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

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SRC/ISMT Factory Operations Research Center

SRC/ISMT FORCe:Factory Operations Research Center

Task 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

SRC/ISMT Factory Operations Research Center2

•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

CONTENTS

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Michael Fu, Ph.D.Institute for Systems Research

University of Maryland

Project Overview/Status

Summary


(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.

Research Plan (Proposed)


• “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.

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

Executive Summary


• 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

Industrial Liaisons


• 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)

Industrial LiaisonsNew Members


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)

Research Personnel


• 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

Project Management


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.

Task Description(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)

Deliverables to Industry(Proposed)

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OverviewSRC 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)


•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.

To do in 2002-2003

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Emmanuel Fernandez, Ph.D.ECECS Department

University of Cincinnati

Overview of Software Tools and Summer Internships

Summary

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Outline

1. PMOST: Preventive Maintenance Optimal Scheduling Tool

2. Summer Internships:

• Jason Crabtree at Intel, Chandler, AZ

• Jose Ramirez at AMD, Austin, TX.

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“Recent Accomplishments” List:April 2002 Review

1. PM Best Practices Survey report delivered (appended).

2. Paper for MASM delivered (appended).

3. Progress on development of generic implementation of PM Scheduling Algorithm and IT implementation: new deliverable (report, draft appended). Completed Beta Version, report draft

4. Progress on development of generic ASAP simulation engine of PM Scheduling Algorithm for key tools. Completed Beta Version, report draft

5. Investigated analytical (MDP models and queueing models) and simulation-based approaches to PM planning: dissertation. Done

6. 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.

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OverviewSRC 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)

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PMOSTOverview

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

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PMOST Overview

1. 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..

2. 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.

3. The system requires a set of data files, defined under specific (standard) formats, used in the optimization process.

4. 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.

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PMOSTDiagram

START User input main.c

Read Input Data

-Tool/PM data files: *.fam, *.data -PM schedule: files *.sch -Estimated WIP data files: files *.wip -Debugging file: debug.txt

Write MPS file

LP/MIP SOLVER

main.c calls the solver (OSL, CPLEX, etc)

Parse Solution

-Output data: *.set, *.val files -MPS file: *.mps

.mps file

ASAP write_sch_file.c

Output: pm_order.txt

utils.c General functions used in different parts of the system.

-Planning horizon -Tools family -Number of Technicians

solution file (text file)

pm_solution.txt

create_pm_vectors.c write_set_val_files.c write_mps_file.c write_debug_file.c

parse_osl_solution.c parse_cplexl_solution.c write_solution_file.c write_pm_order_file.c

read_data_file.c read_fam_file.c read_sch_file.c read_wip_file.c

optimize.exe

pmost.exe

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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.

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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.

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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.

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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:

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

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•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:

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•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:

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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 DateCT01 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

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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 PTUNITSorder1 CT01 01/05/2002 07:00:00 8.000000 hrorder2 CT02 01/06/2002 07:00:00 12.000000 hrorder3 CT03 01/02/2002 07:00:00 55.000000 hrorder4 CT04 01/03/2002 07:00:00 55.000000 hrorder5 CT04 01/03/2002 07:00:00 48.000000 hrorder6 CT05 01/03/2002 07:00:00 5.000000 hrorder7 CT06 01/06/2002 07:00:00 5.000000 hrorder8 CT07 01/06/2002 07:00:00 50.000000 hrorder9 CT08 01/04/2002 07:00:00 50.000000 hrorder10 CT09 01/05/2002 07:00:00 24.000000 hrorder11 CT10 01/01/2002 07:00:00 24.000000 hrorder12 CT02 01/01/2002 07:00:00 8.000000 hrorder13 CT04 01/03/2002 07:00:00 12.000000 hrorder14 CT01 01/05/2002 07:00:00 55.000000 hrorder15 CT01 01/05/2002 07:00:00 48.000000 hrorder17 CT10 01/01/2002 07:00:00 5.000000 hrorder18 CT04 01/03/2002 07:00:00 5.000000 hrorder19 CT06 01/06/2002 07:00:00 50.000000 hrorder20 CT05 01/03/2002 07:00:00 50.000000 hrorder21 CT03 01/02/2002 07:00:00 24.000000 hrorder22 CT09 01/01/2002 07:00:00 24.000000 hr


Jason CrabtreeM.S. Student, Univ. of Cincinnati


Outline

• Completed Work (April-June 2002)

• Summer 2002 Internship, Intel Chandler, AZ

• Current Work


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


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


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 CincinnatiThanks 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


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


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


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)


Simulation Studies

• Process Overview1. Collect optimization data from fab systems and

engineers2. Run optimization3. Collect simulation data from fab systems and engineers4. Run simulations using “actual” and optimized PM

schedules1. “actual” PM schedule refers to the schedule implemented by the

tool manager (not necessarily the nominal PM schedule)

5. Compare results and report out


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


Simulation Studies

• Optimization/Simulation Setup: Initial Study– Four setups used

• 1-week horizon, normal WIP

• 1-week horizon, inflated WIP


• 2-week horizon, inflated WIP


Simulation Studies

• Optimization/Simulation Setup: Second Study– One setup used (due to time constraints)


– Nominal PM dates had to be used for several PMs due to lack of data and time constraints


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


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


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


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


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


• Completed work: April – June 2002

• Summer internship: AMD, Inc.

