Data Visualization and Fault Detection Using Bi-Kernel t-Distributed Stochastic Neighbor Embedding in Semiconductor Etching Processes

In semiconductor etching processes, fault detection monitors the quality of wafers. However, the detailed dynamics in batch data are ignored in many traditional methods. In this paper, sequential image-based data visualization and fault detection, using bi-kernel t-distributed stochastic neighbor embedding (t-SNE), is proposed for semiconductor etching processes. In the proposed method, multi-modals, multi-phases, and abnormal samples in batches are visualized in two-dimensional maps. First, the batch data are restructured into sequential images and input to a convolutional autoencoder (CAE) to learn the abstract representation. Then, bi-kernel t-SNE is applied to visualize the CAE codes in two-dimensional maps. To reduce the computational burden and overcome the out-of-sample projection diffusion in bi-kernel t-SNE, data subsampling is used in the training procedure. Finally, a one-class support vector machine is employed to calculate the visualization control boundary, and a batch-wise index is presented for fault wafer detection. To demonstrate the feasibility and effectiveness of the proposed method, it was applied to two wafer etching datasets. The results indicate that the proposed method outperforms state-of-the-art methods in data visualization and fault detection.