Technical Support for 

Modifications to the 

Fluorinated Greenhouse Gas 

Emission Estimation Method 

Option for Semiconductor 

Facilities under Subpart I

Office of Air and Radiation, U.S. Environmental Protection Agency

August 2012
August 2012 Subpart I Default Emission Factor Method Technical Support 

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1. Introduction

This document presents technical support for the proposed changes to the methods for estimating 

fluorinated greenhouse gas (F-GHG) emissions from the plasma etching, chamber cleaning, and wafer 

cleaning process types at semiconductor manufacturing facilities under subpart I, Electronics 

Manufacturing, of the Greenhouse Gas Reporting Program (GHGRP). It also describes proposed changes 

to the provisions for verifying gas apportioning models for all electronics facilities and calculating 

abatement system uptime when default emission factors (EFs) are used to estimate GHG emissions for 

all electronics facilities.

1

This document is based on information from Technical Support Document for Process Emissions from 

Electronics Manufacture (e.g., Micro-electro-mechanical systems, liquid crystal displays, photovoltaics, 

and semiconductors): Proposed Rule for Mandatory Reporting of Greenhouse Gases, Revised – November 

2010 (hereafter, the November 2010 TSD, available in docket EPA-HQ-OAR-2009-0927) and nonconfidential materials submitted to the EPA by the Semiconductor Industry Association (SIA).

In addition to the methods described in this document to estimate F-GHG emissions, the EPA is 

proposing to allow semiconductor manufacturers (and other electronics manufacturers) the option to 

estimate F-GHG emissions using a periodic stack testing option. For more l information about the stack 

testing option, please refer to Technical Support for the Stack Test Option for Estimating Fluorinated 

Greenhouse Gas Emissions from Electronics Manufacturing Facilities under Subpart I.

2. Estimating F-GHG Emissions from Semiconductor Fabs Manufacturing Wafers 300 mm or Smaller

The EPA is proposing to modify the methods by which facilities are required to estimate F-GHG 

emissions resulting from plasma etching, chamber cleaning, and wafer cleaning processes used at 

semiconductor manufacturing facilities, in particular those that manufacture wafers with diameters 

measuring 300 mm or smaller (i.e., 200 mm and 150 mm). The EPA is proposing to remove the

provisions that require the use of the “Tier 2d method” for the largest semiconductor manufacturing 

facilities2, which requires:

i. The use of default EFs to estimate F-GHG emissions resulting from chamber cleaning and wafer 

cleaning processes, and 

ii. The use of recipe-specific EFs to estimate F-GHG emissions resulting from etching processes.

The Tier 2d method focuses on recipe-specific EFs for plasma etching processes because of the gaps in 

the available EF data for the etching process type used in the industry at the time subpart I was finalized 

in December 2010.3

Since then, SIA has undertaken an effort to gather and collect a much larger data set of etch EFs from 

semiconductor manufacturing facilities and tool manufacturers (see Section 2.1) (SIA, 2012a and SIA, 

 

1

The term emission factor for the electronics manufacturing industry refers to either or both factors associated with the direct 

utilization of an input F-GHG or with by-product emission formation resulting from the use one or more input F-GHGs. 

Throughout the document it is indicated if the term emission factor is being used only to refer to one of these types specifically.

2

Large semiconductor manufacturing facilities are classified as those with production capacities greater than 10,500 m2of 

silicon substrate per year. 

3

For more details and information about the Tier 2d method refer to the November 2010 TSD.
August 2012 Subpart I Default Emission Factor Method Technical Support 

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2012b). In addition, SIA raised concerns with the burden, technical feasibility, and the protection of 

confidential business information (CBI) associated with the Tier 2d method (and other methods based 

on recipe-specific EFs). Please refer to the reference SIA, 2012a for a detailed discussion about SIA’s 

burden, feasibility, and CBI concerns. Because of these concerns, and after evaluation and consideration

of alternative methods and additional data submitted to the EPA (summarized below), the EPA is

proposing to remove the Tier 2d method from subpart I.

With this change, the EPA is proposing to remove the distinction between large and other 

semiconductor manufacturing facilities, such that all semiconductor manufacturing facilities subject to 

the GHGRP and opting to not use a periodic stack testing method would be required to use F-GHG 

estimation methods based entirely on default EFs (i.e., requirements would be independent of facility 

size). In the December 2010 subpart I, the EPA had required only the largest semiconductor 

manufacturing facilities to use the Tier 2d method to ensure that burden was commensurate with a 

facility’s potential emissions (November 2010 TSD). In proposing to use default EFs to estimate 

emissions from all process types, the burden on larger facilities would be reduced significantly, and the 

need to distinguish between “large” and “other” semiconductor manufacturing facilities would no 

longer be required. For more information specifically on the reduced economic burden from the 

proposed removal of the recipe-specific option, please see Economic Impact Analysis for the Mandatory 

Reporting of Greenhouse Gas Emissions F Gases: Subpart I.

Due to the aforementioned concerns voiced by SIA about recipe-specific EFs, the EPA is also proposing 

to remove the provisions that would allow all electronics manufacturing facilities (regardless of the type 

of electronic product manufactured or size of the facility) the option to use recipe-specific EFs to 

estimate emissions from all F-GHG emitting processes (etching, chamber cleaning, and wafer cleaning) 

and N2O-using processes (chemical vapor deposition processes and other N2O-using processes). This is

also referred to as a “Tier 3 method” (See the document entitled Technical Support for Other Technical 

Issues Addressed in Revisions to Subpart I for more information on estimating N2O emissions).

2.1. Developing Revised Default Etch Emission Factors

In previous subpart I rulemaking efforts the EPA has repeatedly evaluated approaches using default EFs 

for process types and subtypes4. In general, methods based on default EFs are relatively easy to use 

(particularly if the number of process types and/or subtypes is limited), but they may result in estimates 

with high uncertainties if the EFs are not representative at both the industry and fab level. Default EFs

can fail to represent industry-wide EFs if they are based on too few process measurements or on 

process measurements that are not representative of the industry as a whole. Default EFs can also fail to 

represent fab-level EFs if actual EFs vary significantly across fabs, and if the default EFs do not capture 

that variability. 

Uncertainties resulting from few or unrepresentative measurements can be addressed by increasing the 

number of measurements and/or their representativeness of the industry; uncertainties resulting from 

the failure of the default EFs to capture variability can be addressed by developing factors for additional 

 

4

For more information on the methods evaluated by the EPA and the data sources for EF development available previously,

refer to the November 2010 TSD.
August 2012 Subpart I Default Emission Factor Method Technical Support 

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and/or different categories. Note, however, that the addition of categories (e.g., increasing the number 

of process types or sub-types) can introduce other uncertainties, e.g., by decreasing the number of 

measurements per category or requiring estimates of gas consumption for more categories. 

In the fall of 2011, as part of the process of reconsidering the Tier 2d method, SIA provided the EPA with 

a data set of etch EFs that was much larger than the data set available to the EPA in 2010. The number 

of EF data points available to the EPA in 2010 and 2011 is shown in Table 1.

Table 1. Comparison of Available EF (Utilization) Data

Number of EF Data Points Available to the 

EPA in 2010

Number of Data Points Available to the

EPA in 2011

Etch 96 976

Chamber Clean 163 163

Wafer Clean 5 5

Total 264 1144

Note: The counts represent EFs related to utilization of an input gas. Counts of by-product EFs are not 

represented.

SIA stated that the etch data provided to the EPA in 2011 are considered representative of the industry 

as a whole because of the large number of data points, the presence of a measured EF for almost every 

gas, and the inclusion of data from the four primary suppliers of etch equipment in the data set. 

