Learning to dispatch operations with intentional delay for re-entrant multiple-chip product assembly lines

Jaeseok Huh, Inbeom Park, Seongmin Lim, Bohyung Paeng, Jonghun Park, Kwanho Kim

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

As the demand for small devices with embedded flash memory increases, semiconductor manufacturers have been recently focusing on producing high-capacity multiple-chip products (MCPs). Due to the frequently re-entrant lots between the die attach (DA) and wire bonding (WB) assembly stages in MCP production, increased flow time and decreased resource utilization are unavoidable. In this paper, we propose a dispatcher based on artificial neural networks, which minimizes the flow time while maintaining high utilization of resources at the same time through exploiting the possible intentional delays on DA stage. Specifically, the proposed dispatcher learns the assignment preferences between available lots and DA resources based on assembly line data generated by using a simulator, then the proposed dispatcher performs lot dispatching decisions by considering the intentional delays. The numerical experiments were performed under various configurations of the MCP assembly lines, and the results show that the proposed dispatcher outperformed the existing methods.

Original languageEnglish
Article number4123
JournalSustainability (Switzerland)
Volume10
Issue number11
DOIs
StatePublished - 9 Nov 2018

Keywords

  • Artificial neural network
  • Assembly line
  • Intentional delay
  • Lot dispatching
  • Multiple-chip product
  • Semiconductor
  • Sustainability

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