Adaptive dispatching rule for semiconductor wafer fabrication facility

Li Li, Zijin Sun, Mengchu Zhou, Fei Qiao

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

Uncertainty in semiconductor fabrication facilities (fabs) requires scheduling methods to attain quick real-time responses. They should be well tuned to track the changes of a production environment to obtain better operational performance. This paper presents an adaptive dispatching rule (ADR) whose parameters are determined dynamically by real-time information relevant to scheduling. First, we introduce the workflow of ADR that considers both batch and non-batch processing machines to obtain improved fab-wide performance. It makes use of such information as due date of a job, workload of a machine, and occupation time of a job on a machine. Then, we use a backward propagation neural network (BPNN) and a particle swarm optimization (PSO) algorithm to find the relations between weighting parameters and real-time state information to adapt these parameters dynamically to the environment. Finally, a real fab simulation model is used to demonstrate the proposed method. The simulation results show that ADR with constant weighting parameters outperforms the conventional dispatching rule on average; ADR with changing parameters tracking real-time production information over time is more robust than ADR with constant ones; and further improvements can be obtained by optimizing the weights and threshold values of BPNN with a PSO algorithm.

Original languageEnglish (US)
Article number6374713
Pages (from-to)354-364
Number of pages11
JournalIEEE Transactions on Automation Science and Engineering
Volume10
Issue number2
DOIs
StatePublished - Jan 1 2013

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Automated manufacturing system
  • neural network
  • particle swarm optimization
  • scheduling
  • semiconductor manufacturing

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