Applying LCA into Decision Making

1. Applying LCA into Decision Making Nina Chen (yuechen@mit.edu) Greg McRae (mcrae@mit.edu) Department of Chemical Engineering MIT

2. Outline • Motivation and scope of study • Process-product Input Output (PIO)-LCA mechanism • Case studies • Value of information • Conclusion

3. Key Environmental Challenges and Needs · More quantitative environmental metrics · Systematical evaluation approach to environmental impacts · Database that includes important chemical properties ·Consideration of uncertainty in the environmental evaluation ·Rapid environmental evaluation · Life Cycle Assessment (LCA) beyond the factory · Dynamic simulation of the processes · Advanced process control, sensing and metrology · Multi-objective optimization and decision support

4. Scope • Develop methodologies and metrics for rapid economic and environmental evaluation • Integrate the treatment of uncertainties into decision making about alternative technologies • Identify opportunities for creating ‘win-win’ situations Strategy • Focus on understanding uncertainty and processes • Use existing PIO-LCA method at different stages • Explore value of information

5. Frequently Encountered Issues in Life Cycle Analysis • Large amount of data are required • Large uncertainties are imbedded in • Environmental information ~1 order of magnitude in air pollutant emission factors 2 ~ 3 orders of magnitude in cancer toxicity indicators 3 ~ 6 orders of magnitude in non-cancer toxicity indicators • Process information New technologies Unknown equipment Upstream information incomplete • Time and resources do not allow indefinite refining of data and model What Shall We Do?

6. Life Cycle Analysis Model of This Work Upstream & Downstream Emissions, Material and Energy Usage Weighting Factors Flow Rates Human Toxicity Global Warming Effect Ozone Depletion Effect Respiratory Effect … Products Byproducts Chemical Energy Water Waste Input Output LCA Model Design Decisions Emissions Process Model Impact Indicator Environmental Performance Yield Process Time … Human Exposure Compliance with Regulations Environmental Concentration Fate, Transport, and Exposure Model Environmental Properties Chemical Properties Exposure Properties Alternative Designs

7. Components of an Environmental Valuation Model* Characterization Weighting Activity Emission Factors Factors Factors Eij Hik Greenhouse Greenhouse Methane Reforming effect effect wk CO2 Acid Acid CO Deposition Deposition 2 NO x Environmental SO Carcinogen Impact Carcinogen 2 Indicator W Exposure Exposure N O 2 Energy Cu CVD CO Generation Process VOC Photochemical Photochemical smog smog CH 4 PM ... Ozone Ozone HCHO depletion depletion ... ... å å Precursor Generation W = w H Eij k ik k i ... * Cano-Ruiz 2000

8. Coal Production Coal Coal-fired Plant Gas Gas Production Gas-fired Plant Hydroelectric Plant hfac Synthesis Raw Materials hfac Cu Cu1(hfac)(tmvs) Synthesis Cu Refining Cu Mining tmvs Synthesis tmvs CH4 Methane Reforming Nature Gas Production Model Input One: Usage Matrix (B) Electricity Usage Matrix B Electricity Cu CVD Cu Film Cu1(hfac)(tmvs) H2

9. Coal Production Coal Coal-fired Plant C Gas Gas Production Gas-fired Plant Hydroelectric Plant hfac Synthesis Raw Materials hfac Cu Cu1(hfac)(tmvs) Synthesis Cu Refining Cu Mining tmvs Synthesis tmvs CH4 Methane Reforming Nature Gas Production Model Input Two: Fabrication Matrix (C) Fabrication Matrix Electricity Cu CVD Cu Film Cu1(hfac)(tmvs) H2

10. F Coal Production Coal Coal-fired Plant 57% 9% Gas Gas Production Gas-fired Plant 11% Hydroelectric Plant hfac Synthesis Raw Materials hfac Cu Cu1(hfac)(tmvs) Synthesis Cu Refining Cu Mining tmvs Synthesis tmvs CH4 Methane Reforming Nature Gas Production Model Input Three: Market Share Matrix (F) Market Share Matrix Electricity Cu CVD Cu Film Cu1(hfac)(tmvs) H2

