About the Presenter

1. Inventory Management in Semiconductor Manufacturing Supply Chains (and Beyond): Insights Gained from a Process Control Perspective Daniel E. RiveraControl Systems Engineering LaboratoryDepartment of Chemical and Materials EngineeringIra A. Fulton School of EngineeringArizona State Universitydaniel.rivera@asu.edu

2. About the Presenter • Born and raised in San Juan, Puerto Rico • Education • B.S. ChE degree from the University of Rochester (1982) • M.S. ChE degree from the University of Wisconsin (1984) • Ph.D. from Caltech (1987) • Positions: • Associate Research Engineer, Shell Development Company, Houston, TX (1987-1990) • Associate Professor, Arizona State University, (1990 - present)

3. Control Systems Engineering Laboratory Projects • Chemical Process Control. • American Chemical Society-Petroleum Research Fund: “Constrained Multisine Inputs for Plant-Friendly Identification of Chemical Processes” • Honeywell Intl. Foundation: “Control Systems Engineering Laboratory” • Supply Chain Management. • National Science Foundation: “GOALI: Process Control Approaches to Supply Chain Management in Semiconductor Manufacturing” • Intel Research Council: “Supply Chain Management Research Using Process Control Approaches” “Improving Short-term Demand Forecasting in Supply Chain Management” • Behavioral Health. • NIH-NIDA (subcon via Penn State): “Control Engineering Approaches to Adaptive, Time-Varying Interventions in Drug Abuse Prevention”

4. http://www.fulton.asu.edu/~csel

5. Presentation Outline • Control engineering basics review • Supply Chain Management (SCM) as a process control problem • Application to SCM in semiconductor manufacturing • Adaptive interventions in drug abuse prevention • Summary and conclusions

6. What to take with you from this talk • The transfer of variance from a valuable system resource to a less expensive one is an important outcome of well-designed control systems, in any application setting. • Both feedback and feedforward strategies are needed in the design of effective control systems for delayed, nonlinear, stochastic plants. • Process control ideas have significant application in diverse problem settings, for example: • supply chain management for semiconductor manufacturing, and • adaptive interventions in behavioral health • Prepare yourself for life-long learning, since you may very well work on problems you have never imagined (in a not-too-distant future).

7. Control Engineering • Control engineering is a broadly-applicable field that spans all areas of engineering: • Chemical • Electrical • Mechanical and Aerospace • Civil / Construction • Industrial • Biomedical • Computer Science and Engineering • Control engineering principles play a role in everyday life activities.

8. Control Engineering (Continued) Considers how to manipulate or adjust system variables so that its behavior over time is transformed from undesirable to desirable, • Open-loop: refers to system behavior without a controller or decision rules (i.e., MANUAL operation). • Closed-loop: refers to system behavior once a controller or decision rule is implemented (i.e., AUTOmatic operation).

9. Open-Loop (Manual) vs. Closed-Loop (Automatic) Control Open-Loop “Manual” Closed-Loop “Automatic”

10. An Improved Closed-Loop System(Dual Climate Control)

11. An Industrial Process Control Problem are needed to see this picture. QuickTime™ and a BMP decompressor Objective: Use fuel gas flow to keep outlet temperature under control, in spite of occasional yet significant changes in the feed flowrate.

12. The “Shower” Control Problem Disturbances: Inlet Water Flows, Temperatures Controlled: Temperature, Total Water Flow The presence of delay or “transportation lag” makes this a difficult control problem Manipulated: Hot and Cold Water Valve Positions

13. Feedback and Feedforward Control Strategies • In feedback control strategies, a controlled variable (y) is examined and compared to a reference value or setpoint (r). The controller issues actions (decisions on the values of a manipulated variable (u)) on the basis of the discrepancy between y and r (e = r - y, the control error). • In feedforward control, changes in a disturbance variable (d) are monitored and the manipulated variable (u) is chosen to counteract anticipated changes in y as a result of d.

14. Flow setpoint Controller Temp. setpoint T Sensors F Actuators Shower Problem: Automatic Feedback Control Controlled: Temperature, Total Water Flow Disturbances: Inlet Water Flows, Temperatures Manipulated:Hot and Cold Water ValvePositions Cold Hot

15. d Pd ec = r - ym u r y + + C P - n + ym Closed-Loop Feedback Control “Block Diagram” Disturbances: Inlet Water Flows, Temperatures Manipulated: Hot and Cold Water Valve Positions Reference: Desired Temperature, Total Water Flow Controlled: Temperature, Total Water Flow sensor noise C = Controller P = Plant Model/“Transfer Function” Pd = Disturbance Model/“Transfer Function”

16. From Open-Loop Operation to Closed-Loop Control Temperature Deviation (Measured Controlled Variable) Open-Loop (Before Control) Hot Water Valve Adjustment (Manipulated Variable) Closed-Loop Control The transfer of variance from an expensive resource to a cheaper one is one of the major benefits of engineering process control

