SRC/ISMT FORCe:Factory Operations Research Center Task NJ877. Michael Fu, Director Emmanuel Fernandez Steven I. Marcus San Jose, CA, Nov. 2021, 2002. Intelligent Preventive Maintenance Scheduling in Semiconductor Manufacturing Fabs. CONTENTS.
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SRC/ISMT FORCe:Factory Operations Research CenterTask NJ877
Michael Fu, Director
Emmanuel Fernandez Steven I. Marcus
San Jose, CA, Nov. 2021, 2002
Intelligent Preventive Maintenance
Scheduling in Semiconductor
Manufacturing Fabs
CONTENTS
Michael Fu, Ph.D.Institute for Systems ResearchUniversity of MarylandProject Overview/Status
Summary
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.
Faculty:
Students:
Year 1Implementing 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 simulationbased 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
(Proposed)
1.Survey of current PM practices in industry
(Report) (P:15DEC2001)
2. Models and algorithms to cover bottleneck
tool sets in a fab(Report) (P:31MAR2002)
3.Simulation engine implemented in commercially available
software, with case studies and benchmark data (Report) (P:30SEP2002)
4. PM planning/scheduling software tools, with accompanying
simulation engine (Software, Report) (P:30JUN2003)
5. Installation and evaluation, workshop and consultation
(Report) (P:31DEC2003)
Overview
SRC Deliverables List
Emmanuel Fernandez, Ph.D.ECECS DepartmentUniversity of CincinnatiOverview of Software Tools and Summer Internships
Summary
Outline
“Recent Accomplishments” List:
April 2002 Review
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; 29Jul2002; 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; 29Jul2002; 6pp.; (Pub P004306); Task 877.001[Abstract] [Document] (786k)
PMOST
Overview
PMOST Overview
PMOST
Diagram
PMOST
PMOST
Executable Version of PMOST
PMOSTDemo Screen Views
PMOSTDemo Screen Views
PMOSTDemo Screen Views
PMOSTDemo Screen Views
PMOSTDemo Results
PMOSTDemo Results
Jason Crabtree
M.S. Student, Univ. of Cincinnati
Thanks to:
José A. RamírezPh.D. Student, University of Cincinnati
Outline
Completed Work
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).
Summer Internship
Summer Internship
Wafer to Calendarbased PMs conversion
Wafer to Calendarbased PMs conversion
WaferBased PM window definition in time/wafer line:
Wafer to Calendarbased PMs conversion
400
200
300
.
. .
. . .
tP+1
tP
100
. . .
. . .
. . .
09/06/2002
Estimated dates
09/04/2002
09/07/2002
09/01/2002
09/02/2002
t0
Throughput rate (wafers/hour)
WIP(hours)
Inputs and Outputs
Additional Information obtained:
A set of tools
Initial PM schedule*
Planning horizon
Projected Incoming WIP
Chambers configurationTool Parameters
Optimization
Scheduling
Model/algorithm
Optimized PM Schedule
Estimated Availability
Estimated WIP
*Estimated for waferbased PM’s
Algorithm Flow Chart
Begin
.ini file;
.tool file;
.item file;
System Initialization
and selecting a
machineFamily
Generating consolidated tasks vector set {v}
Specifying a planning horizon
.chm file
(Chamber Scenario)
Computing availability loss and resources requirement for each task vector.
Wafer to calendarbased PM’s conversion process
Reading in Fab database, performing data filtering
Fab
database
SIMPLEX and BranchandBound algorithms are used in the default solver
Generating MIP model instance in a standard format
wip file
Reading in projected WIP
Invoking OSL default solver to solve
the MIPmodel
Resource
Data File
(manpower.txt)
Reading in projected resource
Parsing model solution and interpreting the
result to users
Fab
database
End
Conversion process: software flow diagram
Begin
Generates and read waferbased PM windows file, count number of PM tasks
Inputs:

Planning Horizon

Tool Family definition
Compute number of
Compute the planning horizon time length
periods in the
planning horizon
Compute warning, due and late dates estimations
WIP file
incomplete
Check and
End
read WIP file
WIP file OK
Write a new Fabdatabase containing estimated due dates
Read number of tools
End
and chamber
configuration file
Read
Fab
Database
Begin
Select Tool Group
Confirm PM Items
Enter Planning Dates
LP Solved On Remote Server
WaferBased PM’s Conversion Process
Confirm Optimal Schedule
Enter Manpower Schedule
Update Fab Database Records
Run Optimization
End
Simulation Study
Simulation Study
Calendar based PMs – Lithographic Process
Simulation Study
Wafer based PMs – Metal Deposition tools
**This work week have a more complex scenario for PM tasks.
Conclusions
Current work and Future Tasks
Current work:
Future Work:
Xiaodong YaoPh.D. Student, Univ. of MarylandOptimal Preventive Maintenance Policy for Unreliable Queueing systems with Applications to Semiconductor Manufacturing Fabs
Outline
Timewindow policy: PM conducted within a time window,
according to a distribution
Example: uniform distribution.
Problem Setting
failure
PM
Tp
Tf
T
t
Machine
newest
state
Machine
newest
state
Machine
newest
state
Consider an unreliable machine,
Objective: determine PM policy G(t) to minimize longterm average cost
Optimality of NonRandomized Policy
For the case of instantaneous repairs and PMs, i.e., Tf=Tp=0,
Barlow and Proschan (1965) derive a nonrandomized optimal policy.
We have extended this result to our setting.
By renewal theory, the average cost is
Proposition: There exists a nonrandomized optimal policy that minimizes the
average cost, i.e., the distribution function G(t) degenerates to a point mass
at (can be infinite).
u
d
Combined PM and Production Policy
for Unreliable Production Systems
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 controllimit
form.
Property 3: For fixed age, there exists an inventory threshold level such that
above the threshold, it is not optimal to produce.
u
d
OperationDependent Failures
Operationdependent failures:
Machine deteriorates only when it is producing, and
can not fail while idle;
Timedependent failures:
Machine deteriorates whether or not producing, and
can fail while idle.
Then we can show property 2 (controllimit 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(·)
Optimal vs. Heuristic Policies
Optimal policy: The policy derived from Bellman Equations.
DoubleThreshold 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.
SingleThreshold policy (timewindow 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:
Summary: optimality gap < 1% for optimal doublethreshold policy
~ 4% for optimal singlethreshold policy.
Appendix: DiscreteTime Model for
Unreliable Production System
Bellman Equations
Structural Results with J Function
Emmanuel Fernandez, Ph.D.ECECS DepartmentUniversity of CincinnatiTransfer to Commercialization Plan
Commercialization
Commercialization
Commercialization
Commercialization
Brooks
Commercialization
Adexa
Commercialization
Ibex Processes
Commercialization
Plan