关键词: 统计模量, 局部近邻标准化, 局部离群因子, 多工况, 半导体过程

Abstract: A Local Outlier Factor fault detection method based on Statistics Pattern and Local Nearest neighborhood Standardization (SP-LNS-LOF) was proposed to deal with the problem of unequal batch length, mean drift and different batch structure of multi-process data. Firstly, the statistical pattern of each training sample was calculated; secondly, each statistical modulus was standardized as standard sample by using the set of local neighbor samples; finally the local outlier factor of standard sample was calculated and used as a detection index. The quintile of the local outlier factor was used as the detection control limit, when the local outlier factor of the online sample was greater than the detection control limit, the online sample was identified as a fault sample, otherwise it was a normal sample. The statistical pattern was used to extract the main information of the process and eliminate the impact of unequal length of batches; the local neighborhood normalization was used to overcome the difficulties of mean shift and different batch structure of process data; the local outlier factor was used to measure the similarity of samples and separate the fault samples from the normal samples. The simulation experiment of semiconductor etching process was carried out. The experimental results show that SP-LNS-LOF detects all 21 faults, and has higher detection rate than that of Principal Component Analysis (PCA), kernel PCA (kPCA), Fault Detection using k Nearest Neighbor rule (FD-kNN) and Local Outlier Factor (LOF) methods. The theoretical analysis and simulation result show that SP-LNS-LOF is suitable for fault detection of multimode process, and has high fault detection efficiency and ensures the safety of the production process.

Key words: statistical pattern, local neighborhood standardization, Local Outlier Factor (LOF), multimode, semiconductor process