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JACIII Vol.19 No.1 pp. 58-66
doi: 10.20965/jaciii.2015.p0058
(2015)

Paper:

Comparison of Knowledge Acquisition Methods for Dynamic Scheduling of Wafer Test Processes with Unpredictable Testing Errors

Tsubasa Matsuo*, Masahiro Inuiguchi*, and Kenichiro Masunaga**

*Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan

**Renesas Electronics Co., 2-6-2 Otemachi, Chiyoda-ku, Tokyo 100-0004, Japan

Received:
April 20, 2014
Accepted:
August 25, 2014
Published:
January 20, 2015
Keywords:
semiconductor manufacturing, simulation, genetic-based machine learning
Abstract

The scheduling of semiconductor wafer testing processes may be seen as a resource constraint project scheduling problem (RCPSP), but it includes uncertainties caused by wafer error, human factors, etc. Because uncertainties are not simply quantitative, estimating the range of the parameters is not useful. Considering such uncertainties, finding a good situationdependent dispatching rule is more suitable than solving an RCPSP under uncertainties. In this paper we apply machine learning approaches to acquiring situation-dependent dispatching rule. We compare obtained rules and examine their effectiveness and usefulness in problems with unpredictable wafer testing errors.

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Last updated on Nov. 20, 2017