Random projection based k Nearest Neighbor rule for semiconductor process fault detection
Fault detection technique is essential for improving overall equipment efficiency of semiconductor manufacturing industry.It has been recognized that fault detection based on k nearest neighbor rule(kNN)can deal with some unique characteristics of semiconductor processes,such as multimode batch trajectories and nonlinearity.However,the computation complexity and storage space required in neighbors searching of kNN prevent it from online monitoring,especially for high dimensional cases.To deal with it,principal component based kNN is also presented in literature,in which dimension reduction by principal component analysis(PCA)is done before kNN rule applied to perform fault detection.However,the process of dimension reduction by PCA may distort the distances of pairwise samples(trajectories).Thus the performance of kNN for fault detection degenerates after projection by PCA.To overcome this drawback,we propose a new fault detection method based on random projection and kNN rule,which combines the advantages of random projection in distance preservation and kNN rule in dealing with the problems of multimodality and nonlinearity that often coexist in semiconductor processes.Industrial example illustrates the performance of the proposed method.
Fault Detection k Nearest Neighbor Random Projection Distance Preservation
ZHOU Zhe YANG Chunjie WEN Chenglin
Zhejiang University,Hangzhou 310027,P.R.China Hangzhou Dianzi University,Hangzhou 310018,P.R.China
国际会议
The 33th Chinese Control Conference第33届中国控制会议
南京
英文
3169-3174
2014-07-28（万方平台首次上网日期，不代表论文的发表时间）