Src ismt force factory operations research center task nj 877
1 / 44

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

  • Uploaded on

SRC/ISMT FORCe:Factory Operations Research Center Task NJ-877. Michael Fu, Director Emmanuel Fernandez Steven I. Marcus Crystal City, VA, December 13-14, 2001. Intelligent Preventive Maintenance Scheduling in Semiconductor Manufacturing Fabs. CONTENTS.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' SRC/ISMT FORCe:Factory Operations Research Center Task NJ-877' - candra

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Src ismt force factory operations research center task nj 877

SRC/ISMT FORCe:Factory Operations Research CenterTask NJ-877

Michael Fu, Director

Emmanuel Fernandez Steven I. Marcus

Crystal City, VA, December 13-14, 2001

Intelligent Preventive Maintenance

Scheduling in Semiconductor

Manufacturing Fabs


  • Project Summary - Michael Fu

  • Optimal Preventive MaintenanceScheduling Model - Emmanuel Fernandez

  • Generic MIP Model

  • Implementation - Jason Crabtree

  • Optimal Preventive Maintenance Policy for Unreliable Queueing systems with Applications to Semiconductor Manufacturing Fabs - Xiaodong Yao

  • “Best Practices” PM Survey -Emmanuel Fernandez

  • SMITLab and Project Web Page - Jose A. Ramirez

  • NJ 877 FORCe Kick-off Meeting Presentation 04-26-01

  • Papers:

    • Optimization of Preventive Maintenance Scheduling for Semiconductor Manufacturing Systems: Models and Implementation

    • A Markov Decision Process Model for Capacity Expansion and Allocation

Michael fu ph d professor school of business univ maryland project summary

Michael Fu, Ph.D.Professor, School of Business., Univ. MarylandProject Summary


Research Plan

(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.

Executive summary
Executive Summary

  • Successful Systems Integration of Software Implementation of PM Scheduling Algorithm at Member Company in 2001

  • PM Practices Survey Instrument Developed, Distributed, and Preliminary Data Analysis Underway

  • Project Website Up and Running

  • In Progress Research

    • Generic Implementation of PM Scheduling Algorithm

    • PM Planning Models (Analytical and Simulation-Based)

Project Overview

  • Review of Project Administration

    • - Industrial Liaisons, Research Personnel

    • - Project Management

    • - Research Progress

      • Anticipated Results, Deliverables

      • Task Description, Past Year, Next Year

      • Technology Transfer

  • Review/Summary of Research Approach

  • Research Details

  • “Best Practices” PM Survey

Industrial liaisons
Industrial Liaisons

  • Gurshaman S. Baweja, Texas Instruments Incorporated

  • Ben-Rachel Igal, Intel Corporation

  • Mani Janakiram, Intel Corporation

  • Ying Tat Leung, IBM Corporation

  • Marcellus Rainey, Texas Instruments Incorporated

  • Madhav Rangaswami, Intel Corporation

  • Ramesh Rao, National Semiconductor Corporation

  • Man-Yi Tseng, Advanced Micro Devices, Inc.

  • Jan Verhagen, Philips Corporation

  • Sidal Bilgin, LSI

  • Motorola still being decided

Research personnel
Research Personnel


  • Michael Fu, Maryland

  • Steve Marcus, Maryland

  • Emmanuel Fernandez, Cincinnati


  • Xiaodong Yao, Maryland (advanced PhD)

  • Ying He, Maryland (advanced PhD)

  • Jiaqiao Hu, Maryland (beginning PhD)

  • Jason Crabtree, Cincinnati (advanced MS)

  • Jose Ramirez, Cincinnati (beginning PhD)

Project management
Project Management

  • Weekly Site Meetings at Maryland & Cincinnati

  • Weekly Teleconferences UMD & UC

  • Monthly Teleconferences With Liaisons and PI’s

  • Project Website


Anticipated primary results
Anticipated Primary Results

  • Models, algorithms and a suite of software tools will be developed to automate and/or guide in optimally planning, scheduling and coordinating PM tasks for bottleneck tools in a semiconductor fab.

  • Software will be designed for seamless integration with

    existing commercial discrete-event simulation software packages such as AutoSched AP and WorkStream.

Task description
Task Description

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.

Deliverables to Industry

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)

Past year progress and accomplishments
Past Year Progressand Accomplishments

  • Two student internships at member company: - successful implementation of PM scheduling algorithm - tested and validated with ASAP simulation and real data - integrated with MES and PM monitoring databases

  • PM Practices Survey Instrument Developed and Distributed

    • - thus far, 12 responses from 8 companies and 5 countries

    • - very diverse answers (more details later)

  • Investigated analytical (MDP models and queueing models) and simulation-based approaches to PM planning problem

  • Developing generic implementation of PM Scheduling Algorithm and IT implementation

Next year plans
Next Year Plans

  • Complete implementation of generic PM scheduling algorithm

    • - further student internships at member companies to implement, test, and evaluation models and algorithms

