Wafer　bin　map　(WBM)　inspection　is　a　critical　approach　for　evaluating　the　semiconductor　manufacturing　process.　An　excellent　inspection　algorithm　can　improve　the　production　efficiency　and　yield.　This　paper　proposes　a　WBM　defect　pattern　inspection　strategy　based　on　the　DenseNet　deep　learning　model,　the　structure　and　training　loss　function　are　improved　according　to　the　characteristics　of　the　WBM.　In　addition,　a　constrained　mean　filtering　algorithm　is　proposed　to　filter　the　noise　grains.　In　model　prediction,　an　entropy-based　Monte　Carlo　dropout　algorithm　is　employed　to　quantify　the　uncertainty　of　the　model　decision.　The　experimental　results　show　that　the　recognition　ability　of　the　improved　DenseNet　is　better　than　that　of　traditional　algorithms　in　terms　of　typical　WBM　defect　patterns.　Analyzing　the　model　uncertainty　can　not　only　effectively　reduce　the　miss　or　false　detection　rate　but　also　help　to　identify　new　patterns.