Paper Info
Title
Flexible Hotspot Detection Based on Fully Convolutional Network With Transfer Learning
Abstract
Layout hotspot detection is one of the most important issues for the reliability enhancement of integrated circuits. Machine learning-based hotspot detectors have shown their advantages of efficiency and generalization compared with computationally intensive lithography process simulation. However, most machine learning-based hotspot detectors only accept layout clips of fixed size as input with the potential defect whose location is restricted at the center of each clip. Therefore, they cannot be used directly for multiple hotspots detection in a large area, which occurs frequently in real design cases. In this article, we build a new end-to-end hotspot detector based on a fully convolutional network, which has the flexibility of detecting a various number of hotspots in a layout of any size at one time. Moreover, we also develop a transfer learning scheme matching our proposed detector network, which can reduce the requirement of sample number when setting up a new model for a more advanced technology node. The experimental results demonstrate our proposed hotspot detector outstanding among state-of-the-art works and the transfer learning scheme is effective.
Year
DOI
Venue
2022
10.1109/TCAD.2021.3135786
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Keywords
DocType
Volume
Deep learning,hotspot detection,lithography,transfer learning
Journal
41
Issue
ISSN
Citations 
11
0278-0070
0
PageRank 
References 
Authors
0.34
12
10
Name
Order
Citations
PageRank
Tianyang Gai100.34
Tong Qu200.68
Shuhan Wang300.34
Xiaojing Su400.34
Renren Xu500.34
Yun Wang624.53
Jing Xue700.34
Yajuan Su841.44
Yayi Wei941.44
Tian-Chun Ye1032.77