SRC/ISMT FORCe:Factory Operations Research Center Task NJ-877

1. SRC/ISMT FORCe:Factory Operations Research CenterTask NJ-877 Michael Fu, Director Emmanuel Fernandez Steven I. Marcus San Jose, CA, Nov. 20-21, 2002 Intelligent Preventive Maintenance Scheduling in Semiconductor Manufacturing Fabs

2. CONTENTS • Project Overview/Status -Michael Fu, Project Director • Overview of Software Tools and Summer Internships • -Emmanuel Fernandez • Simulation Studies and Model Validation: Intel, Chandler • Jason Crabtree • Simulation Studies and Model Validation: AMD, Austin • Jose A. Ramirez • Research on Stochastic Models - Xiaodong Yao • Commercialization Plans - Emmanuel Fernandez

3. Michael Fu, Ph.D.Institute for Systems ResearchUniversity of MarylandProject Overview/Status Summary

4. Research Plan (Proposed) (1) Develop, test, and transfer software tools for optimal PM planning and scheduling; (2) Research and validate the models, methods and algorithms for software development in (1); (3) Facilitate the transfer of models, algorithms and tools to 3rd party commercial software vendors.

5. Executive Summary • “Report on Models and algorithms to cover major bottleneck tool sets in a semiconductor manufacturing fab” Delivered: • 29-Jul-2002, SRC Pub P004304. • “Preventive Maintenance Scheduling Model and Generic Implementation: Mathematical Programming Modeling Languages and Solvers” Report Delivered: • 29-Jul-2002, SRC Pub P004306. • Paper (presented at MASM 2002) Delivered: • Incorporating Production Planning into Preventive Maintenance Scheduling in Semiconductor Fabs • Two invited “Operational Models in Semiconductor Manufacturing I-II” sessions organized and chaired within Applied Probability Cluster at INFORMS 2002 Annual Meeting.

6. Executive Summary • Two presentations delivered at “Operational Models in Semiconductor Manufacturing I-II” sessions, INFORMS 2002 Annual Meeting. • Developed and Beta tested a software tool (PMOST) for: • Generic Scheduling Simulation Engine • Generic Implementation of PM Scheduling Algorithm • Beta Version and Draft Report ready. • Two summer internships (AMD & Intel) successfully completed. • AMD in process of making tools operational in fab. • Stochastic PM Planning Models (Analytical and Simulation-Based) research advanced. Ph.D. dissertation (Yao) near completion. • Commercialization feasibility discussions: Brooks, Adexa, Ibex Processes. • Worked with Swee Leong in obtaining seed funding from NIST • Teleconferences with Liaisons

7. Industrial Liaisons • Matilda O'Connor, Advanced Micro Devices, Inc. • Nipa Patel, Advanced Micro Devices, Inc. (sign in SRC list) • Man-Yi Tseng, Advanced Micro Devices, Inc. • Ying Tat Leung, IBM Corporation • Wayne F. Carriker, Intel Corporation • Robin L. Hoskinson, Intel Corporation • Ben-Rachel Igal, Intel Corporation • Mani Janakiram, Intel Corporation • Madhav Rangaswami, Intel Corporation • Sidal Bilgin, LSI (sign in SRC list) • Ramesh Rao, National Semiconductor Corporation • Jan Verhagen, Philips Corporation(sign in SRC list) • K.J. Stanley, Motorola (sign in SRC list) • Gurshaman S. Baweja, Texas Instruments Incorporated • Marcellus Rainey, Texas Instruments Incorporated

8. Industrial LiaisonsNew Members • Jason Wang, Taiwan Semiconductor Manufacturing Company • James Yang, Taiwan Semiconductor Manufacturing Company • Giant Kao, Taiwan Semiconductor Manufacturing Company • Jacky Fan, Taiwan Semiconductor Manufacturing Company • Russell Whaley, LSI Logic(sign in SRC list)

9. Research Personnel Faculty: • Michael Fu, Maryland • Steve Marcus, Maryland • Emmanuel Fernandez, Cincinnati Students: • Xiaodong Yao, Maryland (near completion of PhD) • Ying He, Maryland (PhD completed, summer 2002) • Jiaqiao Hu, Maryland (2nd year PhD) • Jason Crabtree, Cincinnati (near completion of MS) • Jose Ramirez, Cincinnati (2nd year PhD) • Sumita Jagannathan, Cincinnati (2nd year MS)

10. Project Management • Weekly Site Meetings at Maryland & Cincinnati • Weekly Teleconferences between Maryland & Cincinnati • Monthly Teleconferences With Liaisons and PI’s • Project Website • http://www.smitlab.uc.edu

11. Task Description(Proposed) Year 1-Implementing the PM scheduling algorithm; developing, distributing, and analyzing PM practice survey to drive PM planning models and algorithms; literature review of research on analytical and simulation-based models for PM planning with production considerations. Year 2 -Developing generic implementation platform for PM scheduling algorithm to facilitate possible transfer to 3rd party software provider; developing, testing, and validating PM planning models and algorithms. Year 3 –Implementing PM planning models and algorithms, validating and testing; training workshop to facilitate transfer to 3rd party software vendor.

