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http://dx.doi.org/10.3745/KIPSTD.2004.11D.3.709

An Intelligent Monitoring System of Semiconductor Processing Equipment using Multiple Time-Series Pattern Recognition  

Lee, Joong-Jae (숭실대학교 대학원 컴퓨터학과)
Kwon, O-Bum ((주)임프 연구)
Kim, Gye-Young (숭실대학교 컴퓨터학부)
Publication Information
The KIPS Transactions:PartD / v.11D, no.3, 2004 , pp. 709-716 More about this Journal
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 tile effective steps for all parameters of a monitoring equipment. The learning phase clusters time series patterns, which are producted and fathered 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.
Keywords
Semiconductor; Processing Equipment; Monitoring System; Pattern Recognition; DTW(Dynamic Time Warping); LBG(Linde-Buzo-Gray); Clustering;
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