Joong Jae Lee , O Bum Kwon , Gye Young Kim

KIPS Transactions on Software and Data Engineering, Vol. 11, No. 3, pp. 709-716, Mar. 2004
10.3745/KIPSTD.2004.11.3.709, Full Text:

Abstract

This paper describes an intelligent real-time monitoring system of a semiconductor processing equipment, which determines normal or not for a wafer in processing, using multiple time-series pattern recognition. The proposed system consists of three phases, initialization, learning and real-time prediction. The initialization phase sets the weights and the effective steps for all parameters of a monitoring equipment. The learning phase clusters time series patterns, which are producted and gathered for processing wafers by the equipment, using LBG algorithm. Each pattern has an ACI which is measured by a tester at the end of a process. The real-time prediction phase corresponds a time series entered by real-time with the clustered patterns using Dynamic Time Warping, and finds the best matched pattern. Then it calculates a predicted ACI from a combination of the ACI, the difference and the weights. Finally, it determines Spec in or out for the wafer. The proposed system is tested on the data acquired from etching device. The results show that the error between the estimated ACI and the actual measurement ACI is remarkably reduced according to the number of learning increases.

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