• Current work / Future tasks: September – present

Outline


• 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.

Completed Work


• 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.

Summer Internship


• First optimization tool (OPMST) installed in summer internship 2001 handled only calendar-based PMs.

• Idea: convert wafer-count targets to equivalent calendar targets, then use already existing optimization tools.

• Met with several modules (groups) to discuss wafer-based PMs, their scheduling and approaches and logic.

• Results include a conversion algorithm to estimate due dates for wafer-based PMs.

• The resulting conversion system is completely integrated with the existing scheduling tool.

• Most of the required data is automatically extracted from the Fab information systems.

Summer Internship


• Current procedure in the algorithm consists of estimating the due dates for the wafer-based PM’s.

• Two approaches:• Using averages: tools utilization.• Using all WIP information available and tool parameters.

• Work focused on the second approach, this procedure involves several steps:– Define a planning horizon to perform the estimation.– Use actual data about wafers processed per tool at the beginning of the

planning horizon, throughput rate, tool availability, and incoming WIP.– Chamber configuration per tool (e.g. parallel, serial, etc.).

• Data sources used in this process:– Incoming WIP reports, throughput rate, and tool availability.– PM tasks, tool and chambers related.– Tool experts: chamber configuration data.

Wafer to Calendar-based PMs conversion


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Additional Information obtained:

– Estimated due dates for wafer-based PM’s.

A set of toolsInitial PM schedule*

Planning horizonProjected Incoming WIPChambers configuration

Tool Parameters

Optimized PM ScheduleEstimated AvailabilityEstimated WIP

Optimization Scheduling

Model/algorithm

*Estimated for wafer-based PM’s

Inputs and Outputs


Algorithm Flow Chart

System Initializationand selecting a machine Family

.ini file;.tool file;.item file;

Fabdatabase

wip file

Resource Data File

(manpower.txt)

Specifying a planning horizon

Reading in Fab database,

performing data filtering

Reading in projected WIP

Reading in projected resource

Generating consolidated

tasks vector set {v}

Computing availability loss and resources

requirement for each task vector.

Generating MIP model instance in a

standard format

Invoking OSL default solver to solve the MIP model

Parsing model solution and interpreting the

result to users

End

Begin

.chm file(Chamber Scenario)

SIMPLEX and Branch-and-Bound algorithms are used in the default solver

Fabdatabase

Wafer to calendar-based PM’s conversion process


Begin

Inputs: -Planning Horizon

-Tool Family definition

Compute number of periods in the

planning horizon

Check and read WIP file

WIP file OK

WIP file incomplete

Read Fab Database

Read number of tools and chamber

configuration file

End

Generates and read wafer-based PM windows file,

count number of PM tasks

Compute the planning horizon time length

Compute warning, due and late dates estimations

EndWrite a new Fabdatabase containing estimated due dates

Conversion process: software flow diagram

Wafer to Calendar-based PM’s conversion process


Begin

Select Tool Group

Enter Planning Dates

Enter Manpower Schedule

Run Optimization

Confirm Optimal Schedule

Confirm PM Items

Update Fab Database Records

End

LP Solved On Remote Server

Wafer-Based PM’s Conversion Process

Wafer to Calendar-based PM’s conversion process


• Objective: quantify the impact of optimal schedule on tool availability.

• Simulation studies conducted to compare model-based optimized PM schedule and base-line or historical (“best in practice”) PM schedules.– Semiconductor Fab model from the liaison company

(AutoSched AP)– Calendar based PM study for Lithographic Process Tools

(optimized vs. “best in practice” and base-line).– Wafer based PM study for Metal Deposition Tools

(optimized vs. base-line).– Optimized schedules obtained using PMOST.

Simulation Study


Calendar based PMs – Lithographic Process

– Work week 28 WIP and Starts data in model– Included PMs for one week– Output evaluated

• Shows positive change in tool availability (up to 1%).• Due to rather low loads currently in the Fab, no major

improvements were observed for calendar-based scheduling, which performed as well ascurrent "best-in-practice" methods.

– Most of the PMs included in the study were short term and they do not represent a big challenge for the optimization tool or “humans”.

