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

SRC/ISMT FORCe: Factory Operations Research Center Task NJ-877. Michael Fu, Director Emmanuel Fernandez Steven I. Marcus Atlanta, GA, Oct. 21-22, 2003. Intelligent Preventive Maintenance Scheduling in Semiconductor Manufacturing Fabs. CONTENTS.

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

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  1. SRC/ISMT FORCe: Factory Operations Research CenterTask NJ-877 Michael Fu, Director Emmanuel Fernandez Steven I. Marcus Atlanta, GA, Oct. 21-22, 2003 Intelligent Preventive Maintenance Scheduling in Semiconductor Manufacturing Fabs

  2. CONTENTS • Project Overview: Michael Fu • Summary of Completed Tasks: Emmanuel Fernandez • Interaction with Industry • Deliverables • Models, Algorithms, and Software Tools • Simulation Case Studies • Documentation submitted to SRC website • Other documentation • Software implementation: PMOST (Jose Ramirez) • Integration with fab schedulers: collaboration with ASU • Students trained • Summary of Doctoral and Master Theses: Students • Continuing and Future Research: Emmanuel Fernandez • Conclusions: Michael Fu

  3. Michael FuRobert H. Smith School of Business &Institute for Systems ResearchUniversity of Maryland1. Project Overview 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 • Deliverables (reports) completed: January and July 2002; SRC Pub P005269, P006317 • Best Paper in Session, TECHCON 2003 (X.Yao presenter): “Optimal preventive maintenance policies for unreliable production systems with applications to semiconductor manufacturing” • Paper submitted for publication IEEE-Trans. Semiconductor Mfg: • “Incorporating Production Planning into Preventive Maintenance Scheduling in Semiconductor Fabs” • INFORMS 2003 Annual Meeting: invited talks and an invited session organized and chaired within Applied Probability Cluster.

  6. Executive Summary • software tool (PMOST): • Generic Scheduling Simulation Engine • Generic Implementation of PM Scheduling Algorithm • summer internships (AMD & Intel) • Ph.D. dissertations supported: He, Yao, Hu, RamirezMS dissertations supported: Crabtree, Jagannathan • commercialization feasibility discussions: Adexa, Ibex Processes. • NIST internship via Swee Leong

  7. Industrial Liaisons • Matilda O'Connor, AMD • Nipa Patel, AMD(sign in SRC list) • Ying Tat Leung, IBM • Wayne F. Carriker, Intel • Robin L. Hoskinson, Intel • Ben-Rachel Igal, Intel • Mani Janakiram, Intel • Madhav Rangaswami, Intel • Sidal Bilgin, LSI (sign in SRC list) • Russell Whaley, LSI (sign in SRC list) • Ramesh Rao, National Semiconductor • Jan Verhagen, Philips (sign in SRC list) • Shekar Krishnaswamy, Motorola (sign in SRC list) • K.J. Stanley, Motorola (sign in SRC list) • Gurshaman S. Baweja, TI • Jason Wang, TSMC (ISMT) • James Yang, TSMC (ISMT) • Giant Kao, TSMC (ISMT) • Jacky Fan, TSMC (ISMT)

  8. Research Personnel Faculty: • Michael Fu, Maryland • Steve Marcus, Maryland • Emmanuel Fernandez, Cincinnati Students: • Xiaodong Yao, Maryland (PhD final defense Nov.2003) • Ying He, Maryland (PhD completed, summer 2002) • Jiaqiao Hu, Maryland (3rd year PhD) • Jason Crabtree, Cincinnati (MS completed, summer 2003) • Jose Ramirez, Cincinnati (3rd year PhD) • Sumita Jagannathan, Cincinnati (3rd year MS)

  9. 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.