12. Deliverables to Industry (Proposed) 1.Survey of current PM practices in industry (Report) (P:15-DEC-2001) 2. Models and algorithms to cover bottleneck tool sets in a fab(Report) (P:31-MAR-2002) 3.Simulation engine implemented in commercially available software, with case studies and benchmark data (Report) (P:30-SEP-2002) 4. PM planning/scheduling software tools, with accompanying simulation engine (Software, Report) (P:30-JUN-2003) 5. Installation and evaluation, workshop and consultation (Report) (P:31-DEC-2003)

13. Overview SRC Deliverables List • Annual review presentation (Completed: 12-Dec-2001), Presentation(s) and Related Publication(s): P003262 • Survey of current PM practices in industry, conducted via Web and electronic mail (Completed: 17-Jan-2002), Presentation(s) and Related Publication(s): P003461 • Review presentation (Completed: 26-Apr-2002), Presentation(s) and Related Publication(s): P003862 • Report on models and algorithms to cover bottleneck tool sets in a fab (Completed: 29-Jul-2002), Presentation(s) and Related Publication(s): P004304,  P004306 • Report on the simulation engine implemented in commercially available software, covering major bottleneck tool sets (Planned: 31-Dec-2002) • Annual review presentation (Planned: 31-Jan-2003) • Report on the intelligent PM scheduling software tools, with accompanying simulation engine (Planned: 30-Jun-2003) • Final report summarizing research accomplishments and future direction (Planned: 31-Dec-2003) • Report on the installation and evaluation services for transfer to semiconductor industry and 3rd party software vendors (Planned: 31-Dec-2003)

14. To do in 2002-2003 • Complete and transfer report and software: • PMOST 1.0. • Graphical interface and output • Refine and extend model: objective function, etc. • Investigate potential feasibility studies and implementation at other SRC/ISMT member companies • Continue discussions on transfer of models/software to commercial vendors • Continue development of (stochastic) MDP and queueing models, in conjunction with simulation-based approaches, for PM planning problem.

15. Emmanuel Fernandez, Ph.D.ECECS DepartmentUniversity of CincinnatiOverview of Software Tools and Summer Internships Summary

16. Outline • PMOST: Preventive Maintenance Optimal Scheduling Tool • Summer Internships: • Jason Crabtree at Intel, Chandler, AZ • Jose Ramirez at AMD, Austin, TX.

17. “Recent Accomplishments” List: April 2002 Review • PM Best Practices Survey report delivered (appended). • Paper for MASM delivered (appended). • Progress on development of generic implementation of PM Scheduling Algorithm and IT implementation: new deliverable (report, draft appended). Completed Beta Version, report draft • Progress on development of generic ASAP simulation engine of PM Scheduling Algorithm for key tools. Completed Beta Version, report draft • Investigated analytical (MDP models and queueing models) and simulation-based approaches to PM planning: dissertation.Done • Two student internships at member company for this summer, one or two more possible: Two Successfully Completed • Test and validated with ASAP fab simulation and real data, • Implement PM scheduling algorithm, • integrated with MES and PM monitoring databases.

18. Overview SRC Publications List Nanostructure & Integration Sciences Deliverable Report: Report on Models and Algorithms to Cover Major Bottleneck Tool Sets in a Semiconductor Manufacturing Fab; X. Yao, M. Fu, S. Marcus and E. Fernandez; Univ. of Maryland; 29-Jul-2002; 4pp.; (Pub P004304); Task 877.001[Abstract] [Document]   (316k) Presentation: Preventive Maintenance Scheduling Model and Generic Implementation, Mathematical Programming Modeling Languages and Solvers; X. Yao, M. Fu, S. Marcus and E. Fernandez; Univ. of Maryland; 29-Jul-2002; 6pp.; (Pub P004306); Task 877.001[Abstract] [Document]   (786k)

19. PMOST Overview • Preventive Maintenance Optimal Scheduling Tool • (PMOST) • Beta Version • Completed ahead of schedule in preparation for internships • Software implementation of our algorithms ready to transfer to fab systems • Standard data structures and formats defined • Can be used in conjunction with ASAP fab models as a simulation engine • Report ready, data structures and formats report ready • Refinements and user interface modification being done • Demo

