Wafer　manufacturing　is　an　important　step　in　quality　control　and　analysis　in　the　semiconductor　industry.　The　defect　pattern　classification　algorithm　of　wafer　maps　has　received　extensive　attention　from　academia　and　industry.　At　present,　most　methods　for　detecting　wafer　surface　defect　patterns　focus　on　static　data　model　classification　and　analysis.　However,　in　the　production　process,　static　data　models　cannot　satisfy　the　dynamic　analysis　of　wafer　defect　patterns　in　the　form　of　streaming　data.　In　this　regard,　this　paper　proposes　a　wafer　surface　defect　pattern　detection　method　based　on　incremental　learning.　Our　experiment　uses　Resnet　as　the　backbone　network,　and　the　data　set　uses　the　WM811K　wafer　data　set.　Experiments　have　proved　that　our　method　can　achieve　better　classification　accuracy　in　the　field　of　wafer　defect　detection,　which　provides　the　possibility　for　continuous　learning　of　wafer　defects　in　the　future.　©　2021　Institute　of　Physics　Publishing.　All　rights　reserved.