  10. 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) MORE DETAILS later in presentation

  11. Emmanuel Fernandez, Ph.D.ECECS DepartmentUniversity of Cincinnati2. Summary of CompletedTasks

  12. Summary of Completed Tasks • We summarize here the accomplishments in the project up to this point: • Interactions with industry • Deliverables • Models, Algorithms, and Software Tools • Case Studies • Documentation submitted to SRC website • Other documentation • Software Implementation: PMOST • Integration with fab schedulers: collaboration with ASU • Students trained • (Doctoral and Master Theses)

  13. Interactions with Industry

  14. Interaction with Industry • Interactions with industry have been fundamental in guiding our research efforts: • These facilitated the design, implementation, and proof of concept of our algorithms, models and software tools. • Interactions have taken place in the form of: • Summer internships for our students from 2000 through 2002. • Direct collaboration to exchange ideas and formulate problems and solutions, e.g: • Survey on best practices of PM scheduling; • Visits to fabs to interview and obtain feedback from tool managers and operators. • Periodic teleconferences with MC liaisons. • Co-authored publications derived from the research work.

  15. Interaction with Industry • Summer InternshipsDuring the project, a total of four summer internships were completed at two member companies (2000 to 2002): • X. Yao, 2000, AMD, Austin, TX: data collection and simulation of one case study. • X. Yao, J. Crabtree, 2001, AMD, Austin, TX: software implementation of algorithms and models; built interfaces to integrate to fab systems. • J. Crabtree, 2002, Intel, Chandler, AZ: data collection, software implementation, and two simulation studies. • J.A. Ramírez, 2002, AMD, Austin, TX: data collection and modeling for wafer to calendar-based conversion of PM schedules, and two simulation studies.

  16. Deliverables:Models, Algorithms, and Software Tools

  17. Deliverables • Models and Algorithms, and Software Tools • Here we summarize the Models and Algorithms produced by the research team representing the theoretical/academic contributions and basis for implementation in software tools: • -Hierarchical Model for Optimal PM Scheduling. • -MIP formulation of the PM scheduling problem. • -Conversion of wafer to calendar-based PM schedules. • - X. Yao Doctoral work.

  18. Objective Failure Dynamics Upper MDP PM Policy Demand Pattern Lower MIP PM Schedule WIP Constraints Deliverables Models and AlgorithmsHierarchical Model for Optimal PM scheduling

  19. Deliverables Models and Algorithms - MIP Formulation Objective: æ ö r N M i å å å ç ÷ × - × - × l l l max b V ( t ) C I ( t ) C a ( t ) ç ÷ i i i i i i è ø a ( t ) = = = t 1 i 1 i 1

  20. l n i å = l a ( t ) 1 i l n = t 1 i l m i å = l a ( t ) 0 i l m = t 1 i N å £ l a ( t ) 1 i = t 1 Deliverables Models and Algorithms - MIP Formulation Constraints: (i) for those PM tasks required to begin by period (ii) for those PM tasks prohibited from beginning before period (iii) for all PM tasks in general

  21. = b " ( ) ( ( ), ( )) , V t f a t t i t i i i a ( t ) i b ( ) t i ( ) a t i b + = ( 1 ) ( ) t a t i i Deliverables Models and Algorithms - MIP Formulation Constraints: (iv) where is the set of PM decisions across all PM tasks, and is a dummy variable holding the value of from the previous period, i.e.

  22. + = - + " = - ( 1 ) ( ) ( ) ( ) , 1 ,..., 1 I t I t X t d t i t N i i i i £ × " X ( t ) K V ( t ) i , t i i i Deliverables Models and Algorithms - MIP Formulation Constraints: (v) where di(t ) is amount of incoming wafers at tool i in period t , and Xi(t ) is the quantity of wafers processed on tool i in period t. (vi) where Ki is the wafer throughput coefficient for tool i.

  23. £ " I ( t ) L i , t i i = b " ( ) ( ( ), ( )) , r t g a t t k t k i i r ( t ) k Deliverables Models and Algorithms - MIP Formulation Constraints: (vii) where Li is the maximum allowed inventory at tool i. (viii) where is the resource requirement variable for resource k in period t .

