Name
Abstract | ||
|---|---|---|
We propose a deep learning-based data-driven framework consisting of two convolutional neural networks: 1) LithoNet that predicts the shape deformations on a circuit due to IC fabrication and 2) OPCNet that suggests IC layout corrections to compensate for such shape deformations. By learning the shape correspondences between pairs of layout design patterns and their scanning electron microscope (S... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCAD.2020.3015469 | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
Keywords | DocType | Volume |
Layout,Integrated circuit modeling,Fabrication,Shape,Computational modeling,Lithography | Journal | 40 |
Issue | ISSN | Citations |
5 | 0278-0070 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao-Chiang Shao | 1 | 9 | 4.97 |
| Chao-Yi Peng | 2 | 0 | 0.34 |
| Jun-Rei Wu | 3 | 0 | 0.34 |
| Chia-Wen Lin | 4 | 1639 | 120.23 |
| Shao-Yun Fang | 5 | 116 | 17.07 |
| Pin-Yian Tsai | 6 | 0 | 0.34 |
| Yan-Hsiu Liu | 7 | 0 | 0.34 |