17. Factory Warehouse Retailer R F W Supply Chain Management • A supply chains consist of interconnected entities (e.g., factories, warehouses, and retailers) which transform ideas and raw materials into delivered products and services

18. Motivation • In the modern economy, products do not simply compete against other products; supply chains compete against other supply chains. • Billions of dollars in potential savings by eliminating supply chain inefficiencies (PriceWaterHouseCoopers, 2000; Kempf, 2004) • An effective SCM system can • Improve an enterprise’s agility to respond to market upturns (and downturns) • Increase revenue while reducing manufacturing and transportation costs. • Eliminate excess inventories and reduce safety stocks • Lower lead times and improve customer satisfaction

19. The Business Literature Can Inspire a Control Engineering Approach • The “bullwhip” effect (Lee et al., "Information Distortion in a Supply Chain: The Bullwhip Effect", Management Science 43(4) 546, 1997); demand distortion caused by variance amplification of orders upstream in the supply chain • This and similar terminology highlight issues relating to stability and performance of a dynamical system, which merit a control-oriented approach. • Not strictly an engineering/scientific problem: financial, organizational, and social issues come into play in this problem.

20. “Bullwhip” Effect

21. LT Demand Supply Chain Inventory Management as a “Level” Control Problem ORDER DECISIONS/STARTS CTL production time; also known as throughput time) Starts (Manipulated) Net Stock (Controlled) (Disturbance) d delivery time) Meet demand (with forecast possibly given f days beforehand) for a node with day production (or order fulfillment) time and d delivery time.

22. LT Demand Feedback-Only Inventory Control Problem Starts (Manipulated) production time) CTL Net Stock (Controlled) (Disturbance) d delivery time) In the feedback-only control problem, ordering decisions are calculated based only on perceived changes to “level” (e.g., net stock or equivalent variable).

23. LT Demand Single Node Inventory Problem Combined Feedback/Feedforward Control Starts (Manipulated) production time) Demand Forecast (known f days beforehand) LIC Net Stock (Controlled) (Disturbance) d delivery time) In the combined feedback/feedforward problem, a demand forecast is used for feedforward compensation.

24. 3DoF Internal Model Control Results(random unforecasted demand at t = 90) Feedback-only Combined FB/FF f = 20, d = 2, f = 1, r = 1, d = 1, nr=1, nd=3, nff=2

25. The ASU-Intel SCM Project Team Involves multiple faculty and graduate students from various departments in Engineering and Mathematics • Dept. of Mathematics, CLAS: • Professors Dieter Armbruster, Matthias Kawski, Christian Ringhofer and Hans Mittelmann; Eric Gehrig (Ph.D. student), Dominique Perdaen, Ton Geubbels (Visiting Researchers from TU-Eindhoven, The Netherlands). • Chemical Engineering, Fulton School: • Prof. Daniel E. Rivera; Wenlin Wang and Jay D. Schwartz (Ph.D. students), Michael D. Pew (UG student), and Asun Zafra Cabeza (Visiting Researcher from the University of Seville, Spain) • Computer Science and Engineering, Fulton School • Prof. Hessam Sarjoughian; Donna Huang and Weilong.Hu (Ph.D. students) • Intel collaborators: • Karl G. Kempf, Kirk D. Smith, Gary Godding, John Bean, Mike O’Brien

26. Proposed Architecture The Outer Loop Problem strategic planning Validation inventory planning goals goals tactical execution limits Prediction simulation The Inner Loop Problem

27. Semiconductor Manufacturing Process

28. Fluid Analogy for Single Fab/Test1, Assembly/Test2 and Finish Nodes

29. Modeling Issues and Challenges • The manufacturing process displays long throughput times (TPT) which are stochastic and nonlinearly dependent on load • Yields are also stochastic • There is an error between the forecasted and actual demand, which is also stochastic • Additional problem features include package dynamics, stochastic splits in die properties, and multi-factory issues involving cross-shipments, shared capacity, and correlated demands.

30. Fab/Test Manufacturing Node Dynamics Load Outs Throughput Time Starts Load Time

31. Model Predictive Control (Inventory Levels, WIP) (Actual Demand) (Forecasted Demand) (Previous Starts) (Future Starts)

32. Model Predictive Control Advantages • Ability to handle large multivariable systems • Ability to enforce constraints on manipulated and controlled variables • Effective integration of feedback, feedforward controller modes; ability to incorporate anticipation • Novel formulations (such as hybrid MPC) enable the application to systems involving both discrete-event and continuous variables.