  24. £ " ( ) ( ) , r t R t k t k k ³ ³ ³ ³ k " ( ) 0 ( ) 0 ( ) 0 V t I t X t ( ) 0 r t , , k i t i i i i = " l ( ) 0 1 , , a t or i l t i Deliverables Models and Algorithms - MIP Formulation Constraints: (ix) where Rk(t) is the amount of resource k available in period t. (x) , , , (xi)

  25. Deliverables Models and Algorithms Conversion of Wafer to Calendar-based PM Schedules PM window (W: warning, D: due, L: late) (Wafer counts/period) (Time period) Estimated due time (date)

  26. Deliverables:Simulation Case Studies

  27. Simulation CaseStudies • Objectives • Validate PM optimization through simulation studies with real fab data • Simulation studies conducted to compare model-based optimized PM schedule and base-line or historical (“best in practice”) PM schedules. • Lay groundwork for integration of PM optimization into production environment

  28. Simulation CaseStudies • Five case studies with real fab data. Calendar and/or wafer based PM’s. • Case 1: Metal Deposition process (11 tools, 7days); Best in Practice vs. Optimized Schedule • Case 2: Photolithography process (25 tools, 7 days); Best in Practice vs. Optimized PM schedule • Case 3: Metal Deposition process (29 tools, 7 days); Baseline vs. Optimized PM Schedule • Case 4: Photolithography process (12 tools, 7days); Baseline vs. Optimized PM schedule • Case 5: Thin films process (28 tools, 21 days); Best in Practice vs. Optimized PM schedule.

  29. Simulation CaseStudies • Results: Optimization made logical decisions and showed good performance gains. • Case 1: up to 14% gain in throughput for one tool. • Case 2:Matched tool availability throughput for “Best-in-Practice” schedule. • Case 3: about 1% average gain in tool availability for entire tool group; 1.7% average gain in total throughput for entire tool group. • Case 4:1% average gain in tool availability for entire tool; 2.2% average gain in total throughput for entire tool group. • Case 5: up to 6% gain in tool availability for one tool; 0.7% average gain in tool availability for entire tool group; 1% average gain in total throughput for entire tool group.

  30. Deliverables:Documentation Submitted to SRC Website

  31. Deliverables • Documentation submitted and currently available at SRC website • The following is the list of all the documentation produced by the research team and available at the SRC website: • Annual review presentations • Intelligent Preventive Maintenance Scheduling in Semiconductor Manufacturing Fabs; Crystal City, MD, December 13-14, 2001, Pub P003262. • Intelligent Preventive Maintenance Scheduling in Semiconductor Manufacturing Fabs; Tempe, AZ, April 9-10, 2002, Pub P007441. • Intelligent Preventive Maintenance Scheduling in Semiconductor Manufacturing Fabs; San Jose, CA, November 20-21, 2002, Pub P005082. • Reports • Survey of Current PM Practices in Industry, Conducted Via Web and Electronic Mail; E. Fernandez, M. Fu and S. Marcus; Univ. of Maryland; 17-Jan-2002; 19pp.; Pub P003461. Abstract: The researchers present the results of survey on the practices employed in the semiconductor manufacturing industry for scheduling Preventive Maintenance (PM) tasks. The survey was distributed by the middle of October 2001, and responses were received until the middle of December 2001. • 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.Abstract: The researchers have developed models and algorithms for optimal PM scheduling based on calendar information of time since last PM, and the time window within which the next PM needs to fall. A computationally tractable mixed Integer/Linear Programming (IP/LP) model for short-term planning horizon, e.g., 1-3 weeks, has been developed, tested and implemented to do the day-to-day actual scheduling of PM tasks across tools within a given family.

