VIRTUAL METROLOGY INTEGRATION AND PERFORMANCE MONITORING OF RUN-TO-RUN EWMA CONTROLLERS IN SEMICONDUCTOR MANUFACTURING PROF. THOMAS F.

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1 NSF I-UCRC on Intelligent Maintenance Systems; ; May 15, 2012, Austin, TX VIRTUAL METROLOGY INTEGRATION AND PERFORMANCE MONITORING OF RUN-TO-RUN EWMA CONTROLLERS IN SEMICONDUCTOR MANUFACTURING PROF. THOMAS F. EDGAR

2 2 PRESENTATION OUTLINE»Motivation»Methodology»Preliminary Work»Research Plan

3 3 MOTIVATION: Manufacturers desire information of each wafer to improve the process yield. However, very few processed wafers are sent to metrology due to the associated high cost. VM predicts end-of-batch properties (metrology data) from measurable process inputs and process variables. Current R2R control used in high-mix manufacturing relies on metrology results, which has high measurement costs. However, using virtual metrology alone is subject to inaccuracies and process drifts and noises. A combination of two methods would alleviate both concerns and results in reduced costs and increased accuracy.

4 4 RUN-TO-RUN CONTROL Recipe settings are updated based on measurements made to the previous lot. 5/11/2012 Chemical Engineering, UT-Austin

5 5 VM-ASSISTED EWMA CONTROL»Virtual metrology data with high accuracy can be fed into the run-to-run controller instead of final metrology results several runs later»recipe settings can be updated without final metrology data 5/11/2012 Chemical Engineering, UT-Austin

6 6 SHORTCOMINGS OF CURRENT VM METHODS» Researchers have proposed several approaches, but there is no standard and generalized method.» Accuracy fundamental requirement of VM.» Most semiconductor manufacturing processes suffer from process drifts and shifts. Not much VM literature addresses this issue.» No research on VM in presence of faults so far.» Metrology events need to be optimally scheduled.» Performance monitoring activities should be integrated with VM and fault detection 5/11/2012 Chemical Engineering, UT-Austin

7 7 METHODOLOGY» Comparison of control methodologies through simulation B-EWMA, VM-assisted EWMA, and EWMA-R2R under different sampling algorithms.» Reliance index metrics for VM predictions to provide additional reliability information» Model based control intelligent controller output based on metrology predictions and associated reliability level. Reliance index tracks the reliability of virtual metrology model by comparing the resulting prediction distribution against a reference distribution 5/11/2012 Chemical Engineering, UT-Austin

8 8 METHODOLOGY IMC structure of EWMA controller (with model mismatch) α k + 1 = α k + ε k + 1 θε k + - Σ + + Σ y k + d = k α + β u k + Σ Σ (IMA) a ( 1 ω) a + ( y bu ) k + 1 = k ω k k (EWMA)

9 9 PRESENTATION OUTLINE»Motivation»Methodology»Preliminary Work»Research Plan

10 10 PRELIMINARY WORK: VM-ASSISTED R2R CONTROL» Simulations studies utilizing different control methodologies and the resulting reliance index of the VM predictions. 5/11/2012 Chemical Engineering, UT-Austin

11 11 NOVEL CONTROL SCHEME SIMULATION RESULTS» Simulation studies show that VM has potential to reduce measurement costs while giving better process control 5/11/2012 Chemical Engineering, UT-Austin

12 12 PRELIMINARY WORK: EWMA CPA Optimal Suboptimal Optimal Suboptimal Incorrect Tuning Both methods ok 0.0 -C(2) 0.0 -C(2) t a) Regression result of output error e t b) Regression result of estimated noise ε -C(2) Optimal Suboptimal -C(2) Optimal Suboptimal Model Plant Mismatch New method better for detection NSF I-UCRC on Intelligent Maintenance t Sys. a) Regression result of output error e t b) Regression result of estimated noise ε

13 13 PRESENTATION OUTLINE»Motivation»Methodology»Preliminary Work»Research Plan

14 14 TASK 1: VM FOR INDUSTRIAL DATASETS Prediction of etch rate and sheet resistance for an organosilicate glass (OSG) etch process using optimal emission spectroscopy (OES) data. OES data consists of intensities for 18 different intervals of wavelengths collected every 0.1 second by an end-point detection software. Actual metrology (etch depth) was done by atomic force microscope (AFM). Wafer-level predictions of critical dimension (CD) using 38 process variables in a gate-etch process. Problem: The first two wafers had significantly different CDs than the rest of the lot.

15 15 RESEARCH PLAN» VM improvements Better accuracy: data pre-treatment and reconciliation. More robust: incorporate test-wafer metrology results into VM prediction model to account for process drifts and noises More information: develop better reliance index metrics in order to provide confidence intervals for virtual metrology results» Better reliance index formulation Current method Calculates the overlapping area between a reference distribution and the VM predictions Would result in low RI for process dynamics New method Develop a clustering based reliance index metric that integrates prediction model operating regions.» Incorporate virtual metrology reliance index information in intelligent run-to-run controller. 5/11/2012 Chemical Engineering, UT-Austin

16 16 TASK 2: RUN-TO-RUN CONTROLLER PERFORMANCE ASSESSMENT (CPA)» + Σ + + Σ - + Σ Σ (EWMA) (IMA)

17 17 POTENTIAL APPLICATION AREAS Plasma based semiconductor manufacturing Pharmaceutical manufacturing Petrochemical refinery Combined cycle power plant Electrical grid fault diagnosis Run-to-run based manufacturing industries Automotive manufacturing

18 18 SUMMARY OF PROPOSED WORK TASKS Task 1: Integration of virtual metrology with run-to-run EWMA controllers Task 2: Controller performance assessment of EWMA run-to-run controllers YEAR 1 DELIVERABLES Integrating test wafer metrology results into virtual metrology model (Task 1) Formulation of a new reliance level metric (Task 1) Test and online verification of CPA assessment method using industrial dataset (Task 2) PERSONNEL Mentors: Prof. Thomas Edgar & Industrial Partners Researchers: One graduate student per task POTENTIAL IMPACT PROJECT SCHEDULE Increased prediction accuracy from existing metrology models Better controller performance using both virtual/real metrology results Integrated and automated controller fault detection and controller performance tuning framework TASKS 1 2 YEAR 1 YEAR

19 19 BUDGET SUMMARY Year 1 Budget Year 2 Budget Input Batch Reactor Recipe update RtR controlle r Output Multi products A B B A B Tool I Tool II Tool III Method development $40K (student tuition and stipend for a year) for one student Method development $80K (student tuition and stipend for a year) for two students

20 NSF I-UCRC on Intelligent Maintenance Systems; ; May 15, 2012, Austin, TX THANK YOU