20. PMOST Overview • PMOST accepts a set of parameters, e.g., • planning horizon, • number of resources for the PM tasks, • cost coefficient related to the PM tasks, etc.. • PMOST obtains an optimal solution for that problem via the use of mathematical programming solvers for Linear Programming/Mixed Integer Programming problems. The PMOST system was designed to work with different types of mathematical programming solvers, such as IBM OSL, CPLEX. • The system requires a set of data files, defined under specific (standard) formats, used in the optimization process. • Another characteristic of PMOST is the possibility to generate scheduling files containing the optimal schedule (PM orders) to perform simulation with AutoSched AP, and the generation of Mathematical Programming System files that can be used as input to mathematical programming solvers.

21. PMOST Diagram

22. PMOST • The “START” process, implemented within the main.c file, configures the optimization problem. It also orchestrates all function calls throughout the entire execution cycle. • Once the system is initialized, a process of reading (“READ INPUT DATA”) is performed over the tool/PM file (.fam), PM schedule files (.sch) and work in process files (.wip). • There is an additional file write_sch_file.c that can be used when dealing with ASAP simulations. This file takes a pm schedule from an ASAP simulation customization and writes a schedule file in the .sch format for use with the software. • Several files including .fam, .pm, .sch and .wip files have a customized format designed by us with the objective to facilitate the data handling.

23. PMOST • The “Write MPS file” block produces the MPS format file corresponding to the data related to the current optimization problem. This block, written in C, makes our software independent from any specific the mathematical programming solver. • After the MPS file is generated a process to obtain the optimized schedule starts, this is represented by the “LP/MIP SOLVER” block in the diagram. • In this case a call is made from the main program (main.c) to the current/selected solver in the system (IBM OSL, CPLEX, etc). The solver accepts the MPS file as its input and generates a solution for the mathematical programming problem. • The solution file is called “pm_solution.txt”. If the user wants to use the optimal solution in an ASAP simulation, then the functions in the file write_pm_order_file.c can be used to create a pm order file. This file is called “pm_order.txt” and its format is compatible with the simulation engine AutoSched AP (“ASAP” block). Then, such file could then be used in an ASAP simulation.

24. Executable Version of PMOST • PMOST directory structure • PMOST requires the use of the following directories and files for its execution: • pmost.exe and optimize.exe files: These files correspond to the executables files in the PMOST system. The user will use pmost_main.exe as main program. • input_files directory: This directory contains the <file_name>.fam, <file_name>.sch, <file_name>.wip and <file_name>.data files that contains the parameters of the system. • output_files directory: This directory will receive the solution files generated after the optimization process by the solver and PMOST. These files include: debug.txt, pm_order.txt, pm_solution.txt and <solver>_solution.txt • mps_files directory: In this directory the final MPS format file for the actual problem will be written as well as the necessary data files used to generate it.

25. PMOSTDemo Screen Views • The input data used for this exercise was artificially created for illustration purposes only. • The user executes the file pmost.exe and the following prompt will be shown:

26. PMOSTDemo Screen Views • After that, the user will define the “Start Date” and “End Date” in the format requested in the following screenshot:

27. PMOSTDemo Screen Views • Finally, PMOST will ask for the number of technicians assigned to each period in the planning horizon defined by the “Start Date” and the “End Date”, as follows:

28. PMOSTDemo Screen Views • PMOST will then produce the MPS file, and finally it will communicate this MPS to the solver selected. The solver will compute the optimal solution that will be decoded by PMOST and written in the output_files directory. The messages presented by PMOST are as follows:

