In　the　semiconductor　wafer　manufacturing　process,　it　is　necessary　to　inspect　the　electrical　parameters　and　functions　of　the　wafer　to　identify　the　defects　in　the　chip　manufacturing　process.　The　inspection　results　are　presented　in　the　form　of　wafers;　therefore,　the　accuracy　of　wafer　defect　recognition　directly　affects　the　chip　yield.　Traditional　manual　methods　suffer　from　subjectivity,　inefficiency,　and　diminished　accuracy.　With　improvements　in　computing　power,　computer　vision　based　on　convolutional　neural　networks　has　demonstrated　notable　advantages　in　defect　recognition.　Nonetheless,　with　the　development　of　Moore＇s　law　and　the　continuous　increase　in　wafer　size,　it　continues　to　contend　with　challenges　regarding　the　accurate　identification　of　mixed,　complex　types　of　wafer　defects　and　necessitates　distinct　training　for　each　defect　type,　a　process　that　is　both　laborious　and　time-intensive.　In　this　study,　we　introduce　a　new　deep　learning　model　called　the　Deep　Attention　Pyramid　Network　(DAP-Net),　which　is　based　on　depth-wise　separable　convolution　and　combines　an　improved　coordinate　attention　mechanism　with　a　multi-scale　convolution　structure　to　identify　single　and　mixed　types　of　wafer　defects.　Our　model　achieved　an　impressive　recognition　accuracy　of　98.6%　when　evaluated　on　a　mixed-type　defect　dataset　(Mixed38WM),　surpassing　the　performance　of　most　previously　reported　deep　learning　models.