关键词: 判别核主元, k近邻, 批次过程, 故障检测, 半导体

Abstract: Aiming at the nonlinear and multi-mode features of batch processes, a fault detection method for batch process based on k Nearest Neighbors (kNN) rule in Discriminated kernel Principle Component space, namely Dis-kPCkNN, was proposed. Firstly, in kernel Principal Component Analysis (kPCA), according to discriminating category labels, the kernel window width parameter was selected between within-class width and between-class width, thus the kernel matrix can effectively extract data correlation features and keep accurate category information. Then kNN rule was used to replace the conventional T² statistical method in the kernel principal component space, which can deal with fault detection of process with nonlinear and multi-mode features. Finally, the proposed method was validated in the numerical simulation and the semiconductor etching process. The experimental results show that the kNN rule in discriminated kernel principle component space can effectively deal with the nonlinear and multi-mode conditions, improve the computational efficiency and reduce memory consumption, in addition, the fault detection rate is significantly better than the comparative methods.

Key words: discriminated kernel principle component, k Nearest Neighbors (kNN), batch process, fault detection, semiconductor