Wafer　map　defect　pattern　contains　the　critical　information　about　semiconductor　manufacturing,　effective　defect　analysis　technology　can　improve　the　yield　of　products.　This　paper　develops　a　wafer　defect　pattern　recognition　and　analysis　method　based　on　convolutional　neural　network　(CNN).　We　build　an　8-layer　CNN　model　to　inspect　wafer　map　defect　and　a　13-layer　model　to　classify　defect　patterns.　To　analyze　the　defects,　we　first　extract　features　based　on　the　classification　network,　then　perform　PCA　dimensionality　reduction　and　similarity　ranking,　we　infer　the　root　causes　of　tested　samples　according　to　the　retrieved　wafer　maps.　In　particular,　we　analyze　the　impacts　of　different　network　layers　and　feature　dimensions　on　image　retrieval　performance,　it　turns　out　that　the　appropriate　dimensionality　reduction　can　increase　the　accuracy　and　speed　of　wafer　map　retrieval.　The　models　are　trained　and　tested　on　the　WM-811K　wafer　map　dataset,　which　are　collected　from　real　wafer　maps.　The　experimental　results　show　that　the　proposed　method　is　able　to　identify　common　wafer　defect　patterns　and　analyze　the　root　causes　accurately　and　effectively.