期刊
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
卷 41, 期 3, 页码 669-680出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCAD.2020.3021663
关键词
Feature extraction; Layout; Detectors; Kernel; Pattern matching; Neural networks; Convolution; Design for manufacturability; hotspot detection; machine learning
类别
资金
- Research Grants Council of Hong Kong SAR [CUHK24209017, CUHK14209420]
- National Key Research and Development Program of China [2016YFB0201304]
- National Natural Science Foundation of China Research Projects [61574046, 61774045]
- NVIDIA
This article proposes a new end-to-end framework that can efficiently detect multiple hotspots in a large region and improves performance.
As the circuit feature size continuously shrinks down, hotspot detection has become a more challenging problem in modern design for manufacturability flows. Developed deep learning techniques have recently shown their superiorities on hotspot detection tasks. However, existing hotspot detectors can only handle defect detection from one small layout clip each time, thus, may be very time-consuming when dealing with a large full-chip layout. In this article, we develop a new end-to-end framework that can detect multiple hotspots in a large region at a time and promise a better hotspot detection performance. We design a joint auto-encoder and inception module for efficient feature extraction. A two-stage classification and regression framework is designed to detect hotspot with progressive accurate localization, which provides a promising performance improvement. Experimental results show that our framework enables a significant speed improvement over existing methods with higher accuracy and fewer false alarms.
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