– Results validate robustness of the optimization tool.

Simulation Study


Wafer based PMs – Metal Deposition tools

• Work week 36** WIP and starts in model– Showed 3% improvement in tool availability on average (max.

improvement of 5.8% on a tool)

• Work week 32 Model– Showed 2% Improvement in tool availability

• Work week 34 Model– Showed 1% Improvement in tool availability on average (max

improvement of 3% on a tool)

**This work week have a more complex scenario for PM tasks.

Simulation Study


• Simulation study has shown improvements on tools’ availability by applying an optimized schedule and validate the optimization tool robustness.

• Successfully documented implementation procedures for PM scheduling software. Methodology and code was extended to handle wafer-based PMs.

• AMD is currently going through the authorization processes to make operational" in FAB 25 tools developed during internships implementing our models.

Conclusions


Current work:• Extend conversion algorithm to different types of PMs:

– Processing time-based and Energy-based PMs: First report completed with logic and mathematical model.

– Code developed to integrate with PMOST environment.– Developed Matlab testing platform for conversion algorithms

(Demo).

Future Work:– Write papers.– Simulation of mixed scenarios with calendar, wafer and

processing time based PMs.– Research in how to incorporate risk factors in decision taking

about PM scheduling in the optimization process (PMs scheduled early or late.)

– Research in hot lots and Optimal PM Scheduling.

Current work and Future Tasks

64


Xiaodong YaoPh.D. Student, Univ. of Maryland

Optimal Preventive Maintenance Policy for Unreliable Queueing systems with Applications to

Semiconductor Manufacturing Fabs


Outline

1. Study of time-window policies for single machine

2. Combined PM and production policy

3. Numerical study for M/G/1 unreliable queueing systems


Problem Setting

down time PMfor ratecost ,down time failurefor ratecost cost setup PM cost, setuprepair

r.v., PM,for time , r.v., repair,for time ~ r.v., PM, do to timeˆ , ~ r.v., failure, to time

pf

pf

ppff

kkcc

bTETaTETtGTtFT

Objective: determine PM policy G(t) to minimize long-term average cost

Consider an unreliable machine,

failure PMT Tf T̂ Tp

Machine newest

state

Machine newest

state

Machine newest

state

t

Time-window policy: PM conducted within a time window,

according to a distribution

Example: uniform distribution.

, ,ˆ wttT . ~ˆ tGT

,,~ ˆ wttUT


For the case of instantaneous repairs and PMs, i.e., Tf=Tp=0, Barlow and Proschan (1965) derive a non-randomized optimal policy. We have extended this result to our setting.By renewal theory, the average cost is

.

, where

0

0

0

t

ppff

tFbtaFdyyFtq

tFbkctFakctp

tdGtq

tdGtp

E

CE

tqtptt

/infarg*

Optimality of Non-Randomized Policy

Proposition: There exists a non-randomized optimal policy that minimizes the average cost, i.e., the distribution function G(t) degenerates to a point massat (can be infinite).


Combined PM and Production Policyfor Unreliable Production Systems

• machine experiences time-dependent failures

• flexible production rate,

• inventory consumed by a constant demand d, and backlog allowed

• Upon machine failures, repair has to be initiated with cost cr, and time

for repair is a r.v.

• Before machine failures, PM can be applied with cost cp, and time for

PM is a r.v. as well

• inventory holding cost g(·), piecewise linear function of inventory level

• Objective: find PM / production policy to minimize discounted cost

,0 Pu

du


Some Structural Results

Property 1: When there is backlog, if choose not to do PM, then optimal production rate is at least as large as demand rate.

Property 2: Under the following conditions: (1) machine failures is IFR; (2) (3) times for repair and PM are stochastically equivalent or machine failure rate is constant. For fixed inventory level, optimal PM policy has control-limit form.

Property 3: For fixed age, there exists an inventory threshold level such that above the threshold, it is not optimal to produce.

;pr cc


Operation-Dependent Failures

du

Operation-dependent failures: Machine deteriorates only when it is producing, and can not fail while idle;Time-dependent failures: Machine deteriorates whether or not producing, and can fail while idle.Then we can show property 2 (control-limit policy) is preserved under relaxed condition (3), i.e., hazard rate ordering between times for PM and for repair, in addition to other two conditions.


M/G/1 with Unreliable Server

Gs(·)

• Server deteriorates over the time horizon

• Conditional probability of server failure is qn when the nth job is just finished

• Two types of maintenance, i.e., CM (Corrective Maintenance) and PM

• Generally distributed times for CM/PM, Gp(·), Gr(·)

• PM setup cost cp, running cost rate rcp

• CM setup cost cr, running cost rate rcr

• Inventory holding cost rate h, and lost demand penalty ld

• Yield rate yn at state n, unit revenue R

• Objective is to maximize average revenue over an infinite horizon.