29. PMOSTDemo Results • For this example in particular, the pm_solution.txt file will looks as follows: • Tool Name PM Name Old Due Date Optimal Due Date • CT01 7 DAY PM 01/06/2002 07:00:00 01/05/2002 07:00:00 • CT02 14 DAY PM 01/05/2002 07:00:00 01/06/2002 07:00:00 • CT03 28 DAY PM 01/04/2002 07:00:00 01/02/2002 07:00:00 • CT04 56 DAY PM 01/03/2002 07:00:00 01/03/2002 07:00:00 • CT04 PMCH1 01/01/2002 07:00:00 01/03/2002 07:00:00 • CT05 PMCH4 01/02/2002 07:00:00 01/03/2002 07:00:00 • CT06 PMCH5 01/03/2002 07:00:00 01/06/2002 07:00:00 • CT07 PMCH2 01/04/2002 07:00:00 01/06/2002 07:00:00 • CT08 PMCH3 01/02/2002 07:00:00 01/04/2002 07:00:00 • CT09 KIT CH2 01/05/2002 07:00:00 01/05/2002 07:00:00 • CT10 KIT CH3 01/01/2002 07:00:00 01/01/2002 07:00:00 • CT02 7 DAY PM 01/02/2002 07:00:00 01/01/2002 07:00:00 • CT04 14 DAY PM 01/03/2002 07:00:00 01/03/2002 07:00:00 • CT01 28 DAY PM 01/04/2002 07:00:00 01/05/2002 07:00:00 • CT05 56 DAY PM 01/01/2002 07:00:00 01/03/2002 07:00:00 • CT01 PMCH1 01/05/2002 07:00:00 01/05/2002 07:00:00 • CT10 PMCH4 01/01/2002 07:00:00 01/01/2002 07:00:00 • CT04 PMCH5 01/02/2002 07:00:00 01/03/2002 07:00:00 • CT06 PMCH2 01/05/2002 07:00:00 01/06/2002 07:00:00 • CT05 PMCH3 01/03/2002 07:00:00 01/03/2002 07:00:00 • CT03 KIT CH2 01/02/2002 07:00:00 01/02/2002 07:00:00 • CT09 KIT CH3 01/01/2002 07:00:00 01/01/2002 07:00:00

30. PMOSTDemo Results • Also, a pm_order.txt file can be generated for use it in AutoSched AP simulations as PM orders:PMORDER STN DUEDATE PTIME PTUNITS • order1 CT01 01/05/2002 07:00:00 8.000000 hr • order2 CT02 01/06/2002 07:00:00 12.000000 hr • order3 CT03 01/02/2002 07:00:00 55.000000 hr • order4 CT04 01/03/2002 07:00:00 55.000000 hr • order5 CT04 01/03/2002 07:00:00 48.000000 hr • order6 CT05 01/03/2002 07:00:00 5.000000 hr • order7 CT06 01/06/2002 07:00:00 5.000000 hr • order8 CT07 01/06/2002 07:00:00 50.000000 hr • order9 CT08 01/04/2002 07:00:00 50.000000 hr • order10 CT09 01/05/2002 07:00:00 24.000000 hr • order11 CT10 01/01/2002 07:00:00 24.000000 hr • order12 CT02 01/01/2002 07:00:00 8.000000 hr • order13 CT04 01/03/2002 07:00:00 12.000000 hr • order14 CT01 01/05/2002 07:00:00 55.000000 hr • order15 CT01 01/05/2002 07:00:00 48.000000 hr • order17 CT10 01/01/2002 07:00:00 5.000000 hr • order18 CT04 01/03/2002 07:00:00 5.000000 hr • order19 CT06 01/06/2002 07:00:00 50.000000 hr • order20 CT05 01/03/2002 07:00:00 50.000000 hr • order21 CT03 01/02/2002 07:00:00 24.000000 hr • order22 CT09 01/01/2002 07:00:00 24.000000 hr

31. Jason Crabtree M.S. Student, Univ. of Cincinnati

32. Outline • Completed Work (April-June 2002) • Summer 2002 Internship, Intel Chandler, AZ • Current Work

33. Completed WorkApril-June 2002 • Work for internship started in April 2002 • Developed generic version of PM scheduling software “PMOST” • Frontend for PM optimization • Created using experience from past internships (Yao, Crabtree) • Designed for portability from one company to another • Collects and processes input data for the PM optimization • Generates MPS file for use with mathematical solver (Jose Ramirez) • Currently adding more features to handle output data from optimization

34. Summer 2002 InternshipIntel Chandler, AZ • Objectives • Validate PM optimization through simulation studies • Run optimization based on historical data • Optimized PM schedule can then be compared to best-in-practice PM schedule through simulation of fab • Lay groundwork for integration of PM optimization into production environment • Accomplished, but not discussed here due to proprietary reasons

35. Team Members • Jason Crabtree – CAS (Factory Automation and Support Group) • Robin Hoskinson - CAS • Paul Flores - CAS • Bob Madson - CAS • John Braunbeck –ODST (Factory Optimization/Simulation Group) • Madhav Rangaswami - ODST • Mani Janakiram – ODST • Emmanuel Fernandez – University of Cincinnati Thanks to: • Jack Fan – Fab12 Litho • Todd Ireland – Fab 12 IE • John Williams – F12 Thin Films • Kaeti Hendrickson – F12 Thin Films • Sharon Ramsey – Fab 12 IE • Megan Walsh – ODST • Jim Dempsey - ODST • Kowdle Prasad - CAS

