A genetic algorithm approach to manage ion implantation processes in wafer fabrication

Shwu Min Horng, John Fowler, Jeffery K. Cochran

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

The management of ion implantation processes is one of several challenging problems in scheduling wafer fabrication facilities. A complicating factor is the fact that there are sequence dependent set-ups (e.g. species changes). Because of the set-ups, it is sometimes desirable to leave an implanter idle (if another lot requiring this species will arrive soon) rather than to change the set-up. We study the use of a genetic algorithm (GA) to assign the jobs to machines where the First In First Out (FIFO) dispatching rule is used to schedule the individual machines. This approach is compared to the use of a commonly used dispatching policy-set-up avoidance. The parameters of the genetic algorithm (population size, crossover probability, and mutation probability) are analysed using response surface techniques to find combinations that allow the algorithm to determine a relatively good solution in a short CPU time.

Original languageEnglish (US)
Pages (from-to)156-172
Number of pages17
JournalInternational Journal of Manufacturing Technology and Management
Volume1
Issue number2-3
StatePublished - 2000

Fingerprint

Ion implantation
Genetic algorithms
Fabrication
Program processors
Scheduling
Genetic algorithm
Implantation
Wafer fabrication
Factors
Mutation
Schedule
Crossover
Dispatching rules
Avoidance
Dispatching
Response surface

Keywords

  • genetic algorithm
  • Ion implantation
  • scheduling
  • sequence dependent set-ups

ASJC Scopus subject areas

  • Strategy and Management
  • Information Systems and Management
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Computer Science Applications

Cite this

A genetic algorithm approach to manage ion implantation processes in wafer fabrication. / Horng, Shwu Min; Fowler, John; Cochran, Jeffery K.

In: International Journal of Manufacturing Technology and Management, Vol. 1, No. 2-3, 2000, p. 156-172.

Research output: Contribution to journalArticle

@article{3ca95a6e669849149febd98b8f28ea51,
title = "A genetic algorithm approach to manage ion implantation processes in wafer fabrication",
abstract = "The management of ion implantation processes is one of several challenging problems in scheduling wafer fabrication facilities. A complicating factor is the fact that there are sequence dependent set-ups (e.g. species changes). Because of the set-ups, it is sometimes desirable to leave an implanter idle (if another lot requiring this species will arrive soon) rather than to change the set-up. We study the use of a genetic algorithm (GA) to assign the jobs to machines where the First In First Out (FIFO) dispatching rule is used to schedule the individual machines. This approach is compared to the use of a commonly used dispatching policy-set-up avoidance. The parameters of the genetic algorithm (population size, crossover probability, and mutation probability) are analysed using response surface techniques to find combinations that allow the algorithm to determine a relatively good solution in a short CPU time.",
keywords = "genetic algorithm, Ion implantation, scheduling, sequence dependent set-ups",
author = "Horng, {Shwu Min} and John Fowler and Cochran, {Jeffery K.}",
year = "2000",
language = "English (US)",
volume = "1",
pages = "156--172",
journal = "International Journal of Manufacturing Technology and Management",
issn = "1368-2148",
publisher = "Inderscience Enterprises Ltd",
number = "2-3",

}

TY - JOUR

T1 - A genetic algorithm approach to manage ion implantation processes in wafer fabrication

AU - Horng, Shwu Min

AU - Fowler, John

AU - Cochran, Jeffery K.

PY - 2000

Y1 - 2000

N2 - The management of ion implantation processes is one of several challenging problems in scheduling wafer fabrication facilities. A complicating factor is the fact that there are sequence dependent set-ups (e.g. species changes). Because of the set-ups, it is sometimes desirable to leave an implanter idle (if another lot requiring this species will arrive soon) rather than to change the set-up. We study the use of a genetic algorithm (GA) to assign the jobs to machines where the First In First Out (FIFO) dispatching rule is used to schedule the individual machines. This approach is compared to the use of a commonly used dispatching policy-set-up avoidance. The parameters of the genetic algorithm (population size, crossover probability, and mutation probability) are analysed using response surface techniques to find combinations that allow the algorithm to determine a relatively good solution in a short CPU time.

AB - The management of ion implantation processes is one of several challenging problems in scheduling wafer fabrication facilities. A complicating factor is the fact that there are sequence dependent set-ups (e.g. species changes). Because of the set-ups, it is sometimes desirable to leave an implanter idle (if another lot requiring this species will arrive soon) rather than to change the set-up. We study the use of a genetic algorithm (GA) to assign the jobs to machines where the First In First Out (FIFO) dispatching rule is used to schedule the individual machines. This approach is compared to the use of a commonly used dispatching policy-set-up avoidance. The parameters of the genetic algorithm (population size, crossover probability, and mutation probability) are analysed using response surface techniques to find combinations that allow the algorithm to determine a relatively good solution in a short CPU time.

KW - genetic algorithm

KW - Ion implantation

KW - scheduling

KW - sequence dependent set-ups

UR - http://www.scopus.com/inward/record.url?scp=33750698473&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33750698473&partnerID=8YFLogxK

M3 - Article

VL - 1

SP - 156

EP - 172

JO - International Journal of Manufacturing Technology and Management

JF - International Journal of Manufacturing Technology and Management

SN - 1368-2148

IS - 2-3

ER -