T'F' Edgar

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• T.F. Edgar
• Department of Chemical Engineering
• The University of Texas
• Austin, Texas 78712
• February 19-20, 2001
• Austin, Texas

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AgendaMonday February 19th

• 830 Coffee and Donuts
• 900 Introduction/Review of Agenda T. Edgar
• 910 PI Overviews T.Edgar
• J. Qin
• J. Rawlings
• 1010 Comparison of Process Systems Consortia T.
  Edgar
• 1040 Break
• 1100 Multi-model Robust MPC J. Wang
• 1145 Lunch (TCC Cafeteria)
• 100 Closed-Loop State and parameter
  Estimation M. Nikolaou
• Univ. of Houston
• 200 Process Modeling and Control at
  Motorola Karen McBrayer
• Motorola
• 230 Break

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AgendaMonday, February 19th

• 300 Reactive Distillation Modeling and Control
• J. Schell (UT)
• J. Peng (UT)
• S. Lextrait (UT)
• 400 Poster Session
• J. Hahn J. Schell S. Lextrait
• J. Peng H. Potrykus S. Valle-Cervantes
• Q. He J. Wang (UT) J. Wang (UW)
• 530 Adjourn
• 630 Mixer/Dinner Matts El Rancho - 2613 S. Lamar

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AgendaTuesday, February 20th

• 815 Meeting with PIs and Sponsors -
  Continental Breakfast
• 900 CPC-6 Review J. Rawlings
• 930 Recent Advances in Global
• Optimization L. Lasdon
• 1015 Break
• 1030 Hierarchical Statistical Monitoring
  S. Valle- Cervantes
• 1120 Subspace Identification Using PCA J.
  Wang (UT)
• 1150 Wrap-up T. Edgar
• 1200 Adjourn

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TWMCC - Multiple Projects

• Company JBR TFE JQ
• AMD v v v v
• Aspentech v v
• Condea-Vista v
• DuPont v v
• Equilon v
• Exxon/Mobil v v
• Fisher-Rosemount v
• KLA-Tencor v
• Motorola v

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TWMCC - Multiple Projects (Contd)

• Company JBR TFE JQ
• Pharmacia v
• SmithKline Beecham v
• Texas Instruments v
• Tokyo Electron America v in progress
• Union Carbide/Dow v
• Weyerhauser v v
• Courting Applied Materials, GE-Continental
  Controls, Chevron, DOT Products, Alliant Energy,
  SEMATECH, Matrikon, Oil Systems, Honeywell, Yield
  Dynamics

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M.S., Ph.D. Graduates(1999 - 2001)

• Student/Supervisor Destination
• K. Teague (TFE) Ph.D. (5/99) Aspentech
• C. Pfeiffer (TFE) Ph.D. (12/99) Motorola
• Q. Finefrock (TFE) Ph.D. (?/?) Still
  consulting
• J. Campbell (TFE) Ph.D. (8/99) KLA-Tencor
• M. Misra (JQ) Ph.D. (8/99) Motorola
• T. Nugroho (JQ) Ph.D. (8/99) Pertamina
• R. Bindlish (JBR) Ph.D. (12/99) Dow
• C. Rao (JBR) Ph.D. (12/99) Postdoc/Ohio
  State
• G. Scheid (JQ) M.S. (5/99) LSI Logic
• W. Cho (TFE) Ph.D. (5/01) AMD
• B. Ko (TFE) Ph.D. (5/00) ExxonMobil

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M.S., Ph.D. Graduates(1999 - 2001)

• Student/Supervisor Destination
• H. Yue (JQ) Ph.D. (5/00) Tokyo Electron
  America
• S. Middlebrooks (JBR) Ph.D. (2/01) LSI
  Logic
• C. Bode (TFE) Ph.D. (5/01) AMD
• T. Soderstrom (TFE) Ph.D.
  (2/01) ExxonMobil
• S. Alici (TFE) Ph.D. (5/01) Air Products
• J. Schell (TFE) Ph.D.
  (5/01) Interviewing
• J. Wang (JBR) Ph.D. (5/01) Interviewing
• D. Patience (JBR) Ph.D. (12/01) Interviewing
  