11. SO2, CO2, PM10… E Coal Production Coal Coal-fired Plant Gas Gas Production Gas-fired Plant H2, Cu1(fac) Hydroelectric Plant hfac Synthesis Raw Materials hfac Cu Cu1(hfac)(tmvs) Synthesis Cu Refining Cu Mining tmvs Synthesis tmvs CH4 Methane Reforming Nature Gas Production Model Input Four: Emission Matrix (E) Emission Matrix Electricity Cu CVD Cu Film Cu1(hfac)(tmvs) H2

12. Model Input Five: Characterization Matrix (H) • Characterization matrix (H) • Large uncertainties imbedded in the values GWP100 Respiratory Human Toxicity Effect Potential (non- cancer) … kg CO2 equivalent/kg kg PM10 equivalent/kg DALYs/kg Unit 1 -23.3 0.15 4.21E-9 -8.3 1 CO2 kg SO2kg PM10kg Based on willingness to pay … Valuation Factor w$ 3e-2 40 85000

13. Mathematical Model • Model Input Six: Price vector (p) • Allocation matrix (G): for multiple product processes • Throughput matrix (D) Dji = FjiGji • Direct product requirement (qdirect) qdirect = (I + BD)d • Total product requirements q = (I + Aprod + AprodAprod + AprodAprodAprod + …)d = (I – Aprod)-1d where Aprod BD • Gji: the amount of throughput of process j that is attributed to one unit of product i made in process j Dji: the amount of throughput of process j that is attributed to the demand of one unit of product I at current price and market share

14. Mathematical Model • Total process throughput requirements (x) x = Dq • Life cycle environmental exchanges inventory (e) e = Ex • Impact valuation by process (process) process = Diag(x) ET H w • Impact valuation by emission (emission) emission = Diag(e) H w

15. hfac Synthesis Raw Materials hfac Cu Cu1(hfac)(tmvs) Synthesis Cu Refining Cu Mining tmvs Synthesis tmvs CH4 Methane Reforming Nature Gas Production A Smaller Case • Eighteen processes • Fourteen products • One hundred and two emissions • Seven environmental impacts … Nature Gas Production Coal Gas-fired Plant Gas Coal-fired Plant Coal Production Hydroelectric Plant Electricity Cu CVD Cu Film Cu1(hfac)(tmvs)

16. Case Study—Cu CVD Pressure Sensor and Controller The process model is provided by University of Maryland. Film Thickness Sensor and Controller Precursor Cu1(hfac)(tmvs) Wafer Heater Temperature Sensor and Controller Scrubber Carrier Gas Hydrogen Sensor Path Control Path

17. Analysis Results of the Environmental Model • When uncertainties are considered, power generation still contribute to a significant part of environmental impact. • Large uncertainty in coal-fired power plant and oil-fired power plant is from the uncertainty in PM10 effect and CO2 effect in GWP

18. Merits of NF3 High disassociation rate High removal rate High etch rate Drawback of NF3 High cost Second Case Study – Chamber Cleaning with NF3 or F2? RF Power SiO2 Deposited on Wall NF3/F2, Ar, N2 F, NF, NF2, Ar Plasma Generator N2, F-, NF+ … SiF4 F, F2, N2, SiF4, O2… O2 SiF4 F, F2, O2, N2, SiF4… CVD Reaction Chamber • Merits of F2 • Low cost • Drawbacks of F2 • High toxicity • High reactivity • On-site generation creates explosive H2 Comparison criterion considered: Life cycle impacts given the same cleaning performances

19. Process Modeling with Kinetics • Lumped kinetics and Perfectly Stirred Tank Reactor (PSTR) model • Key assumptions • Free electrons are generated mainly by ionization Ar+e --> Ar++2e • Electron loss and production are linear to electron concentration • Diffusion of electrons dominates the transport of electrons. NF3 + e  NF2 + F + e k3=2.06E-17 Te1.7exp(-37274/Te) NF2 + e  NF + F  + e k2=1.57E-17 Te1.8exp(-27565/Te) NF + e  N + F  + e k1=1.57E-17Te1.8exp(-27565/Te) F2 + e  F- + F k =1.02E-5Te-0.9exp(1081.8/Te) 4F + SiO2 SiF4 + O2

