An Efficient Approach for DRC Hotspot Prediction with Convolutional Neural Network

被引：7
|
作者：
Li, Lin [1 ]
Cai, Yici [1 ]
Zhou, Qiang [1 ]
机构：
[1] Tsinghua Univ, Dept Comp Sci & Technol, Beijing, Peoples R China
来源：
2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) | 2021年
基金：
国家重点研发计划;
关键词：
VLSI; DRC hotspot prediction; deep learning; MobileNetV3; connectivity;
D O I：
10.1109/ISCAS51556.2021.9401274
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Predicting the design rule check (DRC) violation hotspots in an early stage plays an essential role in the efficiency of the physical design. Multiple factors that affect the performance of a DRC hotspot predictor, among them, the efficacy of the extracted features plays a substantial role. In this paper, we propose a connectivity-based DRC hotspot prediction method using a convolutional neural network. We show that the proposed method is efficient in both training and prediction. The relation between pin features and predictor performance is further investigated and two weighted connectivity-based route map features are introduced. Experimental results demonstrate that the proposed algorithm can predict on average 73% of the DRC hotspots with only 2.7% false alarms.
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页数：5
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