• S.V. Venkatesh (JBR) M.S. (5/01) ASML
  Mastools
• S. Valle Cervantes (JQ) Ph.D.
  (5/01) Interviewing
• R. Mak (JQ) M.S. (5/01) Interviewing
• S.B. Hwang (TFE) Ph.D. (5/01) Return to
  Hyundai

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Highlights (Edgar Group)

• Added two new (experienced) graduate students in
  Spring
• Six Ph.D.s to graduate in 2001
• Publication of optimization book (with Himmelblau
  and Lasdon)
• Seven students doing research at industrial sites
  during past year
• (AMD, Motorola, Condea-Vista, KLA-Tencor, Tokyo
  Electron, SEMATECH)
• ChE Department looking for systems faculty

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Six Ph.D Candidates to Graduate in 2001

• 1. Tyler Soderstrom Data Reconciliation and
  Gross Error Detection
• 2. Semra Alici Dynamic Data Reconciliation
  Using Process Simulators
• 3. John Schell Modeling and Control of Reactive
  Distillation Column
• 4. Chris Bode Run-to-run Control of
  Photographic Overlay
• 5. Sung Bo Hwang Modeling of Lithography Dry
  Development and RTCVD of SiGe
• 6. Wonhui Cho Multivariable Temperature Control
  in Rapid Thermal Processing

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Simultaneous Data Reconciliation and Bias
Detection/Identification Research Contributions

• Tyler Soderstrom
• Formulate Combined Problem as a Mixed Integer
  Programming (MIP) Problem
• Evaluate the Effects of Method Parameters Using
  Linear Steady State Models (MILP Problem)
• Incorporate Empirical Bias Detection Methods
  Directly into the Problem Framework
• Extend the Method to be Applicable to Nonlinear
  Systems and Investigate Novel Solution Techniques
  (MINLP Problem)
• Extend the Method to be Applicable to Nonlinear
  Dynamic Systems

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Dynamic Data Reconciliation via Simulation
Software (DDRSS)
Semra Alici
DDRSS Algorithm

• Two New Approaches for the Incorporation of
• Simulation Software Into Dynamic Data
• Reconciliation

HYSYS
Measurements, Initial Guesses
Model Identification

• Finite Difference Approach
• Time Series Model Approach

Simplified Model

• Identification Methods

• Parametric (Ordinary Least
• Squares, AR, ARMA,... Models)
• Multivariate Adaptive Regression
• Splines (MARS)
• Neural Networks

NLP
Constraints
Objective Func.
New Estimates
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Modeling and Control of ReactiveDistillation
Columns

• John Schell
• Novel Application of Reactive Distillation
• Kinetic, Exothermic Reaction with Stearic
  Equilibrium in Product
• Pilot Column gt 40 feet tall
• Validation of Steady State and Dynamic Models
• RADFRAC, RateFRAC, Aspen Dynamics
• Control Study
• Shows need for nonlinear control

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Run-to-run Control of Lithography Overlayand
Poly Gate Linewidth

• Christopher A. Bode
• Compiled a set of known disturbances to the
  process responsible for overlay and linewidth
  performance, and completed an analysis of
  variance to identify the significant
  disturbances.
• Identified an strategy for the control of these
  significant disturbances based upon control
  thread methodology.
• Developed a run-to run Model Predictive Control
  (MPC) method for each controlled variable.
• Deployed above to high-volume semiconductor
  manufacturing, realizing a 40 improvement in
  process capability (Cpk) for overlay and a 44
  improvement in gate linewidth Cpk.