However, the EPA notes that it is not possible to assess fully the representativeness of the data in terms 

of the parameters that potentially affect EFs. Process parameters such as radio frequency (RF) power,

pressure, feature types etched (e.g., gate etch, trench etch, etc...), and stabilization time were generally 

not available (as in the case for some data that were considered to be confidential) or were not provided 

to the EPA in sufficient quantities to assess the impact of such parameters on EFs (SIA, 2011a). Also, the 

sources of the data (i.e., the device and tool manufacturing entities), which affect its representativeness, 

were not known. Nevertheless, the expanded etch data set of EFs was the largest ever assembled for 

this industry.5In light of the new etch data, the EPA concluded that revised default etch EFs could be 

calculated and proposed in place of the etch recipe-specific EFs required as part of the Tier 2d method. 

2.1.1. Options for Emission Factor Development Methods and Conventions 

As a first step in developing a method to replace the Tier 2d method, the EPA evaluated the following 

methods and conventions for developing the default EFs proposed in the revised subpart I:

 Utilization EFs (kilograms of gas emitted/kg of gas used): The EPA calculated the simple arithmetic 

average of all available etch data grouped according to two different EF models (see Section 2.1.2). 

 By-product EFs: The EPA considered two conventions in assigning by-product emissions to etch 

process input F-GHGs for multi-gas processes:6

 

5

SIA, 2012b contains the etch emissions data from SIA submitted in 2011, and Draft EFs for Refined Semiconductor 

Manufacturing Process Categories, and contains etch, chamber clean and wafer clean emissions data the EPA had in 2010. 

(EPA-HQ-OAR-2009-0927-0073)

6

This is most relevant in the context of etch EFs as these are multi-gas processes. Chamber clean processes mainly rely on one 

input gas (e.g., NF3 or C2F6).
August 2012 Subpart I Default Emission Factor Method Technical Support 

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o Dominant gas convention (kg of by-product emitted/kg of dominant gas used): This convention, 

which was adopted in the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines, 

assigns all by-product emissions to the dominant gas used in a process, or the gas with the 

largest mass flow for multi-gas processes. This convention is what was used in the December 

2010 subpart I.

o All input F-GHGs convention (kg of by-product emitted/kg of all input gases used): This 

convention assigns by-product emissions to input F-GHGs used in a process by dividing the 

measured mass emitted of a specific by-product by the total mass of all input F-GHGs and 

assigning this by-product factor to each input F-GHG. This convention was used by SIA when 

compiling the newly available etch EF data (SIA, 2012b).

It is generally not possible to ascertain which input gases specifically give rise to a particular by-product 

for multi-gas processes, and both methods are only conventions that have some drawbacks. In 

particular, while the first convention could lead to an over-assignment of by-product emissions to a 

dominant input gas, the second could lead to assignment of by-product emissions to input gases that do 

not chemically generate a particular by-product (e.g., CF4 is generally not a by-product from the use of 

the input etch gas NF3, unless carbon-containing films are being etched). However, because both 

conventions conserve mass (i.e. theoretically the same amount of by-products will be calculated by both 

methods for a particular recipe when using default EFs), and given that the etch data available are in the 

format to support the “all input F-GHGs convention,” the EPA is proposing to use the second (all input 

gas) convention for etch EFs. Note that data were not available to the EPA to revise the chamber 

cleaning default EFs to follow the all input F-GHGs convention, but this is not expected to be a 

substantive issue at this time because the majority of the chamber clean processes for which data is 

available rely on only one input gas. Regardless of the convention used, the equations used to calculate 

by-product emissions are the same. The only difference in the conventions is how by-product emissions 

are attributed to input gases.

After selection of the all input gas convention, two averaging methods were considered for developing 

the by-product EFs:

1. Simple arithmetic mean of all available by-product EF data: This method averages all available 

by-product EF data (by by-product) for each gas, wafer size, process type/sub-type combination. 

2. Simple arithmetic mean of all available by-product EF data, including use of zeros when 

measurements did not detect by-product emissions: This method averages all available byproduct EF data (by by-product) including measurements that did not detect by-product 

emissions for each gas, wafer size, process type/sub-type combination (represented by a zero

value). For more information on this approach, please refer to Draft EFs for Refined 

Semiconductor Manufacturing Process Categories (EPA-HQ-OAR-2009-0927-0073).7

The EPA compared the resulting by-product EFs from using both of these averaging methods. It was 

found that the number of zeros that would have to be included in the by-product EF averages (i.e., the 

 

7

SIA also analyzed a third method, which would only take into account by-product EFs associated with single input 

gas processes. This method is presented in SIA, 2012a. The EPA did not analyze this method because it did not take 

into account physical/chemical reactions that impact by-product emissions for multi-gas processes.
August 2012 Subpart I Default Emission Factor Method Technical Support 

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number of utilization EFs with no reported measured by-product EF) ranged from 1 to over one 

hundred, depending on the grouping used (see Section 2.1.2). Including the number of zeros towards 

the higher side of this range will have a substantial impact on the calculated average by-product EFs. The 

EPA’s comparison showed that including versus not including the zeros for cases where no detected byproduct was reported resulted in a 38 to 45 percent difference on average for by-product EFs, 

depending on the grouping used (see Section 2.1.2 for grouping definitions).

The EPA questioned SIA about the method for including the zero values for non-detected by-products, 

and in SIA’s Report to EPA on Etch Factor Proposal for Fab GHG Emissions Reporting, SIA states the 

following “...For example, take the case of ten separate tests conducted on an input gas X. If nine of 

these tests produce no detectable by-product of gas Y and one does, then the current method 

establishes the by-product EF based on the one test of the by-product Y from gas X and does not take 

into consideration the measurements where by-products are not detected. SIA makes no specific 

recommendation on this matter.” Because the EPA was not certain whether zeros indicated that 

particular by-products were not looked for or whether they were looked for but not detected, the EPA is 

conservatively proposing by-product EFs that do not include zeros. However the EPA’s approach in the 

December 2010 subpart I (including the zeros) may be considered further; not accounting for the zeros 

in the by-product EF development may result in overstating by-product emissions. Therefore, the EPA is 

seeking comment on the method for averaging data to develop by-product EFs in the preamble to the

proposed rule.

2.1.2. Grouping of Etch Emission Factor Data to Develop Default Emission Factors

The second step in developing the revised default etch EFs was to evaluate how individual EF data 

should be grouped to develop average, default EFs. This was evaluated by the EPA and SIA in looking at 

EF data associated with the utilization of input gases used for etch and available information for 

parameters known to impact EFs. The parameters for which the EPA had sufficient information on which 

to make conclusive decisions about their effect on etch EFs were:

 Input gas

 Wafer size

 Film type etched (oxide, nitride, silicon, or metal)

Note that for 141 etch utilization EFs, or approximately 14 percent of all EF data, the EPA had 

information on the type of feature etched. The EPA analyzed this parameter to the extent possible and 

the results indicated the feature type etched was a parameter that had a potentially significant influence 

on EFs, but, as identified above, feature type information was quite limited. It should also be noted that 

SIA did a full assessment of the parameters that had the most impact on EFs, which the EPA was not 

able to review due to confidentiality concerns. SIA’s analysis and report concluded that the three 

parameters listed above were the most relevant for grouping etch EFs (SIA, 2011a and SIA, 2012a). 

SIA also analyzed the best way to group the etch EF data according to the three parameters. SIA’s 

analysis resulted in the conclusion that the best two models for grouping EFs were: 

 Model 1: Develop default EFs by gas and wafer size for one broad etching process type, and 
August 2012 Subpart I Default Emission Factor Method Technical Support 

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 Model 2: Develop default EFs by gas and wafer size for four etching process sub-types based on 

the types of films being etched (silicon etch, nitride etch, oxide etch, and metal etch).