  32. Deliverables • Documentation submitted and currently available at SRC website • Reports • Preventive Maintenance Optimal Scheduling Tool (PMOST): Ver. 1.0; J. Crabtree, J. Ramirez, E. Fernandez, X. Yao, M. Fu and S. I. Marcus; Univ. of Maryland; 21-Jan-2003; 8pp.; Pub P005269.Abstract: The Preventive Maintenance Optimal Scheduling Tool (PMOST) is a (programmed in C-language) software tool for optimal scheduling of Preventive Maintenance tasks in Semiconductor Fabs. • Preventive Maintenance Optimal Scheduling Tool (PMOST): Ver. 1.1; J. Crabtree, J. Ramirez, E. Fernandez, X. Yao, M. Fu and S. I. Marcus; Univ. of Maryland; 10-Jul-2003; 10pp.; Pub P006317. • Abstract:The Preventive Maintenance Optimal Scheduling Tool (PMOST) is a (programmed in C-language) software tool for optimal scheduling of Preventive Maintenance tasks in Semiconductor Fabs. PMOST v. 1.1 includes conversion of wafer-based to calendar-based PM schedules. • Preventive Maintenance Scheduling Model and Generic Implementation, Mathematical Programming Modeling Languages and Solvers; J. Crabtree, J. Ramirez, E. Fernandez; Univ. of Cincinnati; 29-Jul-2002; 6pp.; Pub P004306.Abstract: This report present a survey on Mathematical Programming Modeling Languages (MDL) and Solvers that can be used in optimization of PM schedules. • Papers • Optimization of Preventive Maintenance Scheduling for Semiconductor Manufacturing Systems: Models and Implementation; X. Yao, M. Fu, S. Marcus and E. Fernandez-Gaucherand; Univ. of Maryland; 17-Dec-2001; 5pp.; Pub P003267.Abstract: In this paper, the researchers present a two-layer hierarchical modeling framework for addressing the PM optimization problem for cluster tools, i.e., a Markov Decision Process (MDP) model at the higher level, and a mixed Linear Programming (LP) model at the lower level. Production planning data such as WIP levels are incorporated in these models. Paper presented at the 2001 IEEE International Conference on Control Applications, Mexico City, Mexico, 2001. • Incorporating Production Planning into Preventive Maintenance Scheduling in Semiconductor Fabs; X. Yao, M. Fu, S. Marcus and E. Fernandez-Gaucherand; Univ. of Maryland; 29-Jul-2002; 6pp.; Pub P004305.Abstract: In this paper, a general mathematical model aiming at the optimization of preventive maintenance (PM) scheduling is proposed. The researchers formulate the problem as a finite-horizon Markov decision process (MDP) that incorporates equipment dynamics and production system dynamics. Paper presented at MASM 2002 Conference, Tempe, AZ, 2002.

  33. Deliverables • Documentation submitted and currently available at SRC website • Papers (cont.) • Optimal Preventive Maintenance Policies for Unreliable Queueing/Production Systems with Applications to Semiconductor Manufacturing; Xiaodong Yao, X. Xie, M. Fu, S. Marcus and E. Fernandez; Univ. of Maryland; 6-Jun-2003; 5pp.; Pub P006072.Abstract: The reliability of equipment is critical to fab's operational performance, and Preventive Maintenance (PM) scheduling is a very challenging task in semiconductor manufacturing. In this paper, the researchers will study optimal PM policies under the context of unreliable queueing systems. Presented at TECHON 2003 (Awarded as "Best Paper in Session") , August 25-27, 2003, Dallas, TX. • Optimal Importance Sampling in Securities Pricing; Y. Su and M. C. Fu; Univ. of Maryland; 21-Jun-2002; 29pp.; Pub P004145. • Abstract: To reduce variance in estimating security prices via Monte Carlo simulation, the researchers formulate a parametric minimization problem for the optimal importance sampling measure, which is solved using infinitesimal perturbation analysis (IPA) and stochastic approximation (SA). • Convergence of Simultaneous Perturbation Stochastic Approximation for Nondifferentiable Optimization; Y. He, M. C. Fu and S. I. Marcus; Univ. of Maryland; 22-May-2003; 5pp.; Pub P005903. • Abstract: This paper considers Simultaneous Perturbation Stochastic Approximation (SPSA) for function minimization. The standard assumption for convergence is that the function be three times differentiable, although weaker assumptions have been used for special cases. However, all previous work appears to at least require differentiability. This paper relaxes the differentiability requirement and proves convergence using convex analysis. • Presentations • Preventive Maintenance in Semiconductor Manufacturing Fabs; M. Fu; Univ. of Maryland; 15-May-2001; 41pp.; Pub P002234.Abstract: FORCe Kick-off meeting presentation, Seatle, WA, April 26-27, 2001. • Optimal Preventive Maintenance Policies for Unreliable Queueing/Production Systems with Applications to Semiconductor Manufacturing Fabs; Xiaodong Yao, X. Xie, M. Fu, S. Marcus and E. Fernandez-Gaucherand; Univ. of Maryland; 8-Sep-2003; 13pp.; Pub P006866. • Abstract: The reliability of equipment is critical to fab's operational performance, and Preventive Maintenance (PM) scheduling is a very challenging task in semiconductor manufacturing. In this paper, the researchers will study optimal PM policies under the context of unreliable queueing systems. Presented at TECHON 2003 (Awarded as "Best Paper in Session").

