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 2 GCForest识别器参数灵敏度分析 |
| Tab.2 Parameter sensitivity analysis of GCForest |
|
| 参数名称 | 参数大小 | Racc/% | | 滑动窗口大小 | 400 | 97.31 | | 700 | 96.7 | | 1 000 | 96.2 | | 1 300 | 96.4 | | 1 664 | 96.2 | 决策树生成的 最小样本数 | 0 | 96.2 | | 0.1 | 96.7 | | 0.2 | 96.2 | | 0.3 | 95.8 | | 0.4 | 95.4 | 决策树生成的 允许误差 | 0 | 96.2 | | 0.1 | 96.7 | | 0.2 | 96.2 | | 0.3 | 95.8 | | 0.4 | 95.4 | 扫描层随机森林的 决策树数量 | 200 | 96.9 | | 400 | 95.8 | | 600 | 97.0 | | 800 | 96.7 | | 1 000 | 96.0 |
|
|
|