Optimal vs. Heuristic Policies

Optimal policy: The policy derived from Bellman Equations.

Double-Threshold policy: characterized by (n,N,k). If age is greater or equal

to N, or if age is between [n,N) and the buffer level is less than k, then

perform PM; otherwise do not perform.

Single-Threshold policy (time-window policy): characterized by (n,N). If age

is greater than N, then perform PM; if age is between [n,N), then perform

PM uniformly within the window; if age is less than n, then do not perform

PM.


Numerical Study: M/M/1 Queue

Representative Results:

.5.1,10,2,10

,3/,,100

2.0,5.0,3,1

prpprp

nn

rps

rcrcrcccc

RldryR

case Optimal policy

g

Optimal

Double-threshold policy

g(n,N,k)

Optimal

Single-threshold policy

g(n,N)

h=0 61.955 61.738(4,6,1) 59.440(4,7)

h=1 61.071 60.603(4,6,2) 58.491(4,7)

Summary: optimality gap < 1% for optimal double-threshold policy ~ 4% for optimal single-threshold policy.


Appendix: Discrete-Time Model for Unreliable Production System

.1 i.e., state, in working machine while,produce to:,0

PM do to: control

state machine of changelast thesince periods time repairin machine if :0

state in working machine if :1PMin machine if :2

levelinventory where,, state

tt

t

t

tttt

iPu

PMu

ia

i

sais

.,01Pr

,,21Pr

,,10Pr :iesprobabilit lconditiona

1

1

1

naiir

naiip

naiif

tttn

tttn

tttn


Bellman Equations

.1,1,1

0,0,,1,

,0,2,,1, where

,,1,min;,1,min,1,

:state in working machine when

,1,2,10,1,,2,

:PMin machine when

,1,0,10,1,,0,

:repairin machine when

,,0

nduxJf

duxJcfxgnxQ

xJcnxQ

nxQnxQnxJ

ndxJpdxJpxgnxJ

ndxJrdxJrxgnxJ

n

rnu

pPM

u

Pu

PM

nn

nn


Structural Results with J Function

.or 0,1,,for such that ,, fixedfor :3Property

.,1,,for such that ,,for i.e., form.limit -control haspolicy PM optimal the, fixedfor implies This

constant. is rate failure machine or equivalentally stochastic are PM andrepair for times(3)

; (2) IFR; failures machine (1) :satisfied are conditions following theif ,in function increasingan is ,1, :2Property

rate. demand as large asleast at is rate production optimal then PM, do not to choose if backlog, is there that whenimplies This

.0for ,in functions decreasing are,,2,,,1,,,0, :1Property

***

***

PMnxnxxnxn

PMnxxnnxnxx

ccnnxJ

xxnxJnxJnxJ

rp

77


Emmanuel Fernandez, Ph.D.ECECS Department

University of Cincinnati

Transfer to Commercialization Plan

Commercialization

78


Commercialization

1. Models and algorithms: Publications

2. PMOST: Preventive Maintenance Optimal Scheduling Tool

3. Summer Internships: Proof of concept and implementation

• AMD: 2000 (Yao), 2001 (Yao, Crabtree), 2002 (Ramirez)

• Intel: 2002 (Crabtree, Fernandez)

79


Commercialization

• What to transfer?

• Model & Algorithms implementation know-how

• Data specification and structures

• Software tools

• Buy-in by industry

80


CommercializationBrooks

• Talked to Joey Skinner (December 2001), and to Randy (?) in April, 2002.

– They: Have first buy-in from several companies (develop market and tools for them)

– Us: Does not appear to fit within what they offer.

– Us: our tools may just need to work well with ASAP.

81


CommercializationAdexa

• Initial discussion with Lia Minelli (Bockert), April:

• May fit well within their “Factory Planning and Scheduling” solutions

• Their tools use optimization procedures

• Second round of discussions involving Simon Tunmore, VP of Strategic Enterprise.

• NDA signed, papers exchanged, discovery phase initiated: May 13 …

• November 11: willing to continue talks …

82


CommercializationIbex Processes

• Start-up Company (2-3 year-old, 16 employees):

• www.ibexprocess.com

• Has an ISMT sponsored project on “Predictive Maintenance,” based on neural network/statistical techniques.

• Our tools could work with theirs, or be stand-alone complements.

• Made connection by referral from ST Micro Electronics in Phoenix.

• Several teleconferences: CEO, COB, Chief Scientist.

• Meeting at San Jose Informs.

• Significant level of interest.

83


CommercializationPlan

1. Clarify what is proprietary information and what is “transferable”

2. Clarify license and IP transfer legal issues with SRC and Universities

3. Continue talks with Adexa and Ibex

4. Other?

Documents

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