20. Process Modeling with Stoichiometrics where for NF3 cleaning for F2 cleaning Driving forces of LCA impacts: Cleaning gas usages Energy consumptions Cleaning Gases Energy • Fluorine Utilization Yield: F% ~ uniform(10-5, 0.6) • Energy Utilization Yield: E ~ uniform(10-10, 0.6) • Cleaning Time: t (s) ~ uniform(6E-4, 1200)

21. Comparison of Relative Impacts of GWP of Two Models Process Model with Kinetics 1.9 3.3 … Process Model with Stochiometrics Relative GWP of NF3 Process to F2 Process • 2~3 orders of magnitude of uncertainties in inputs does not necessarily leads to low confidence in decision • Increase of modeling detail decreases the uncertainty of the outputs • But the decision is still the same – F2 is better! • Required confidence level should determine depth of analysis

22. Process Modeling Hierarchy and Resource Needs Process Model Hierarchy Distributions of Yield Resources Needed 1 Simple stoichiometric yield 1 2 Lumped kinetics (3 reactions) 10 3 Detailed kinetics (60 reactions) 100 4 Model based experiments 1000

23. 15% … Right Procedure of Analysis Key Parameters Problem Analysis Models Relative GWP 4F + SiO2 SiF4 + O2 1. Stoichiometric LCA Gas Usage (mol) Decision: go to next level? Refine cleaning process model NF3 + e  NF2 + F + e NF2 + e  NF + F  + e NF + e  N + F  + e F2 + e  F- + F 2. Simple Kinetics LCA Further refine cleaning process model Decision: go to next level? 3. Detailed Kinetics 163 Gas Phase Reactions in Plasma Generator

24. Process Modeling vs. System Boundary • Depth of process modeling and width of system boundary are complementary to each other. • Based on existing knowledge, choose appropriate direction System Boundary 85% Life Cycle >99% Chemical Industry >99% Semiconductor Industry 12 Month Effort? 6 Month Effort 2 Month Effort Downstream Treatment >95% N/A >99% Confidence Level in Distinguishing NF3 and F2 Cleaning 1 hr Effort N/A >95% Cleaning Tools >95% Process Modeling Level Stoichiometry Simple Kinetics Detailed Kinetics

25. Framework of Decision-Making Process Generate new alternatives Refine model, collect more data, increase data accuracy… Ranking and Sensitivity Analysis No Alternative Technologies: NF3 vs. F2 Cu CVD vs. Cu plating … Is info enough for decision? Economic Impacts Model Process Model Environ. Impacts Model Yes Uncertainty Analysis Do nothing, or change to alternative

26. Future Plan – Value of Information (VOI) • A simple example: is it worthy to buy $1M equipment for testing? More Research Current State: 50% sure COO NF3 cleaning = 3 COOF2 cleaning 90% sure (p) Continue NF3 Cleaning Cost of NF3 cleaning (90%) Adopt F2 Cleaning COOF2 cleaning if works well More Research (10%) COOF2 cleaning if not work well More Research 10% sure (1-p) Continue NF3 Cleaning Cost of NF3 cleaning (10%) Adopt F2 Cleaning COOF2 cleaning if works well (90%) COOF2 cleaning if not work well Continue NF3 Cleaning Cost of NF3 cleaning (50%) COOF2 cleaning if works well Adopt F2 Cleaning (50%) COOF2 cleaning if not work well

27. UNCERTAINTY = IGNORANCE Conclusions and Key Points • Large uncertainty in the inputs does not necessarily lead to low confidence in decisions. • PIO-LCA combines both the merits of EIO and engineering design method • Hierarchical modeling in combination with uncertainty analysis are efficient ways to support the decision making and resource allocation process. • VOI may give direction on resources allocation.

28. Acknowledgements • Laura Losey • David Bouldin, Mike Kasner, Tim Yeakley, and Tina Gilliland – Texas Instruments • Larry Novak – Novak Consulting, LLC • Alejandro Cano-Ruiz and Pauline Ho – Reaction Design • Daren Dance – WWK • Joe Van Gompel – BOC Edwards • Holly Ho – TSMC, Taiwan • McRae Group – MIT • Gleason Group – MIT • Engineering Research Center for Environmentally Benign Semiconductor Manufacturing – NSF/SRC.