36. Simulation Studies • Overview • Two simulation studies were performed during the internship • Initial study involved lithography tools • Second study involved thin film tools • The performance of each optimization was evaluated using AutoSched AP (ASAP) simulation software • An existing tactical (i.e. short term, high detail) simulation model was used for the studies

37. Simulation Studies • Initial Study • A set of litho tools (steppers & tracks) was selected for the initial study • PMs included only calendar-based PM activities • 25 tools were involved in the study (25 steppers, 25 tracks) • Each corresponding stepper and track were modeled as 2 chambers (Stepper and Track) of a single tool • Optimization looks to consolidate PMs between the stepper and track

38. Simulation Studies • Second Study • A set of thin film tools was selected for the second study • PMs included both calendar and wafer-based • Wafer-based PMs were converted to equivalent calendar-based PMs using simple average run-rate rule • 16 tools were involved in the study • Tools modeled as 2-chambered cluster tools • Ideal model would include 4 chambers • Problems with data collection forced simplification • Optimization potential therefore reduced (less chance for PM consolidation)

39. Simulation Studies • Process Overview • Collect optimization data from fab systems and engineers • Run optimization • Collect simulation data from fab systems and engineers • Run simulations using “actual” and optimized PM schedules • “actual” PM schedule refers to the schedule implemented by the tool manager (not necessarily the nominal PM schedule) • Compare results and report out

40. Simulation Studies • Optimization/Simulation Setup: General • 2 simulations runs per study • First run used actual PM schedule employed in fab during historical scheduling horizon • Second run used optimal PM schedule output from MIP • Each simulation run was replicated 10 times and statistics were taken (e.g. average, standard deviation) • Unscheduled down events were included in simulation

41. Simulation Studies • Optimization/Simulation Setup: Initial Study • Four setups used • 1-week horizon, normal WIP • 1-week horizon, inflated WIP • 2-week horizon, normal WIP • 2-week horizon, inflated WIP

42. Simulation Studies • Optimization/Simulation Setup: Second Study • One setup used (due to time constraints) • 2-week horizon, normal WIP • Nominal PM dates had to be used for several PMs due to lack of data and time constraints

43. Simulation Studies • Results: Initial Study • Neither horizon length nor WIP conditions significantly affected results • Optimization made logical decisions, matching current best-in-practice methods • Slight improvements were shown in individual tool utilization (up to 1%) • No PM consolidations could be made by optimization • Already made by tool manager • Improvements due to scheduling PMs around periods of high incoming WIP

44. Simulation Studies • Results: Second Study • Optimization made logical decisions and showed better performance gains than initial study • Improvements were shown in individual tool utilization (up to 5%) • Improvements were shown in individual tool availability (up to 5%) • Several PM consolidations made by optimization • Primary reason for increased improvements

45. Simulation Study Conclusions • First off, thanks to everyone I worked with at Intel this past summer • Two simulation studies performed on lithography and thin film tool groups • PM optimization showed promising results, especially in second study • Groundwork laid for implementation of PM optimization into production environment

46. Current Work • Continuing to advise Intel team to further validate optimization through more simulations (higher WIP) • Continuing to refine optimization model and software at UC • Looking at modification of objective function to include part life component (e.g. maximizing part life may outweigh gains from PM consolidation) • Increasing portability of PMOST • Adding functionality to PMOST (Demo) • Wafer-based PM conversion (Jose Ramirez) • PM Optimization output handling

47. José A. RamírezPh.D. Student, University of Cincinnati

48. Outline • Completed work: April – June 2002 • Summer internship: AMD, Inc. • Current work / Future tasks: September – present

49. Completed Work • The SMITLab group developed code and software PM Optimization Scheduling Tool (PMOST) implementing calendar-based PM scheduling methodology. • I worked specifically in the MPS generator: -Code in C to generate .mps files that can be used to compute optimal solutions with different solvers (e.g. OSL, CPLEX, etc.) -Makes PMOST independent from third party Model Description Language Tools (MDL’s). • Integration and test • Finished MDL/Solvers report • Prepared and successfully completed Ph.D. qualification exams.

50. Summer Internship • Internship from June to September 2002, AMD, Inc. Austin, Texas. • 3rd internship at this Member Company (Yao 2000, Yao & Crabtree 2001) • General objectives: • Quantify impact of optimized calendar-based PM scheduling through: • -Simulation analysis. • -Real-world comparison of optimized PM scheduling vs. actual PM scheduling on selected tool sets. • Enhance PM scheduling tool and models to include Wafer based PMs: -Determine model to translate into equivalent calendar estimates. • -Test by simulation and quantify impact of optimized PM scheduling, Develop code and integrate.