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Application of Symbolic Representation Method for Fault Detection and Clustering in Semiconductor Fabrication Processes  

Loh, Woong-Kee (성결대학교 멀티미디어학부)
Hong, Sang-Jeen (명지대학교 전자공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.15, no.11, 2009 , pp. 806-818 More about this Journal
Abstract
Since the invention of the integrated circuit (IC) in 1950s, semiconductor technology has undergone dramatic development up to these days. A complete semiconductor is manufactured through a diversity of processes. For better semiconductor productivity, fault detection and classification (FDC) has been rigorously studied for finding faults even before the processes are completed. For FDC, various kinds of sensors are attached in many semiconductor manufacturing devices, and sensor values are collected in a periodic manner. The collection of sensor values consists of sequences of real numbers, and hence is regarded as a kind of time-series data. In this paper, we propose an algorithm for detecting and clustering faults in semiconductor processes. The proposed algorithm is a modification of the existing anomaly detection algorithm dealing with symbolically-represented time-series. The contributions of this paper are: (1) showing that a modification of the existing anomaly detection algorithm dealing with general time-series could be used for semiconductor process data and (2) presenting experimental results for improving correctness of fault detection and clustering. As a result of our experiment, the proposed algorithm caused neither false positive nor false negative.
Keywords
symbolic representation; time-series; semiconductor process; fault detection; clustering; algorithm;
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