  34. Deliverables • Documentation submitted and currently available at SRC website • Other documentationSoftware Description:Preventive Maintenance Optimal Scheduling Tool (PMOST); SMITLab University of Cincinnati; Univ. of Maryland; 30-Jun-2003; 2pp.; Pub P006313. • Abstract: The Preventive Maintenance Optimal Scheduling Tool (PMOST) is a (C-language) software tool for optimal scheduling of Preventive Maintenance tasks in Semiconductor Fabs. PMOST accepts a set of parameters related to the PM optimization process, 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 and CPLEX. The system requires a set of data files, defined under specific (standard) formats, used in the optimization process. • Thesis-MS:Optimal Preventive Maintenance Scheduling in Semiconductor Fabs; J. Crabtree; Univ. of Cincinnati; 10-Oct-2003; 84pp.; Pub P007381. • Abstract: This thesis is spawned from the research project, "Preventive Maintenance in Semiconductor Fabs", sponsored by the Semiconductor Research Corporation (SRC) and International SEMATECH. The project proposes a two-level hierarchical optimization structure that considers important factors such as the work-in-progress (WIP) at a tool and the complex relationships between the chambers of a cluster tool. This thesis focuses on the lower level of the aforementioned hierarchy that deals with PM scheduling. It expands on the work accomplished thus far in the project, specifically analyzing and fixing current issues with the PM scheduling algorithm and creating a software implementation of the scheduling algorithm.

  35. Deliverables:Other Documentation

  36. Deliverables • Other Documentation (not posted yet at SRC web site) • Papers • Optimal Preventive Maintenance Scheduling in Semiconductor Manufacturing, X. Yao; E. Fernandez-Gaucherand; M.C. Fu; S.I. Marcus; submitted for publication to IEEE Transactions on Semiconductor Manufacturing, 2003. • An Algorithm to Convert Wafer to Calendar-Based Preventive Maintenance Schedules for Semiconductor Manufacturing Systems, J.A. Ramírez-Hernández and E. Fernández-Gaucherand., to appear in Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, HI, December, 2003. • Optimal PM Scheduling in Semiconductor Manufacturing Systems: Case Studies, Univ. Cincinnati, Univ. Maryland, AMD, Intel. In preparation. • Survey of Best Practices of PM Scheduling in Semiconductor Manufacturing Systems, J.A. Ramírez, J. Crabtree, E. Fernandez, X. Yao , M. Fu and S.I. Marcus. In preparation. • Optimal Joint Preventive Maintenance and Production Control Policies for Unreliable Production Systems, X. Yao, X. Xie, M. Fu, and S. Marcus. In preparation. • Presentations • Suppliers Teleconference Presentation: Commercialization, M. Fu, E. Fernandez, S.I. Marcus, J. Crabtree, J.A. Ramírez, X. Yao, September 4th,, 2003, SEMATECH Webex teleconference system.