    • - begin facilitating possible transfer to software vendors

  • Complete PM Practices Survey Data Analysis and Report,

    • - make available to member companies

    • - utilize in research on models and algorithms (see next bullet)

  • Develop MDP and queueing models, in conjunction with simulation-based approaches, for PM planning problem, focusing on bottleneck tool sets in the fab

Technology transfer
Technology Transfer

  • Software Developed

    • MIP model for PM scheduling algorithm in OSL

    • ASAP cluster tool model to validate PM scheduling algorithm

    • Subroutines to integrate various databases

  • Conference Presentations

    • INFORMS International Conference, 6/01

    • Conference on Control Applications, 9/01

    • SRC/ISMT FORCe Annual Review Meeting, 12/01

  • Publications

    • X. Yao, M.C. Fu, S.I. Marcus, and E. Fernandez, “Optimization of Preventive Maintenance Scheduling for Semiconductor Manufacturing Systems: Models and Implementation,'‘ Proceedings of the 2001 IEEE Conference on Control Applications, 407-411.

Emmanuel Fernandez, Ph.D.Associate Prof., ECECS Dept., Univ. CincinnatiOptimal Preventive MaintenanceScheduling Model

Model Overview

Project Background


  • Scheduling Preventive Maintenance(PM) tasks for cluster tools is a very complicated and challenging job.

  • Many factors, like manpower constraints, projected upstream and downstream WIP, chamber inter-relationships, etc., just to name a few, come into the decision process.

  • A wealth of information is ready for use from the tool monitoring system (TMS), the wafer dispatching system (WDS), etc.


  • To provide a computer-aided decision making support tool for cluster tools’ PM scheduling.

  • Summer Internships: Xiaodong Yao and Jason Crabtree at AMD from June to August, 2001. Faculty visits to industry.

Scheduling Algorithm


  • A mixed integer program (MIP) has been formulated to address the optimization problem for cluster tools’ Preventive Maintenance (PM) scheduling.

  • Four main decision factors in current PM scheduling practice are identified: chambers status vs. tools throughput, PM windows, manpower constraints, and projected WIPs.

  • Optimization’s objective is to maximize profits via tools’ availability, and meanwhile factor into other costs such as from WIP.

  • The algorithm has been designed to work tightly with other information systems like TMS and WDS.

Algorithm kernel
Algorithm kernel

  • Mathematically, the kernel of our algorithm is a mixed integer program, which has been formulated to address the optimal scheduling problem, and can be solved by using a standard optimization package, e.g. IBM OSL Optimization Subroutine Library.

  • Obtained as the linear and non-stochastic version of MDP model formulation.

  • The algorithm will be searching for any feasible consolidation of PM tasks on each tool, choosing the “best” schedule in terms of maximizing tools availabilities (or tools throughputs), and meanwhile satisfying manpower constraints, not exceeding maximal WIP limit and trying to reduce overall WIP level.

Research Approach

The generic form of the problem of interest:

  • Where:

    • μ is a PM policy;

    • π is a production policy;

    • E[C] represents the expected total costs.

Model statement
Model Statement


Subject to:

Finding an optimal schedule

  • An optimal schedule will be chosen with maximal profits from tools availabilities (or throughputs) among all feasible schedules.

    A feasible schedule should satisfy:

  • Any scheduled PM tasks should be in its predefined PM window.

  • On any day, the total number of resources (e.g. headcount of maintenance technicians) required by that day’s schedule should not exceed the projected available resources.

  • On any day, the WIP level effected by projected incoming WIP and PM schedule on each machine should not exceed a predefined WIP limit, i.e. it will not schedule a PM task on a certain day when large WIP is expecting to arrive. A schedule with low WIP levels is preferred by the algorithm.

Simulation Study

  • A preliminary simulation study of comparison between a model-based schedule and a reference schedule using AutoSched AP has been conducted. Real fab data was used throughout the study.

  • The simulation study showed model-based schedule outperforms the reference schedule in general. Some significant improvement on tools’ availability was recorded in the study, for example, 14% improvement for a cluster tool was observed in the simulation.

Jason Crabtree

M.S. Student, Univ. CincinnatiGeneric MIP Model


Generic Implementation


To create a generic model and IT implementation based on experience thus far (models, internships, customizations)

Generic Implementation

  • The MIP model is designed to be very robust, thus it can handle a large variety of tools without changing the actual formulation of the model

  • The generic model can be separated into three facets:

    • Inputs

    • Scheduling algorithm

    • Outputs

  • Implementation of the generic model then consists of:

    • Formulating input data and defining interfaces to collect input data

    • Formatting input data into proper form for chosen MIP solver

    • Handling of solution data output from solver

Inputs and Outputs

A set of tools

Initial schedule

Planning horizon

Projected Incoming WIP




Optimized Schedule

Estimated Availability

Estimated WIP

Input Data:

  • A Tool family, which includes all relevant tools of interest, for example, a group of cluster tools in thin films module

  • Tool parameters, like throughput rate, maximal WIP level (limit), inventory cost if applicable

Input Data (cont.)