33. X Number of Die Slow devices Fast devices I31 I30 C40 C41 M40 M40 E1 E2 E3 D1 D2 D3 Case Study: Assembly/Test2 Stochastic Split Problem Fab/Test1 C35 M10 The outcome of the Assembly/Test2 process is stochastic in terms of the number of fast and slow devices that result. Fast devices can be used to make high speed products (C37). Slow devices can be used to make low speed products (C39). I10 Assmbly/Test2 C36 Speed M20 I20 I21 C90 C38 C37 C39 M30 M30 M30 Fin/Pack

34. A/T2 Load Finishing Load Case 2: No Move Suppression F/T1 Starts CW (Fast) Reconfiguration Starts CW (Slow)

35. A/T2 Load Finishing Load Case 2: With Move Suppression [10 10 10 0 10] F/T1 Starts CW (Fast) 43.2% variance reduction 98.9% variance reduction 69.3% variance reduction CW (Slow) Reconfiguration Starts 4.7% variance reduction 51.5% variance reduction

36. Customer Service Comparison No Move Suppression With Move Suppression Unfilled Orders: 4.62% Unfilled Orders: 0.34% Fast Device Backlog Fast Device Backlog Unfilled Orders: 7.41% Unfilled Orders: 2.38% Slow Device Backlog Slow Device Backlog

37. D1 E1 E2 D2 D3 E3 E1 D1 E2 D2 E3 D3 “Combination” Problem M11 C37 C35 C36 C38 M10 M51 M50 I50 I51 I10 I11 C40 C42 C41 C39 M20 M21 C90 C91 I20 I23 I21 I22 C46 C43 C45 C44 C47 C48 M30 M30 M31 M31 M31 M30 I33 I30 I31 I31 C49 C52 C51 C50 M41 M40 M40 M41

38. = Mats Mfg T2-1 Fin1 Asm1 33 28 = Inv Hold 4.1 5.1 3.1 Box1 7.1 = Prod Mfg 37 34 3.2 18 = Transport 7.2 39 43 B 6.1 20 24 40 44 7.3 pp 7.4 pp vend7 vend8 11 3.3 pp 3.4 pp Fab1 P1 46 T1-1 P1 7 41 6.2 2.1 45 C vendor3 vendor4 42 7.5 vendor1 21 12 25 1 38 35 1.1 si 3.5 2 19 A 7.6 Box2 13 3 T2-2 Fin2 Asm2 8 29 Fab2 P1,P2 T1-2 P1,P2 14 4.2 5.2 2.2 3.6 36 F 4 30 15 5 1.2 si 3.7 3.5 6 vendor2 16 9 Fab3 P2 T1-3 P2 vendor5 vendor6 26 22 2.3 3.8 pp 3.9 ram 10 17 27 23 3.11 3.10 31 D T2-3 6.3 Asm3 4.3 3.12 32 A “Small” Semiconductor Mfg Problem E Blue = Intel Red = Mat. Sub. Green = Cap. Sub.

39. Adaptive Interventions • Adaptive interventions individualize therapy by the use of decision rules for how the therapy level and type should vary according to measures of adherence, treatment burden and response collected during past treatment. • Adaptive interventions represent an important emerging paradigm for prevention and treatment of chronic, relapsing disorders, such as drug and alcohol abuse, depression, hypertension, obesity, and many other maladies. • Also known as stepped care models, dynamic treatment regimes, structured treatment interruptions, and treatment algorithms.

40. Home Counseling-Parental Function Intervention • Based on the Fast Track Program (a multi-year intervention designed to prevent conduct disorders in at-risk children). • Parental function (the tailoring variable) is used to determine the frequency of home visits (intervention dosage) according to the following decision rules: • - If parental function is “low” the intervention dosage should correspond to weekly home visits, • - If parental function is “average” then intervention dosage should correspond to bi-weekly home visits, • - If parental function is “high” then intervention dosage should correspond to monthly home visits.

41. Parental Function Feedback Loop Block Diagram* (to decide on home visits for families with at risk children) Disturbances Clinical Judgment + Goal Intervention I(t) Outcomes + Decision Rules Process ReviewInterval If PF(t) is “Low” then Weekly Home Visits If PF(t) is “Medium” then Bi-Weekly VisitsIf PF(t) is “High” then Monthly Home VisitsIf PF(t) is “Acceptable” then No Visits Tailoring Variable Estimation + Reliability/ Measurement Error + Estimated Parental Function PF(t) *Based on material from Collins, Murphy, and Bierman, “A Conceptual Framework for Adaptive Preventive Interventions,” Prevention Science, 2004.

42. LT Depletion Parental Function Feedback-Only Control Problem I(t) (Manipulated) CTL PF(t) (Controlled) D(t) (Disturbance) In the feedback-only control problem, intervention dosages are calculated based only on perceived changes to “inventory” (parental function PF(t)).

43. Summary and Conclusions • The transfer of variance from a valuable system resource to a less expensive one is an important outcome of a well-designed control system, in any application setting. • Both feedback and feedforward strategies are needed in the design of effective control systems for delayed, nonlinear, stochastic plants. • Process control ideas have significant application in diverse problem settings, for example: • supply chain management for semiconductor manufacturing, and • adaptive interventions in behavioral health • Prepare yourself for life-long learning, since you may very well work on problems you never imagined (in a not-too-distant future).