8

To confirm SIA’s results the EPA used two statistical approaches in analyzing the most meaningful way to 

group etch EF data to develop default EFs taking into account the above listed parameters known to 

impact EFs (input gas, wafer size, and film type).:

First the EPA ran a “best fit analysis” (i.e., an analysis of R-squared values9, assuming normal 

distributions10). This analysis indicated that two well-fitting models for developing etch EFs would 

indeed be Models 1 and 2 as described above. The EPA’s analysis showed that:

1. Input gas and wafer size (Model 1) provided an R-squared value of 58.6 percent, or 

2. Input gas and wafer size and film type (Model 2) provided an R-squared value of 64 percent.11

The EPA then performed an analysis of variance (ANOVA) and looked at the p-values for the different 

models in order to confirm that the difference in R-squared values for the two models was statistically 

significant, that is, that Model 2 was really providing a better fitting model. The results showed a slight 

reduction in uncertainty for Model 2 compared to Model 1, also consistent with SIA’s analysis (SIA, 

2012a). SIA analyzed other parameters such as pressure, but this information is considered confidential 

and was therefore not used in the EPA’s analyses. See SIA, 2012a for more information. 

As another tool to assess the two models considered to group EFs, the EPA compared the uncertainties 

in the distributions of the GWP-weighted EFs for each etch input gas (including utilization and byproducts EFs), according to the groupings described above for Model 1 and Model 2, and using the 

conventions and averaging methods described in Section 2.1.1. For each GWP-weighted EF, the 

uncertainty at the 95 percent coverage level was calculated by multiplying the standard deviation of the 

factor by 1.96 and dividing by the mean. For Model 1, the uncertainty in EFs simply results from the 

deviation of the individual EFs around the mean (default) factor, so that, using the fitted distribution, 95 

percent of individual EFs are estimated to deviate from the mean by no more than the uncertainty 

percentage. For Model 2 (and for the purpose of comparing Model 2 to Model 1), the uncertainty was 

calculated for each film type etched, then averaged over the four film types. For the EFs for which it was 

not possible to calculate an uncertainty value because only one data point in the database existed, a 

standard deviation equal to half the value of the data point (and thus an uncertainty equal to 98

percent) was assumed. The results of this analysis are presented in Figure 1 (200 mm) and Figure 2 (300

mm) below.

 

8

In SIA documents the first model is referred to as the “two-factor model”, and the second model is referred to as the “threefactor model”.

9

An R-Squared value represents the squared correlation coefficient between the measured EF and the predicted EF. In this case 

the predicted value is the mean EF for the corresponding combination of input gas, wafer size, and, for Model 2, film type, Thus 

the R squared measures the fit of the best straight line through the pairs of measured and predicted EFs.

10 The EPA analyzed various potential distributions for fitting model parameters, including normal and log normal. No consistent

preference (fit) for a distribution was found, therefore a normal distribution was assumed in all of the EPA’s analyses. This is 

consistent with SIA’s analysis of distributions (SIA, 2012a). 

11 The EPA’s R-squared analysis looked at both the reported EFs and the natural log of the reported EFs. Results using the log of 

EFs also indicated a better R squared value for the Model 2.
August 2012 Subpart I Default Emission Factor Method Technical Support 

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As can be seen by comparing the etch EF uncertainties for Models 1 and 2, Model 2, which takes into 

account the film type etched (metal, nitride, oxide, and silicon), does not always provide better certainty 

in the EFs than the less refined Model 1 which uses one broad etching process type. For example, the 

average uncertainty of Model 2 for CF4 (200 mm processes, first data point on the left of Figure 1) was 

worse than Model 1 (69 percent vs. 54 percent). However, the opposite trend is observed for 300 mm, 

where the uncertainty of the CF4 EF for Model 2 was better than for Model 1 (46 percent versus 55

percent). On average (over all gases and both wafer sizes) the EF uncertainty for Model 1 is slightly 

higher than for Model 2 (97 percent versus 93 percent). However, opposite trends are actually observed 

as a function of wafer size: for 200 mm, Model 1 provides slightly lower uncertainty than Model 2 (92

percent versus 95 percent respectively); For 300 mm, Model 1 provides slightly higher uncertainty than 

Model 2 (101 percent versus 92 percent respectively). 

In many cases, the higher uncertainties in the Model 2 EFs are a result of spreading the data among 

more categories than in Model 1, lowering the number of data points in each category. Several 

gas/wafer/film-type categories ended up with one data point because for most gas/wafer size 

combinations, one film type tends to dominate others in terms of the amount of available EF data. As 

discussed above, an uncertainty of approximately 98 percent was assumed in cases where there was 

only one EF for a category. (The uncertainty values for Model 2 for the cases with more than 1 data 

point were both lower and higher than 98 percent, with an overall average of 85 percent.) Under the 

averaging convention used to determine the distribution of the Model 2 EFs, all film types were given 

equal weight. This led to some gas/wafer/film combinations that had a small number of highly variable 

values having a large impact on GWP-weighted EF uncertainties. 

Overall, in reviewing the analyses described above, it can be concluded that any reductions in bias and 

uncertainty related to Model 2 EFs are not always consistent and appear minimal in light of the its

added complexity and the often poorly estimated uncertainties. Additionally, while these analyses 

indicated a slight preference for grouping etch EF data by input gas, wafer size, and film type to develop 

default EFs, the EPA was mindful that using this approach would increase the burden associated with 

apportioning gas consumption to different film types, as compared to default etch EFs grouped only by 

input gas and wafer size. Therefore the EPA found it necessary to further assess the EFs models in terms 

Figure 2. Uncertainty of GWP-Weighted EFs 

for 300 mm Etch Processes

Figure 1. Uncertainty of GWP-Weighted EFs 

for 200 mm Etch Processes
August 2012 Subpart I Default Emission Factor Method Technical Support 

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of how they would impact the uncertainty in overall fab emission estimates before making any decisions 

about which model to use in proposing revised default etch EFs. 

2.2. Evaluation and Comparison of Emission Estimates Resulting From EF Models 1 and 2 

To further assess the two etch EF models and determine which model would be most suitable to 

propose to use for an emissions calculation and reporting method, the EPA analyzed the overall 

uncertainty of emission estimates developed using both models. For completeness in this uncertainty 

analysis, the EPA also considered emissions from chamber cleaning processes. As seen in Table 1 there 

were no new data available to the EPA to support revised default EFs for the chamber cleaning process 

sub-types established in the December 2010 subpart I (in-situ thermal, in-situ plasma, and remote 

plasma). Therefore, in this uncertainty analysis the EPA used the same chamber clean EF data used to 

establish the current chamber clean EFs.12 The EPA did not consider emissions resulting from gas 

consumed in wafer cleaning processes (which are required to be estimated under subpart I) because 

wafer cleaning F-GHG consumption is estimated to represent approximately one percent of total fab FGHG consumption, and is expected to similarly represent a small percentage of fab total emissions (SIA, 

2012a). 

To evaluate and compare the two EF models (for estimating emissions resulting from etching and 

chamber cleaning processes) in regards to their impact on the uncertainty in total emission estimates 

the EPA employed a Monte Carlo simulation method. In line with the IPCC recommendations, a Monte 

Carlo-based method was chosen over a propagation of error method because the uncertainty of default 

EFs and other input parameters can be high (>20 percent at 95 percent confidence level).13 The key data 

used in this analysis were the available etch and chamber clean EF data and gas consumption data 

provided by SIA to the EPA. The use of abatement systems (and the uncertainty associated with the use 

of abatement) was not taken into account in the EPA’s analysis (i.e., all emissions estimated were 

unabated). 

