Name
Abstract | ||
|---|---|---|
Lithography simulation is computationally expensive for hotspot detection. Machine learning-based hotspot detection is a promising technique to reduce the simulation overhead. However, most learning approaches rely on a large amount of training data to achieve good accuracy and generality. At the early stage of developing a new technology node, the amount of data with labeled hotspots or nonhotspo... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCAD.2019.2912948 | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
Keywords | DocType | Volume |
Training,Layout,Lithography,Machine learning,Feature extraction,Labeling,Training data | Journal | 39 |
Issue | ISSN | Citations |
7 | 0278-0070 | 1 |
PageRank | References | Authors |
0.34 | 0 | 6 |