Detecting　wafer　map　anomalies　is　crucial　for　preventing　yield　loss　in　semiconductor　fabrication,　although　intricate　patterns　and　resource-intensive　labeled　data　prerequisites　hinder　precise　deep-learning　segmentation.　This　paper　presents　an　innovative,　unsupervised　method　for　detecting　pixel-level　anomalies　in　wafer　maps.　It　utilizes　an　efficient　dual　attention　module　with　a　knowledge　distillation　network　to　learn　defect　distributions　without　anomalies.　Knowledge　transfer　is　achieved　by　distilling　information　from　a　pre-trained　teacher　into　a　student　network　with　similar　architecture,　except　an　efficient　dual　attention　module　is　incorporated　atop　the　teacher　network＇s　feature　pyramid　hierarchies,　which　enhances　feature　representation　and　segmentation　across　pyramid　hierarchies　that　selectively　emphasize　relevant　and　discard　irrelevant　features　by　capturing　contextual　associations　in　positional　and　channel　dimensions.　Furthermore,　it　enables　student　networks　to　acquire　an　improved　knowledge　of　hierarchical　features　to　identify　anomalies　across　different　scales　accurately.　The　dissimilarity　in　feature　pyramids　acts　as　a　discriminatory　function,　predicting　the　likelihood　of　an　abnormality,　resulting　in　highly　accurate　pixel-level　anomaly　detection.　Consequently,　our　proposed　method　excelled　on　the　WM-811K　and　MixedWM38　datasets,　achieving　AUROC,　AUPR,　AUPRO,　and　F1-Scores　of　(99.65%,　99.35%),　(97.31%,　92.13%),　(90.76%,　84.66%)　respectively,　alongside　an　inference　speed　of　3.204　FPS,　showcasing　its　high　precision　and　efficiency.