2.2.1. Emissions Factors Inputs

As discussed in Section 2.1, a key factor in determining the precision of a default-EF based method is the 

uncertainty of the EFs. While large standard deviations are generally observed for EFs in the 

semiconductor industry due to significant process-to-process variations (see Figures 1 and 2), it is 

nevertheless possible to arrive at a reasonable level of precision for fab-level emission estimates, so long 

as the default EFs are representative of the mix of processes run at a particular fab. Indeed, while 

recipe-specific EFs will significantly vary from one process or one type of tool to another, the impact of 

that variability will be reduced when considering that within each gas/wafer-size category, 

semiconductor facilities may have multiple processes with distinctly different EFs whose differences will 

at least partially “average out”, approximating the mean (default) EFs.” Thus, to account for this 

phenomenon, the EPA introduced the variable “n,” which provides a measure of the representativeness 

and the statistical significance of the mean values of the EFs, when calculating emissions at the fab level. 

 

12 The EPA used the revised NF3 remote plasma clean EF data set per the discussion in Appendix B of the November 2010 TSD in 

the uncertainty analysis. For consistency the EPA also used the “no-zeros” method is calculating by-product EFs for this analysis; 

unlike what was done in the December 2010 subpart I rule.

13 See IPCC Guidelines for National Greenhouse Gas Inventories, Volume 1, Chapter 3 (Uncertainties).
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The EPA considered various assumptions about the uncertainty of EFs to model fab-level uncertainty:

The uncertainty associated with the average default EFs was calculated as 1.96 times the standard 

deviation of a certain EF data group sample (i.e., wafer size and input gas, for Model 1, and wafer size, 

input gas, and film type for Model 2) divided by the mean and by the square root of n, where it is 

assumed that n represents the number of processes within an EF grouping that have distinctly different 

EFs.14 The EPA studied four cases for n: 

1. n=NDB represents the case where n equals the number of individual data points in the data set 

for each wafer size and each gas (Model 1) and each wafer size, gas, and film type etched

(Model 2) combination. 

2. n=SIA3 represents the case where n represents an average count of the etch tool types 

(assuming each tool type runs a process or set of processes that results in a distinctly different 

EF) and etch process combinations by fab and assumes that the number of CVD tool chamber 

cleaning types equals three. The etch tool type n values were provided by SIA to the EPA. 

3. n=SIA1 represents the case where n represents an average count of the etch tool types and etch 

process combination by fab and assumes only one type of CVD tool chamber cleaning (i.e., NF3

remote clean only). The etch tool type n values for this case are the same as for the case where 

n=SIA3. 

4. n=1 represents the “worst case scenario”; This case was selected to model the boundary 

condition where only one process exists within a particular EF grouping (e.g., 200 mm, CF4, oxide 

etch) at a particular fab, and where the uncertainty of the overall estimate would not take into 

account the fact that semiconductor facilities run a variety of processes that, on average, may 

approximate the mean (default) EFs.

2.2.2. Gas Consumption Inputs

In addition to the uncertainty and representativeness of the EFs, another main issue driving the 

precision of emission estimates at the fab level is the uncertainty related to apportioning of gas 

consumption. Most facilities do not track consumption on a tool or process type basis, but only measure 

consumption by gas at the fab (gas cylinder) level, where it is not possible to distinguish consumption of 

a particular gas related to one specific EF (e.g. the amount of C2F6 used for CVD chamber cleaning cannot 

be distinguished from the amount of C2F6 consumed for etch). Thus, it is necessary to rely on an 

apportioning model to estimate the consumption of each gas for each process type or sub-type (see 

Section 4 for more information on apportioning model development for reporting purposes). To 

estimate the impact of the uncertainty related to gas consumption apportioning, the EPA relied on gas 

consumption data provided by SIA for the following profiles (see SIA, 2012a):

 Fab-level gas consumption for two 200 mm fabs (Fabs B and C) and two 300 mm fabs (Fabs A 

and E) broken out according to Model 1 for 2009 and 2010.

 

14 The approach the EPA used in the analysis was to calculate the standard deviation of each EF and calculate the 

uncertainty of GWP-weighted EF at the 95 percent confidence level.
August 2012 Subpart I Default Emission Factor Method Technical Support 

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 “Industry-level” gas consumption for the 200 mm wafer size broken out according to Models 1 

and 2 for 2009 and 2010. (The term industry level refers to an aggregate group of seven 200 mm 

fabs).

 “Industry-level” gas consumption for the 300 mm wafer size broken out according to Models 1 

and 2 for 2009 and 2010. (The term industry level refers to an aggregate group of five 300 mm 

fabs).

The EPA’s analysis took into account the uncertainties associated with apportioning of gas consumption 

to process types/sub-types (see below), and total gas consumption estimates. The EPA evaluated 4 cases 

of assumed gas apportioning uncertainties (95 percent confidence intervals), represented by the 

variable f (1.96 multiplied by the standard deviation of the gas proportion), including:

1. f = 5 percent (based on the apportioning model verification standard from the December 2010 

subpart), 

2. f = 10 percent, 

3. f = 20 percent, and 

4. f = SIA represents a range of apportioning uncertainties estimated by SIA, varying between 56

and approximately 240 percent (SIA, 2011c and SIA, 2012a) To estimate apportioning 

uncertainties, SIA looked at the variability in the difference between modeled and actual 

consumption for a gas at the fab-level and then used a statistical argument to translate this 

variability into potential apportioning errors for each process (see SIA, 2011c for the total fablevel differences). 

There were limitations to the data on apportioning error submitted by SIA: actual gas consumption 

estimates were based on purchase records and did not appear to account for accumulation or 

drawdown of on-site gas inventories. Failing to account for changes in these inventories could lead to 

year-to-year fluctuations in the difference between actual and modeled consumption at the fab, and 

such fluctuations would have nothing to do with errors in assigning consumption to different process 

types (i.e., apportioning error). The range in apportioning errors provided by SIA in 2012 also differed 

from information previously submitted, showing at one facility that a difference of less than 10 percent 

was achievable when measuring and modeling gas consumption at the tool level (SIA 2011b). For these 

reasons the EPA assumed various apportioning uncertainties as noted above. The EPA also assumed an 

uncertainty of 5 percent on total gas consumption estimates for all runs of the analysis (based on the 

calibration accuracy requirements in 40 CFR 98.3(i) of subpart A, which the EPA is proposing to adopt for 

subpart I).

2.2.3. Analysis Results Summary

A comparison of the results for n=SIA3 and varying assumptions for f for Models 1 and 2 (taking into 

account chamber cleaning emissions as well as etch emissions) are presented in Table 2. The gas use 

patterns modeled were industry totals provided by SIA across seven 200 mm fabs and five 300 mm fabs 

for 2009 and 2010; however, the uncertainties calculated here are not intended to represent industrylevel uncertainties, which would generally be expected to be lower than fab-level uncertainties. 

Nominal emissions (i.e., emissions calculated without accounting for uncertainties) and simulated 

emissions (i.e., the mean of the Monte Carlo emissions profile when accounting for uncertainties) are 
August 2012 Subpart I Default Emission Factor Method Technical Support 

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indicated in metric tons of CO2 equivalent (mtCO2e). Also shown on Table 2 are the lower and upper 

uncertainty bounds of the 95 percent confidence interval, expressed as a percentage of the nominal

value. 

Several trends can be identified by comparing the results in Table 2. First, it can be observed that Model 

2 systematically provides lower emission estimates than Model 1, with a difference of between 2.2

percent and 5.2 percent in nominal emissions between the two models (3.6 percent on average). This 

effect appears to be due to the difference in the default EFs according to the two groupings, with the 

EFs for Model 2 generally being slightly lower than for Model 1. While differences between emissions 

estimated for 2009 and 2010 can be observed (2.5 to 3.8 percent increase in nominal emissions 

between 2009 and 2010 for 200 mm, depending on the model, and 24.9 to 26 percent for 300 mm), this 

is mainly attributed to differences in utilization efficiencies of the facilities rather than changes in gas 

consumption patterns.

