基于迁移学习与深度森林的晶圆图缺陷识别

沈宗礼,余建波

Wafer map defect recognition based on transfer learning and deep forest

Zong-li SHEN,Jian-bo YU

表 3 DenseNet-GCForest算法的对比实验结果

Tab.3 Performance comparison of DenseNet-GCForest and other algorithms

% 实验方法 缺陷类别 Racc Rrec F DenseNet-GCForest Center 97.5 97.5 97.5 Donut 100 94.7 97.3 Edge_local 95.5 96.8 96.1 Edge_ring 98.7 95.1 96.9 Local 95.9 96.2 96.0 Near_full 94.1 94.1 94.1 None 99.0 99.6 99.3 Random 89.5 87.2 88.3 Scratch 96.2 93.8 94.9 GoogleNet Center 72.2 34.5 46.7 Donut 59.1 43.3 50.0 Edge_local 63.9 79.8 71.0 Edge_ring 93.4 87.6 90.4 Local 57.3 48.8 52.7 Near_full 93.3 93.3 93.3 None 90.6 92.9 91.7 Random 85.4 92.1 88.6 Scratch 81.7 81.7 81.7 ResNet Center 85.0 77.1 80.9 Donut 47.2 81.0 59.6 Edge_local 87.6 81.4 84.4 Edge_ring 94.3 86.6 90.3 Local 81.7 68.6 74.6 Near_full 75.0 75.0 75.0 None 89.9 99.3 94.4 Random 88.2 73.2 80.0 Scratch 59.5 71.0 64.4 DenseNet Center 76.4 85.5 80.7 Donut 95.2 64.5 76.9 Edge_local 79.7 91.2 85.0 Edge_ring 96.1 81.5 88.2 Local 82.1 66.8 73.6 Near_full 91.7 100 95.7 None 95.2 99.3 97.2 Random 86.4 65.5 74.5 Scratch 81.5 72.6 76.8 Decision TreeEnsemble Center 95.6 93.75 94.7 Donut 92.6 92.3 92.4 Edge_local 83.5 87.3 85.4 Edge_ring 86.8 91.1 88.9 Local 83.5 82.3 82.9 Near_full 89.4 91.7 90.5 None 100 99.5 99.7 Random 91.7 87.3 88.4 Scratch 86.0 88.5 87.2 SDAE Center 98.5 87.3 92.6 Donut 87.6 89.1 88.4 Edge_local 87.5 85.4 86.4 Edge_ring 98.8 94.7 96.7 Local 83.5 55.8 66.9 Near_full 85.3 96.2 90.4 None 99.0 98.7 98.9 Random 93.3 89.3 91.2 Scratch 81.7 39.6 53.3