th e SIA 2-Factor Model when F-Gas abatement in 300mm fabs is considered. All of the 300mm fabs ab ate NF3, three of the fabs also abate CF4 and SF6, and one of the fabs abates all gases. Abatement re duces 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 i mproves the precision of emission estimates in 300mm. Absolute (mtCO2e) emission errors fall to l ess than one-half those in 200mm, while the relativ e 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 interva l) 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 ear lier IPCC and EPA methods. This is true for both E tch 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 Facili ty-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 fro m fab to fab, leading to differences in the sources of em issions and in the importance of apportioning error s for each gas. More importantly, however, the preci sion of the emission factors underlying facility estimates will differ from that achieved at the ind ustry level. In general, there will be more variat ion in the true average emission factor from fab to fab th an there will be in the industry average, leading t o a lower level of precision that can be achieved. How ever, as shown in this section, the performance of the SIA 2-Factor model is very good at the facility lev el and, in many cases, will approach the level of performance achieved at the industry level. As ex plained in Section 3.7, the precision of a facility

specific average emission factor depends on the num ber of distinct tool types and etch processes that are used in the facility during a reporting period (denoted as N F or Facility N).
To assess the precision achieved by the SIA 2-Facto r model at the facility level, two 200mm and two 300mm fabs agreed to count the number of distinct t ool types / etch process combinations using the same distinctions for tool type and etch process ma de in the SIA database (documented in Section 3.6). Although each fab has fewer combinations than in th e 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 ma jor gases.
Table 5-9 summarizes the expected performance of th e SIA 2-Factor Model for the four sample fabs in comparison to the industry-level performance that w as 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 clos e to, but not as good as, the industry-level precis ion. 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 300 mm, 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 facto r 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-boardFebruary 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 Emissi on 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% Confidence Interval (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 (mtCO 2 e) in its emissions estimates by about the same percentage.

We find it remarkable how little difference there i s in the precision achieved by the SIA 2-Factor mod el at the industry and the facility levels. This is partly because there is a tendency to have more too l type / process combinations for the gases used in larger q uantities (and are most important to emissions), bu t we believe that the role played by a few cells in t he database with very few data points (often zero o r one) is also important. The precision of the 2-Fac tor 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 analy sis. 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 data base. These cells have poor precision in an indust rylevel analysis, and their precision level will not be changed at all in the facility-level analysis, i f N F is greater than N DB . They will have large influence on the overall pr ecision 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 le ss in other cases. For 200mm Fabs B and C, the precis ion of facility-level estimates is approximately ± 10% or less (95% confidence). When abatement is accoun ted 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 c ompleted in midyear 2012 and the data points added to the database. We see these results as demonstra ting 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 D atabase

The current ISMI etch emissions characterization da tabase contains data from all four of the major equipment suppliers’ toolsets. Using the current I SMI database, 2-factor model emission factors can b e established for 99.7% of industry F-Gas usage. To further strengthen the representativeness of this database, a semiconductor manufacturer and key equi pment suppliers have committed to conduct additional etch emissions testing. The additional d ata will covers 8 supplier/gas use combinations fou nd in 200mm fabs and 7 supplier/gas use combinations f ound in 300mm for which the ISMI database does not currently contain data. This will improve the s upplier gas/use coverage of the database and reduce the emissions factor uncertainty in several wafer s ize/gas use cells. Testing will be completed in the first half of 2012 and these data will be shared wi th EPA.

###February 28, 2012

56 

## Attachments

A. Changes Since the Presentation of Results in Novemb er 2011

Several corrections were made to the calculations a fter 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 calcul ations, both for etch emissions and other emissions. It is now included.

• Lower and upper emission factors were calculated us ing 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 orig inal data had missing gas usage. This has been corrected.

• There were two typos in the use of the byproduct em ission factors for Wafer cleaning and CVD. These have been corrected. The total F-Gas use wen t up with these corrections. This means that the percentage that is etch and etch byproduct s 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 wo rst case error, the extension of the gas propagatio n error analysis to the 2-Factor model and its impact on the estimation of emissions for non-Etch source s, a different approach to assumptions for data cells that are empty, and the quantification of the facil ity level N for determining the precision of the 2-Fact or 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 em ission factors for the 2-Factor and the 3-Factor models. Note that for a particular wafer diamete r 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 t o 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 0.020677February 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 0.6587February 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 300February 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 0.022517811February 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 0.017562538February 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 0.0014February 28, 2012

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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 0.0086176 8 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 AnalysisFebruary 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 th ose 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 6,470February 28, 2012

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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 26,545February 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 0February 28, 2012

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E. F-Gas Abatement in 300mm Fabs

Table E-1: Fraction of Gas Use Subject to Abatemen t

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 0.000February 28, 2012

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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 0.000February 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 co rrected slides are available as a separate document

. Its title page is given below.

Accelerating Sustainable Manufacturing

Copyright ©2010 Advanced Materials Research Center, AMRC, Internati onal SEMATECH Manufacturing Initiative, and ISMI ar e servicemarks of SEMATECH, and the SEMATECH logo a re registered servicemarks of SEMATECH, Inc. All other servicemarks and trademar ks 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) Privileged and Confidential For Settlement Purposes Only