Second, it is interesting to note that the average uncertainty of the emissions estimate for the 200 mm 

results is 15.4 percent while it is 32.1 percent for the 300 mm facilities. This effect is attributed to the 

larger uncertainty observed for 300 mm EFs (98.3 percent on average), compared to 200 mm factors 

(86.4 percent on average). This trend is further amplified by the large uncertainty associated with the 

NF3 remote clean EFs for both wafer sizes (223 percent for 200 mm and 233 percent for 300 mm), taking 

into account that the use of this cleaning technology is more prevalent for 300 mm facilities than for 200

mm facilities.

Third, when comparing the uncertainties of the emissions estimates for Models 1 and 2, the difference 

averaged over all cases presented in Table 2 is only 1.4 percent, with Model 2 generally providing 

slightly higher precision than Model 1. However, this trend is not consistent and in some cases the 

precision of Model 1 is greater than for Model 2 (in particular for 200 mm profiles). For example, the 

200 mm case with f=5 (first two rows of Table 2) shows an average uncertainty (i.e. the average of lower 

and upper bound uncertainty at 95 percent confidence) of 13.3 percent for Model 1 and 13.9 percent 

for Model 2. 

Fourth, it can be observed that the upper and lower bounds of the uncertainty interval (95 percent 

confidence) are not symmetrical. This is most pronounced for the 2010 300 mm profile with f=SIA (last 

row of Table 2) where the difference between the lower bound emissions represents a -40.9 percent 

deviation from the nominal value while the upper uncertainty bound represents a +80.8 percent 

deviation from the nominal value. This phenomenon is less pronounced for 200 mm facilities and for 

lower values of f. It can be attributed to the more prevalent use of the NF3 remote clean technology for 

300 mm facilities and to the unique characteristics of the EF profile for this technology: because the 

utilization efficiency of NF3 with the remote clean technology is so close to 100 percent (98.2 percent) 

and because utilization efficiencies cannot exceed 100 percent (it is not possible to use more than 100

percent of the precursor gas), it is necessary to truncate the distribution profile for the NF3 remote clean 

EF, which becomes asymmetric (approximating half a Gaussian). This characteristic, combined with the 

fact that NF3 used for remote clean represents the largest gas consumption in 300 mm facilities, explains 

the asymmetry of the emissions profiles and the fact that nominal emissions estimates can be lower 

than simulated emissions estimates (in particular for 300 mm). While the difference between nominal 
August 2012 Subpart I Default Emission Factor Method Technical Support 

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and simulated emissions is generally less than 0.5 percent, this difference for f=SIA, 300 mm, 2010, 

Model 2 (last row of Table 2) is 7.85 percent.

Finally, it is interesting to note that the impact of f, which reflects the uncertainty associated with gas 

consumption apportioning, is relatively limited between f=5 and f=20, but that this impact is well 

pronounced for the values of f=SIA. For example, while the average uncertainty of the overall estimates 

varies between 11.4 percent and 14 percent for f=5 to 20 for 200 mm (between 19.6 percent and 25.7

percent for 300 mm), the average uncertainty is 23.8 percent for 200 mm facilities with f=SIA (60.5

percent for 300 mm). The impacts of the variables f and n are further evaluated below. 
August 2012 Subpart I Default Emission Factor Method Technical Support 

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Table 2. Comparison of Analysis Results for Models 1 and 2 for n=SIA3 and Various Values of f

(mtCO2e)

Wafer 

Size f Year Model

Nominal 

Emissions 

Estimate 

(TCO2e)

Simulated 

Emission 

Estimate 

(TCO2e)

% Lower 

Uncertainty 

Bound 

(95% CI)

% Upper 

Uncertainty 

Bound 

(95% CI)

200 mm

5

2009

1 1,288,195 1,289,421 -13.1% 13.5%

2 1,236,741 1,235,722 -13.8% 13.9%

2010

1 1,337,585 1,337,838 -11.4% 11.7%

2 1,267,472 1,266,101 -11.6% 11.7%

10

2009

1 1,288,195 1,288,883 -13.3% 13.6%

2 1,236,741 1,237,317 -13.4% 13.9%

2010

1 1,337,585 1,336,303 -11.2% 11.7%

2 1,267,472 1,264,090 -12.0% 11.6%

20

2009

1 1,288,195 1,288,022 -13.2% 13.6%

2 1,236,741 1,233,985 -14.0% 14.0%

2010

1 1,337,585 1,338,610 -11.3% 11.8%

2 1,267,472 1,264,586 -12.0% 11.7%

SIA

2009

1 1,288,195 1,252,389 -33.0% 20.5%

2 1,236,741 1,201,227 -38.6% 24.4%

2010

1 1,337,585 1,318,213 -22.5% 15.3%

2 1,267,472 1,267,928 -18.0% 18.2%

300 mm

5

2009

1 437,764 439,114 -24.3% 25.8%

2 424,431 425,184 -18.9% 20.6%

2010

1 546,737 547,474 -22.6% 24.8%

2 534,813 534,920 -19.8% 21.7%

10

2009

1 437,764 438,904 -24.1% 25.1%

2 424,431 425,101 -18.9% 20.2%

2010

1 546,737 548,566 -23.1% 25.2%

2 534,813 536,969 -19.9% 22.1%

20

2009

1 437,764 439,969 -24.5% 26.9%

2 424,431 425,846 -19.6% 21.6%

2010

1 546,737 548,911 -23.3% 25.5%

2 534,813 536,907 -20.2% 22.9%

SIA

2009

1 437,764 444,496 -47.7% 72.6%

2 424,431 455,230 -38.9% 75.9%

2010

1 546,737 558,492 -49.1% 78.1%

2 534,813 576,813 -40.9% 80.8%
August 2012 Subpart I Default Emission Factor Method Technical Support 

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While Table 2 presents emission estimates at the “industry level” for an aggregate of seven 200 mm fabs 

and five 300 mm fabs , the EPA also analyzed emissions from the four individual facilities for which SIA 

provided gas consumption profiles (two 200 mm, Fabs B and C and two 300 mm, Fabs A and E). The 

results of this analysis are provided in Figure 3, where the nominal as well as the simulated mean 

emissions are presented, along with the lower and upper bound of the 95 percent confidence interval 

(as predicted by the Monte Carlo simulation -- P2.5 and P97.5) for each fab and each year (2009 and 

2010). For this analysis, values of f=20 and n=SIA3 were retained. The results indicated that the average 

uncertainty (the average of the lower and upper bound uncertainty) varied between 10.7 percent and 

40.2 percent, with the uncertainty of the 300 mm fabs being higher than for the 200 mm fabs (31.3

percent versus 14.3 percent over 2009 and 2010), confirming the trend observed in Table 2. Also, the 

emissions for year 2010 were generally higher than for year 2009, with average uncertainty values 

varying from 0.4 to 5 percent year over year, these trends being attributed to changes in facility 

production levels rather than to changes in gas consumption patterns.

Figure 3. Nominal, Simulated, Lower, and Upper Bound Emissions (P2.5 and P97.5) at the 95 Percent

Confidence Interval for Fabs B and C (200 mm) and A and E (300 mm) in 2009 and 2010

As noted above while observing the results in Table 2, the impact of variable f, which represents the 

uncertainty related to gas consumption apportioning, seems limited at relatively small values of f (f<20 

percent). To confirm this trend, Monte Carlo simulations where run on the ‘industry-level’ 300 mm 

consumption profile (Model 1, year 2009) with increasing values of f=5, 10, 20, 50, 100, 150, 200

percent, and compared to the range of apportioning uncertainties provided by SIA for various gases 

(f=SIA). The results of the simulations are shown in Figure 4, which depicts the emissions estimates for 

the nominal value of the estimate, the simulated emissions, and the upper and lower bounds of the 

emissions estimates at the 95 percent confidence level (P2.5 and P97.5). The value of n chosen for this 
August 2012 Subpart I Default Emission Factor Method Technical Support 

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analysis was n=SIA3. As can be seen, the results confirm the trend observed in Table 2, where the 

average uncertainty is relatively stable for f=5 to f=20 (24.5 percent to 25.6 percent), but where the

uncertainty starts to go up at higher values of f (30.4 percent for f=50 to 69.9 percent for f=200). 

