Semiconductor　manufacturing　is　a　very　complex　dynamic　manufacturing　process.　Wafer　manufacturing　is　the　most　critical　part　of　it.　Abnormalities　in　any　process　in　wafer　production　may　cause　wafer　defects.　Accurately　identifying　various　defect　patterns　on　the　wafer　surface　can　help　find　and　adjust　abnormal　factors　in　the　online　manufacturing　process　and　improve　the　yield　of　integrated　circuit　production.　Aiming　at　the　very　small　number　of　wafer　defect　patterns　that　appear　in　the　wafer　manufacturing　process,　this　paper　proposes　a　small　sample　wafer　defect　pattern　classification　method　based　on　a　hybrid　self-attention　mechanism　and　a　prototype　network.　This　method　introduces　a　hybrid　self-attention　module　into　the　feature　embedding　network　that　helps　to　automatically　learn　the　global　dependence　of　channels　and　positions　in　the　wafer　map　to　obtain　more　discriminative　features.　The　metric　classifier　is　used　for　classification　to　realize　the　pattern　recognition　of　wafer　defects　in　small　samples.　A　large　number　of　experiments　were　conducted　on　the　WM-811K　data　set　and　the　MixedWM38　data　set.　The　recognition　accuracy　of　this　method　in　the　3-way　1-shot,　3-way　5-shot,　5-way　1-shot　and　5-way　5-shot　tasks　achieved　average　classification　accuracy　rates　of　89.99%,　94.96%,　85.77%,　and　92.32%,　respectively.　The　experimental　results　show　that　compared　with　the　traditional　deep　learning　method,　the　method　in　this　paper　can　learn　new　defect　pattern　features　and　realize　automatic　classification　only　through　a　small　number　of　wafer　samples.　©　2021　IEEE