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.