Interestingly, the upper bound of the uncertainty interval increases faster with higher values of f than 

the lower bound decreases, probably due to an exacerbation of the asymmetry of the profile of the NF3

remote clean gas consumption, as discussed above. Note also that the difference between the simulated 

and the nominal value of the estimate increases slightly with higher f values, due to the same 

phenomenon. When comparing the uncertainty range for f=SIA - which does not provide a fixed value 

for f but a range of values for different gases - Figure 4 suggests that the range of apportioning 

uncertainties provided by SIA corresponds to a fixed value of f of approximately 145.

These results support the EPA’s proposal (see Section 4) to set the maximum difference between the 

modeled and actual gas consumption to 20percent as a requirement for verifying the model used to 

apportion gas consumption, as this quality assurance requirement seemed to achieve a suitable balance 

between technical feasibility and burden, without compromising the uncertainty of the overall 

emissions estimates.

Figure 4. Impact of Apportioning Uncertainty (f) on Emissions Estimates for a Typical 200 mm Fab

In addition to studying the impact of the accuracy of apportioning gas consumption, the EPA 

investigated the influence of the variable n (as discussed in Section 2.2.1). Figure 5 presents the results 

of varying values of n (n=1, SIA1, SIA3, NDB), showing the evolution of the nominal value of the 

emissions estimate, of the simulated emissions, and of the upper and lower bounds of the emissions 

estimates at the 95 percent confidence level (P2.5 and P97.5) for the ‘industry-level’ 300 mm 

consumption profile provided by SIA (Model 1, year 2009). The value of f chosen for this analysis was 

f=20. As can be seen by comparing Figures 4 and 5, the impact of n is much greater than the impact of 
August 2012 Subpart I Default Emission Factor Method Technical Support 

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apportioning uncertainty (f). When varying n from n=1 to n=NDB, the average uncertainty decreases 

from 39.4 percent to 10.5 percent.

Figure 5. Impact of Representativeness of EFs (n) on Emissions Estimates for the 300 mm ‘Industry 

Level’ Gas Consumption Profile (2009, f=20)

Trends for the 3-factor model and 200 mm facilities were found to be similar to those for the two-factor 

model: increasing f values tend to increase the overall uncertainty (but the difference is also minimal for 

values of f of 20 or lower), and increasing the value of n decreases the overall uncertainty interval. In 

looking at the overall results of this analysis, it appears that, in light of its added complexity, Model 2 

does not provide substantial improvements in the uncertainty of emissions estimates compared to 

Model 1. Thus, as summarized in the next section, the EPA opted to propose Model 1 for all 

semiconductor facilities to estimate and report emissions under subpart I.

2.3. Development of Proposed Revised Default Etch Emission Factors

To develop the proposed revised default etch EFs, the EPA used the methods and conventions described 

and selected in 2.1. This included:

 Using a simple arithmetic averaging method to develop default utilization and by-product EFs by

process type/sub-type and gas; and 

 Using the dominant gas convention for assigning by-product emissions for chamber clean byproduct EFs and the all inputs gas convention for assigning by-product emissions for etch EFs.

As described and shown in Section 2.2, the uncertainty improvements that Model 2 could provide are 

minimal in light of the added complexity of it compared to Model 1, and are not always consistent. 

Therefore the EPA is proposing revised default etch EFs for semiconductor manufacturing facilities that 
August 2012 Subpart I Default Emission Factor Method Technical Support 

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correspond with the first model (Model 1) described above: default EFs by gas and wafer size for one 

broad etching process type and three CVD chamber cleaning process sub-types based on the CVD 

chamber clean process being used (in-situ thermal chamber clean, in-situ plasma chamber clean, and 

remote plasma chamber clean).

Despite the larger sample of EF data available, there are still data gaps for certain wafer sizes and 

process types. First, little to no data exist to support establishing separate default EFs for facilities 

manufacturing 150 mm wafers, yet it is estimated that more than 25 facilities using this wafer size are 

still in operation (WFF, 2011).

15 Consistent with the December 2010 version of subpart I, the EPA is 

proposing that the EFs for 200 mm technologies would apply for 150 mm as well. The EPA based the 

2010 EFs for 150 mm wafers on a conclusion that EFs for 150 mm processing technology are expected to 

be closer to 200 mm processing technologies than to 300 mm processing technologies (November 2010 

TSD). Second, the EPA did not receive any new chamber clean emissions data and, therefore, the EPA is 

proposing to maintain the chamber clean EFs finalized in the December 2010 rule.16

As part of the proposed revisions to the F-GHG EFs the EPA is also proposing to combine the wafer clean 

process type with the etch process type because both processes are very similar in principle. Both wafer 

cleaning and etch processes involve using plasma generated fluorine atoms or other reactive fluorinecontaining fragments to remove material from wafer surfaces. While etch processes are used to 

selectively remove portions of material from exposed thin films (e.g., dielectric, metals), wafer cleaning 

relies on similar reactions to remove similar residual materials, including at the wafer edge. Additionally,

wafer cleaning operations are estimated to represent about one percent of total fab F-GHG 

consumption, and thus are expected to represent a similarly small percentage of total fab emissions 

(SIA, 2012a). Lastly, the burden associated with apportioning gas consumption to the various process 

types is expected to be reduced by combining the wafer clean and etch process types because some 

gases used for wafer cleaning (e.g., CH2F2 and SF6) are also used in etching processes. 

3. Estimating F-GHG Emissions from Semiconductor Fabs Manufacturing Wafers Larger than 300 mm

The next generation of semiconductor fabs will produce wafers 450 mm in diameter, but commercial 

manufacturing is unlikely to occur before 2017 (SIA, 2012c). Therefore, the EPA currently has no EF data 

upon which to establish default EFs for the 450 mm wafer size. To address this in the December 2010 

version of subpart I, the EPA finalized provisions that required facilities to use a recipe-specific approach 

to estimate F-GHG emissions from all semiconductor processes used at a fab.

As described in Section 2, because of the concerns raised by SIA in regards to using recipe-specific EFs, 

the EPA is proposing to remove the provisions that require a recipe-specific approach for facilities 

manufacturing wafers larger than 300 mm. The EPA is proposing that these fabs use the same default 

EFs that the EPA is proposing for semiconductor fabs manufacturing wafers that are 300 mm or smaller

and are proposed to no longer have the option to use recipe-specific EFs.

 

15 Note that all of these facilities may not meet the threshold for reporting under the GHGRP.

16 It should be noted that the by-product EFs associated with chamber clean have been modified since the December 2010 

proposal because they now follow the convention where zeros are not assigned in cases where no by-product EF is reported. 

The utilization EFs for chamber clean are still consistent with the December 2010 proposal.
August 2012 Subpart I Default Emission Factor Method Technical Support 

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The EPA, based on information provided by SIA, understands that process chemistries used for the 450

mm wafer technology will not change substantially from those used for 300 mm wafers (SIA, 2012c). 

While the EFs for 450 mm wafers are expected to be different from those for 300 mm wafers, no data 

are currently available to quantify this difference. However, this difference is expected to be, for 

example, less than the difference between those for 200 mm and 300 mm wafers. Therefore, at this 

time, the EPA is proposing to require any fabs manufacturing 450 mm wafers to use the default EFs

developed for fabs manufacturing 300 mm wafers because of the similarity in process chemistries 

between 300 mm and 450 mm wafers. 