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Rapid Thermal CVD of Si1-xGex
Sung Bo Hwang

• RTCVD of Si1-xGex using Si2H6-GeH4 chemistry
  (470oC550oC)
• Minimize diffusive mixing at interfaces using
  temperature and gas switching
• Need process optimization to get the quantum
  dots or to deposit a Ge-graded film
• DOE(Design of experiments)
• 3 factors Temperature, flow rates of GeH4
  and flow rate of Si2H6.
• Responses Growth rate, Ge concentration in
  films, and Morphology (dot size and density)
• Full factorial with 3 center points
• Neural network modeling
• Using Bayesian Network

• AFM (Morphology)
• Dot density ( on Si gt on SiO2 )

• XRD (Ge concentration and thickness)
• Ge composition ration and growth rate mainly
• depend on the reactant ratio (PGeH4/PSi2H6 )

12.68
17.75
Ge 20.48
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Modeling and Control of RapidThermal Processor

• Wonhui Cho
• Multi input/output wafer heat transport dynamic
  model based on finite difference method (FDM) and
  view factor analysis
• Closed loop parameter identification
• Nonlinear MIMO (3X3) Iterative Control
  application to temperature uniformity control
• Development of Windows NT-VME based real-time
  control system

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Reactive Distillation Project Goals(J. Schell,
S. Lextrait, J. Peng)

• Operate columns
• Alkylation (Condea Vista)
• Etherification (TAME chemistry)
• Validate existing reactive distillation
  simulators
• Aspens RADFRAC (equilibrium stage)
• Aspens RateFRAC (mass transfer rate)
• Aspen Custom Modeler
• Develop fundamental nonlinear models for
  comparison
• Future funding by Aspentech, GOALI proposal to
  NSF

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Optimization of Chemical Processes

• Second Edition
• McGraw-Hill - available January, 2001
• By Edgar, Himmeblau, and Lasdon
• ISBN 0070393591
• Major changes from First Edition
• Chapter 2 Modeling for Optimization
• Chapter 6 Unconstrained Optimization
• Chapter 7 Linear Programming
• Chapter 8 Nonlinear Programming
• Chapter 9 Mixed-Integer Programming
• Chapter 10 Global Optimization
• Chapter 15 Process Design and Operations
• Chapter 16 Planning/Scheduling/Control

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Process Systems Research Consortia(www.che.utexas
.edu/cache/trc/t_process.html/systems)

• Chemical Modeling Process Control Research
  Center, Lehigh University
• Center for Process Analytical Chemistry,
  University of Washington
• McMaster Control Consortium
• Measurement and Control Engineering Center
  (University of Tennessee, Oklahoma State)
• Center for Advanced Process Decision-Making,
  Carnegie Mellon

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Process Systems Research Consortia

• Texas - Wisconsin Modeling Control Consortium
  (TWMCC)
• Texas Tech Process Control and Optimization
  Consortium
• UC Santa Barbara Consortium
• University of Delaware Process Monitoring and
  Control Consortium
• Computer-Integrated Process Operations Consortium
  - Purdue

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Consortium Metrics

• Number of Number of Number of
• Students/Postdocs Faculty Sponsors
• Washington 26 28 33
• Purdue 28 3-4 12-15
• CMU 24 6 20
• TWMCC 28 3-4 15
• McMaster 22 4 20
• Delaware 16 3 13
• UT/OSU 15 10 16-19
• Lehigh 14 4 7
• Texas Tech 9 2 7
• UCSB 6 1 11

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General Observations

• Several operate with formal consortium agreement,
  previous or current government agency funding
  (UW, CMU, Lehigh, UT/OSU)
• Two are mostly focused on analytical measurements
  and chemistry faculty (UW, UT/OSU)
• Most use agency funds to supplement industrial
  contributions (UCSB, Delaware, Purdue, Lehigh,
  McMaster, CMU, TWMCC)
• Some allow special membership for vendors or for
  hardware/software donations in lieu of cash
  (Delaware, UCSB)
• Company fees typically range from 12,000
  (McMaster-in US) to 35,000 (UW) UCSB (Single
  prof) 5000
• Most consortia meet twice per year