  • Monitored items, i.e. PM tasks of interest on all chambers

  • Information about PM’s duration (MTTR), manpower requirement, cost etc.

  • Chambers scenarios and their effects on whole tool’s throughput (availability)

  • Planning horizon

  • PM tasks initial schedule, each associated with a time window (warn-date, due-date and late-date)

  • Projected incoming WIP in the planning horizon

  • Projected manpower (maintenance technicians) available in the planning horizon


  • Optimal schedule, users can see the optimized schedule and the initial one in TMS for comparison.

  • An estimated availability of each tool in family will be presented along with an optimal schedule.

  • An estimated inventory level of each tool in family will be presented along with an optimal schedule.


.ini file;

.tool file;

.item file;

System Initialization

and selecting a


Generating consolidated tasks vector set {v}

Specifying a planning horizon

Chamber configuration

Computing availability loss and resources requirement for each task vector.

Reading in TMS database, performing data filtering



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

Generating MIP model instance in a standard format



Reading in projected WIP from WDS

Invoking OSL default solver to solve

the MIPmodel


Data File

Reading in projected resource

Parsing model solution and interpreting the

result to users



Algorithm Flow Chart









System Architecture

RPC(Remote Procedures Call)




Solver (OSL)




System Interfaces

User Interface:

  • Tool family is selected by user.

  • Planning period dates are entered by user.

  • Manpower schedule is input by user.

    Interface with TMS:

  • The TMS PM data file is searched by the software for PM items due within user-defined planning window.

  • A secondary “optimal” PM data file is updated by the software with the optimal dates from the optimization.

    Interface with WDS:

  • A projected WIP data file is extracted from WDS periodically.

  • The software will use updated WIP data each time when running the optimization algorithm.

Using the Software


Confirm PM Items

Select Tool Group

Enter Planning Dates

LP Solved On Remote Server

Confirm Optimal Schedule

Enter Manpower Schedule

Update PM Records

Run Optimization



  • The MIP model is very robust and can handle a wide range of tools

  • Implementation of the generic model consists of formulating and gathering the required input data, passing data to solver, and handling the solution data

  • The complexity of the final software package is currently being looked into. Keeping the software small promotes speed and allows customization but requires that software be tailored for each implementation. A larger software package enhances ease of integration but may limit robustness of software.

Xiaodong YaoPh.D. Student, Univ. MarylandOptimal Preventive Maintenance Policy for Unreliable Queueing systems with Applications to Semiconductor Manufacturing Fabs

Modeling Framework for PM planning and scheduling

  • Two-stage hierarchical framework under development:

    • In the 1st stage (higher level), the objective is to investigate the structure of optimal policies for PMs, (for example, PM window with optimal parameters t* and Dt*), in the presence of stochastic dynamics of machine failure and demand processes.

    • In the 2nd stage (lower level), the objective is to determine exact time to perform PM tasks, taking into consideration the inter-dependency of PMs, resource constraints, short-term projected WIP, etc. A mixed integer program (MIP) has been successfully developed, and implemented in a specific environment.


  • (Semi-)Markov Decision Processes (MDP) model on 1st stage:

    • Machines: queueing systems, fab: queueing network

    • Incorporating system “operating” states (e.g. WIP level, queue length) and “technical” states (e.g. deterioration degree of machines) in PM policy

    • Observable vs. unobservable machine states. Unobservable states can be estimated based on information from SPC (statistic process control) or number of wafers produced since last “renewal” state.

    • Stochastic process for machine’s deterioration, e.g., controlled Markov chain.

    • Independent demand process

    • Random times (non-negligible) for PM tasks

    • Cost structure: PM cost, operating cost, and inventory holding cost, etc.

Structural Optimal Policy

  • Structure of optimal policy:

    • To be investigated with the formulation of MDP models

    • Monotonicity, e.g. control-limit form and/or switching curve, is promising and interesting

    • Effect of system “operating” states on PM policy to be studied.

    • Comparison study between the derived optimal policy and (t*, Dt*) policy

    • Derivation of parameterized policy

Simulation-based Optimization

  • Simulation-based optimization for parameterized policy:

    • Monte Carlo simulation is effective for large optimization problems.

    • Efficient gradient estimation will be employed in stochastic approximation algorithm in search for optimal policy parameters, e.g. threshold values in control-limit policies.

    • Unbiased gradient estimators for system performance w.r.t. structural policy parameters, e.g. (t*, Dt*) will be obtained.

Emmanuel fernandez ph d associate prof ececs dept univ cincinnati best practices pm survey

Emmanuel Fernandez, Ph.D.Associate Prof., ECECS Dept., Univ. Cincinnati“Best Practices” PM Survey

  • PM Practices Survey Instrument Developed and Distributed

  • Thus far, 12 responses from 8 companies and 5 countries

  • Both commonality and divergence in different issues

Jose a ramirez ph d student univ cincinnati smitlab and project web page http www smitlab uc edu

Jose A. RamirezPh.D. Student, Univ. CincinnatiSMITLab and Project Web Page