In the future, the EPA may develop separate EFs for fabs manufacturing 450 mm wafers. In order to 

provide for consistent review of technology changes in the semiconductor manufacturing industry and 

ensure that default gas utilization rates and by-product formation rates and DRE values accurately 

reflect the industry’s practices in future years, especially with respect to 450 mm wafers, the EPA is also 

proposing to require certain semiconductor manufacturing facilities to provide a report to the EPA every 

3 years, beginning in 2017, that addresses technology changes at the facility that could affect GHG 

emissions. The report would address how technology in the industry has changed over the previous 3 

years and the extent to which any of the identified changes are likely to have affected the emissions 

characteristics of semiconductor manufacturing processes in such a way that the default EFs and/or DRE 

values in subpart I may need to be updated or augmented. For more information on this proposed 

provision, please see the preamble for the proposed rule amendments.

4. F-GHG and N2O Apportioning Model Development and Verification

When using an emission estimation method based on EFs in units of mass of gas emitted per mass of gas 

used for various process types and sub-types, fab-wide gas consumption must be apportioned to the 

appropriate process types and p sub-types through the use of fab-specific apportioning factors.17

The EPA is proposing to modify various aspects of the provisions related to developing apportioning 

factors. The provisions, as well the proposed changes and rationale for modifying the provisions are 

provided in Table 3 below.

Table 3. Summary of Proposed Revisions for F-GHG and N2O Apportioning Model Development and 

Verification

December 2010 Provisions Proposed Revised Provisions Rationale for Proposed 

Modification/Addition

Apportioning Model Applicability

The same apportioning model is 

used for the whole facility. 

Apportioning model and verification 

would be on a fab-basis instead of a 

facility basis.

A facility with more than one fab could use 

different methods to estimate F-GHG 

emissions in each fab, use different sized 

wafers, or manufacture different products 

(e.g., MEMS and semiconductors).

Actual Gas Consumption Monitoring Period

Analyze at least a 30-day period of The EPA is proposing to replace the In the December 2010 subpart I, the EPA 

 

17 Apportioning F-GHG and N2O consumption to tools with abatement systems is also required when taking into account 

controlled emissions (i.e., to determine the fraction of an F-GHG used for a process type in tools with associated abatement 

systems).
August 2012 Subpart I Default Emission Factor Method Technical Support 

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operation during which the capacity 

utilization equals or exceeds 60 

percent of the facility design 

capacity. In the event a facility 

operates below 60 percent of its 

design capacity for the whole year,

the facility must use the period of

the highest 30-day average 

utilization for model verification.

“capacity utilization” requirement with 

“representative operating levels,” 

defined to mean operating the fab, in 

terms of substrate starts for the period of 

testing or monitoring, at no less than 50 

percent of installed production capacity 

or no less than 70 percent of the average 

production rate for the reporting year, 

where production rate for the reporting 

year is represented in average monthly 

substrate starts. 

The EPA is also proposing to modify the 

actual gas consumption monitoring 

period to clarify that it may extend up to 

the full year of operation.

required facilities to monitor actual F-GHG 

consumption for a period of at least 30 

days in order to verify an apportioning 

model. The EPA required a minimum 30-

day period to minimize burden to 

reporters. However, in light on new 

information provided by SIA, the EPA is 

clarifying that facilities may monitor actual 

F-GHG consumption for a period of more 

than 30 days and up to a full reporting year. 

The EPA is providing this clarification and 

incorporating the “representative 

operating level definition” because a 

facility may not have the information 

needed to confirm whether a 30-day period 

meets the minimum capacity utilization 

criteria until the end of the reporting year. 

Therefore, some facilities may not be able 

to select a 30-day period in which to 

monitor actual F-GHG consumption. (SIA, 

2012f) 

Consumptions to be Compared for Model Verification

The modeled and actual F-GHG 

consumption for the F-GHGs used 

in the largest quantity, on a mass 

basis, for one F-GHG used for the 

etch process type and one F-GHG 

used for the chamber cleaning 

process type. The comparison made 

for the F-GHGs used in the largest 

quantity for the etch process type is 

the comparison that must meet the 

verification standard (see next 

row).

The modeled F-GHG consumption for the 

F-GHG used in the largest quantity at the 

fab, on a mass basis, for one F-GHG must 

be compared for verification purposes. 

Fabs may elect to compare the modeled 

and actual F-GHG consumed for two FGHGs for verification purposes and 

demonstrate that the verification 

standard is met on an aggregate use 

basis for both F-GHGs if one of the FGHGs selected for comparison 

corresponds to the largest quantity, on a 

mass basis, of F-GHG used at the fab 

during the reporting year.

The EPA is proposing that the comparison 

must be based on total F-GHG consumption 

(as opposed to F-GHG consumption for the 

etch process type) because most fabs are 

not configured to measure F-GHGs 

consumption between the etch and clean 

process types (SIA, 2012f), or do not have 

the infrastructure in place to collect this 

information centrally. The proposed 

modification would reduce burden 

associated with apportioning model 

verification.

The EPA is also proposing to give fabs the 

flexibility to verify the model for two FGHGs on an aggregate use basis because, in 

some cases, it may be difficult to predict 

the F-GHG that will be used in the largest 

quantity for the reporting year if the 

expected quantities of consumption are 

similar.

Verification Standard

To maintain flexibility for facilities

in developing apportioning models 

the EPA allows the development of 

a model based on any quantifiable 

metric of activity that best suits the 

needs or capability of the particular 

facility (see November 2010 TSD for 

Fabs must demonstrate that the 

comparison performed for the largest 

quantity of F-GHG(s), on a mass basis,

used in the fab does not result in a 

difference between the actual and 

modeled F-GHG consumption that 

exceeds 20 percent relative to actual FThe EPA is proposing to relax the 

verification requirement because 

information submitted by SIA for a sample 

test of model verification at a fab 

demonstrates the difficulty in meeting the 

5 percent standard. (SIA, 2011b)
August 2012 Subpart I Default Emission Factor Method Technical Support 

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more discussion). However to 

ensure quality of data, EPA requires 

facilities to meet an apportioning 

model verification standard of 5

percent, as measured by the 

difference between the actual and 

modeled F-GHG consumption for 

the F-GHG used in the largest 

quantity for the etch process type. 

GHG consumption.

Revising the verification standard from 5 

percent to 20 percent is not expected to 

substantially impact the accuracy of 

emission estimates because the main 

source of error in emission estimates is 

from the EFs used, not the apportioning 

error (see discussion in Section 2.2.2 of this 

document on uncertainty evaluation).

In looking at Figure 4 it can be seen that as 

long as the apportioning uncertainty is 20 

percent or less, the overall uncertainty in 

emission estimates varies little with the 

apportioning uncertainty. However, in 

moving to higher apportioning

uncertainties (e.g., 50 percent) a trend in 

increasing overall uncertainty in emission 

estimates can be seen.

Alternative to Developing an Apportioning Model

Not addressed.

(Facilities were not given the option 

of measuring F-GHG consumption 

directly instead of using 

apportioning factors developed 

through modeling.)

Fabs have the option to develop 

apportioning factors through the use of 

direct measurement using F-GHG flow 

meters and weigh scales to measure 

process sub-type, process type input FGHG consumption.

Some fabs may be configured such that FGHG consumption can be measured at the 

process type/sub-type or at tool level 

using direct F-GHG measurement (e.g., flow 

meters and weigh scales) (SIA, 2012f). The 

EPA is proposing to allow direct 

measurement of F-GHG consumption as an 

alternative to developing an apportioning 

model. This provides fabs flexibility while 

potentially improving accuracy in emission 

estimates by using direct measurements in 

place of estimates. 