  37. Software Implementation: PMOST

  38. Software Implementation:PMOST • Software implementation of models and algorithms is an objective that has been accomplished with the design and coding of the software Preventive Maintenance Optimal Scheduling Tool(PMOST). • The following are the versions produced up to this point: • PMOST ver. 1.0: first version of PMOST coded in C-language, running over MS-Windows platforms (Windows 2000 and up). Include a basic text-mode user interface, link with Optimization Library Solutions (OSL) solver from IBM, and generates Mathematical Programming System (MPS) files describing the MIP problem. • PMOST ver. 1.1: includes same characteristics of version 1.0 plus the conversion algorithm for wafer-based to calendar-based PM schedules. An installer for MS-Windows is included in this version. • PMOST ver. 1.2: first Graphical User Interface (GUI) for PMOST, includes all characteristics of verions 1.0 and 1.1. MS-Windows platform (Windows 2000 and up).

  39. utils.c - Planning horizon START User Interface - Tools family General functions used in main.c - Number of Technicians different parts of the system. pmost.exe *.fam, *.data read_data_file.c - Tool/PM data files: - read_fam_file.c Conv ersion to calendar - time PMs data files Read read_sch_file.c write_sch_file.c - files *.sch Input Data PM schedule: read_wip_file.c ASAP - : files *.wip Estimated WIP data files conv2cal ( .exe , .c, .h) debug.txt - Debugging file: Conversion to calendar - time PMs - *.csch Converted Schedule Fi le: create_pm_vectors.c Write *.set *.val - Output data: , files write_set_val_files.c MPS file *.mps - MPS file: write_mps_file.c write_debug_file.c .mps file main.c calls th e LP/MIP solver (OSL, SOLVER CPLEX, etc) solu tion file (text file) parse_osl_solution.c pm_order.txt Output: Parse parse_cplexl_solution.c Solution write_solution_file.c write_pm_order_file.c pm_solution.txt Software Implementation:PMOST PMOST Block Diagram pmost_ui.exe

  40. Software Implementation:PMOST • PMOST 1.2 with GUI, Demo Movie

  41. Software Implementation:PMOST • PMOST 1.1 with text-mode user interface, screen captions • 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:

  42. Software Implementation:PMOST • PMOST 1.1 with text-mode user interface, screen captions • After that, the user will define the “Start Date” and “End Date” in the format requested in the following screenshot:

  43. Software Implementation:PMOST • PMOST 1.1 with text-mode user interface, screen captions • 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:

  44. Software Implementation:PMOST • PMOST 1.1 with text-mode user interface, screen captions • 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:

  45. Software Implementation:PMOST • PMOST 1.1 with text-mode user interface, screen captions • 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

  46. Software Implementation:PMOST • PMOST 1.1 with text-mode user interface, screen captions • 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

  47. Integration with Fab Schedulers: Collaboration with ASU

  48. Integration of Fab Schedulers:Collaboration with ASU • Collaboration is under way with the ASU Team with the objective of integrating fab scheduling and optimal PM scheduling in semiconductor fabs. • The goal is integrate both fab scheduling and preventive maintenance to evaluate long-term performances in semiconductor manufacturing systems via simulation analysis. • The research teams have identified the requirements for such integration as well as proposed a work plan to complete the task. • Currently, both teams are working to close the gap in the software implementation and start experiments using simple models (e.g., minifab) for proof of concept. • Integration involves communication between simulation software (customization of ASAP) and the corresponding schedulers (jobs and PMs).

  49. Students Trained

  50. Students Trained • The following students have participated in the research tasks for this project, and have received substantial training in different topics (e.g., ASAP training, courses in stochastic modeling and decision, simulation analysis and modeling): • Ph.D. Students: • Ying He, Maryland (Ph.D. completed, graduated on summer 2002) • Jiaqiao Hu, Maryland (3rd year Ph.D.) • José Ramírez, Cincinnati (3rd year Ph.D.) • Xiaodong Yao, Maryland (Ph.D., will graduate in December 2003) • M.Sc. Students: • Jason Crabtree, Cincinnati (M.Sc. completed, graduated September 2003) • Sumita Jagannathan, Cincinnati (continuing M.Sc.)

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