Name
Abstract | ||
|---|---|---|
Wafer map defect pattern classification using convolutional neural network (CNN) has gained a lot of attention in recent years but it demands huge computation and memory cost. Therefore, a WDP-BNN framework based on the binarized neural network is proposed to reduce memory requirement by 29.70× and speed up by 1.66×. To overcome the imbalance problem and performance loss due to binarization of network, advanced data augmentation methods including (Chip Reverse, Chip Translate, Chip Combine) along with random under-sampling method have incorporated in the framework. Experimental results on the WM-811K dataset have demonstrated that the WDP-BNN model has outperformed the state-of-the-art works with the highest classification accuracy of 94.83% and the memory reduction of 1.10-25.93×. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.vlsi.2022.04.003 | Integration |
Keywords | DocType | Volume |
Wafer map,Defect pattern classification,Data augmentation,Binarized neural network | Journal | 85 |
ISSN | Citations | PageRank |
0167-9260 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qing Zhang | 1 | 0 | 1.35 |
| Yuhang Zhang | 2 | 0 | 0.68 |
| Jizuo Li | 3 | 0 | 0.68 |
| Yongfu Li | 4 | 0 | 0.68 |