5. Accounting for Abatement System Uptime When Using a Default EF Method

In December 2010 the EPA finalized a method for estimating uptime of abatement systems based on the 

ratio of the total time an abatement system is in an operational mode18 and the time when F-GHGs or 

N2O are flowing through the connected process tool(s). While this method is the most accurate to 

measure abatement uptime and its impact on emissions, SIA raised the issue that it is not consistent 

with how industry currently tracks abatement system uptime. Complying with the December 2010 

provisions would increase burden on facilities because of required installation of hardware or software 

to track and correlate the abatement systems’ operational time with the time in which F-GHGs or N2O 

are flowing in through tools. (For a more detailed discussion see SIA, 2012d).

SIA suggested a simplified approach for calculating uptime and provided additional data and information 

on the uptime of actual abatement systems. The simplified approach calculates uptime for all 

abatement systems for a given gas and process type or sub-type combination, instead of by individual 

 

18 Operational mode is defined in subpart I as the time in which an abatement system is being operated within the range of 

parameters as specified in the operations manual provided by the system manufacturer.
August 2012 Subpart I Default Emission Factor Method Technical Support 

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abatement systems. SIA also suggested that the amount of time a gas is flowing through the tool 

connected to an abatement system should be assumed to be equal to the maximum amount of time a 

tool could be operating each year (i.e., 525,600 minutes per year) (see SIA, 2012d).

The EPA evaluated the abatement uptime information submitted by SIA on actual fab abatement 

systems. This information included a comparison of SIA’s suggested approach for calculating uptime and 

an estimation of how uptime would be calculated using the December 2010 subpart I method. SIA also

quantified the impact of estimating uptime for all abatement systems associated with a gas/process 

type or sub-type combination as opposed to estimating uptime by individual abatement systems. These 

data and results are detailed in SIA, 2012d and SIA, 2012e. In summary, the information provided by SIA 

on its suggested method along with further analysis by the EPA showed that:

1. The method suggested by SIA results in more conservative estimates of uptime for most 

common scenarios (when abatement system uptime is greater than 40 percent) because it 

assumes F-GHGs are flowing through the tool constantly. (SIA, 2012d)

2. Uptimes estimated using SIA’s suggested method are generally high. On average they were 

estimated to be between 75 percent and 90 percent based on data submitted from SIA for a 

sample of 3 companies. (See data in SIA, 2012d)

3. SIA’s suggested method can rely on current industry practices, thereby reducing the need for 

fab system modifications.

4. Calculating and applying uptime on an aggregate basis for all abatement systems associated 

with a gas/process type or sub-type combination, as opposed to uptime for each separate 

abatement system, would remove the need to apportion gas consumption and estimate GHG 

emissions at an individual tool/abatement system level, reducing associated burden. (SIA, 

2012e)

Based on the above listed reasons, the EPA concluded that burden would be minimized by monitoring 

and calculating uptime by gas/process type or sub-type, instead of by individual system. The EPA is 

proposing to revise the abatement system uptime calculation used with the default EF method, based 

on the suggestion by SIA for estimating abatement system uptime that reflects current semiconductor 

industry practices. This approach is based on calculating the ratio of the total time that all abatement 

systems abating a given process type or sub-type/gas combination are not operational to the total time 

when the abated tools are in operation. The total time that the abated tools are in operation would be 

pro-rated if some tools are only operational for a portion of the year (e.g., if they are removed from or 

installed in the fab during the year). The proposed revised method to calculate uptime is represented in 

the equation below, and is applicable for all facilities using a default EF-based emission estimation 

method (regardless of the type of product manufactured).

 = 1 −

∑ 

∑ 

where:
August 2012 Subpart I Default Emission Factor Method Technical Support 

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UTij = The average uptime factor of all abatement systems connected to process tools in the fab 

using input gas i in process sub-type or process type j (expressed as a decimal fraction).

Tdijp = The total time, in minutes, that abatement system p, connected to process tool(s) in the fab 

using input gas i in process sub-type or process type j, is not in operational mode (as defined in 

40 CFR 98.98 of subpart I), when at least one of the tools connected to abatement system p is in 

operation.

UTijp = Total time, in minutes per year, in which abatement system p has at least one associated tool 

in operation. For determining the amount of tool operating time, a facility could assume that 

tools that were installed for the whole of the year were operated for 525,600 minutes per year. 

For tools that were installed or uninstalled during the year, a facility would prorate the 

operating time to account for the days in which the tool was not installed, and would treat any 

partial day that a tool was installed as a full day (1,440 minutes) of tool operation. For an 

abatement system that has more than one connected tool, the tool operating time would be 

525,600 minutes per year if at least one tool was installed at all times throughout the year. If a 

facility had tools that were idle with no gas flow through the tool, the facility could calculate 

total tool time using the actual time that gas is flowing through the tool.

i = Input gas.

j = Process sub-type or process type.

p = Abatement system.

Because the EPA is now proposing that uptime be tracked and associated with a particular gas, it is 

necessary to also propose an equation for estimating the uptime for emissions of by-products that are 

not also used as input gases. While the calculation method for the uptime associated with by-product 

emissions is the same as the method presented in the above equation, different inputs are needed that 

correlate to uptime for by-products as opposed to utilization emissions. For example, for estimating 

uptime for a particular by-product the variable UTjk (The average uptime factor of all abatement 

systems connected to process tools in the fab which emit by-product gas k, in process sub-type or 

process type j) would replace UTij in the above equation, and so forth. 

6. References

IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse 

Gas Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. 

Buendia, K. Miwa, T Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan. Available at: 

http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/3_Volume3/V3_6_Ch6_Electronics_Industry.pdf.

WFF(2011). Semiconductor Equipment and Materials Industry (2011) World Fab Forecast, May 2011 

Edition. (For purchase)

SIA (2011a). SIA Alternatives to Recipe-Specific Testing, August 11, 2011. Presentation from SIA/SIMI to 

EPA. Available in EPA docket EPA-HQ-OAR-2011-0028.. 
August 2012 Subpart I Default Emission Factor Method Technical Support 

Page 23 of 23

SIA (2011b). Semiconductor Engineering Models for Apportionment of Fluorinated Greenhouse Gases to 

Process Categories (Verification Tests to Demonstrate Difficulty of achieving 5 percent limit). June 16, 

2011. Available in EPA docket EPA-HQ-OAR-2011-0028.

SIA (2011c). SIA/ISMI Presentation of Etch Alternative, November 17, 2011 and Additions & Corrections: 

ISMI/SIA Alternative to Recipe- Specific Testing of 17 Nov 2011. Available in EPA docket EPA-HQ-OAR2011-0028.

SIA (2012a). Report to EPA on Etch Factor Proposal for Fab GHG Emissions Reporting. February 28, 2012. 

Available in EPA docket EPA-HQ-OAR-2011-0028.

SIA (2012b). International SEMATECH Manufacturing Initiative Environmental Safety and Health 

Technology Center Etch Process Equipment Emissions Characterization Data. February 6, 2012. Available 

in EPA docket EPA-HQ-OAR-2011-0028.

SIA (2012c). SIA’s Response to EPA Etch Alternative Proposal, April 9, 2012. Available in EPA docket EPAHQ-OAR-2011-0028.

SIA (2012d). SIA Briefing Paper on Abatement Issues: Destruction Removal Efficiency (DRE). January 10, 

2012. Available in EPA docket EPA-HQ-OAR-2011-0028.

SIA (2012e). Questions for SIA on Abatement System DRE, Uptime, and Related Requirements. March 5, 

2012. Available in EPA docket EPA-HQ-OAR-2011-0028.

SIA (2012f). SIA Revised Proposal to Amend the Apportionment Model Validation Criteria in 40 CFR 

98.94(c).March 15, 2012. Available in EPA docket EPA-HQ-OAR-2011-0028.