Aiming　at　nonlinearity　and　multimodal　batch　trajectories　in　semiconductor　manufacturing　processes,　principal　component　analysis　and　k　nearest　neighbor　(kNN)-related　methods　were　previously　presented.　However,　these　methods　require　data　unfolding　and　are　not　capable　of　extracting　crucial　features,　which　affects　the　performance　of　fault　detection.　In　this　paper,　an　automated　fault　detection　method　using　convolutional　auto　encoder　(CAE)　and　k　nearest　neighbor　rule　is　proposed.　Firstly,　data　collected　in　one　batch　is　considered　as　a　two-dimensional　gray-scale　image,　and　is　input　to　CAE　for　feature　unsupervised　learning,　with　no　need　of　data　preprocessing　and　data　labels.　Secondly,　kNN　rule　is　incorporated　into　CAE　to　construct　the　monitoring　index　and　perform　fault　detecting.　Finally,　the　effectiveness　of　the　proposed　method　is　verified　with　a　benchmark　semiconductor　manufacturing　process.