학술논문
A Machine Learning Framework to Identify Detailed Routing Short Violations from a Placed Netlist
Document Type
Conference
Author
Source
2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC) Design Automation Conference (DAC), 2018 55th ACM/ESDA/IEEE. :1-6 Jun, 2018
Subject
Language
Abstract
Detecting and preventing routing violations has become a critical issue in physical design, especially in the early stages. Lack of correlation between global and detailed routing congestion estimations and the long runtime required to frequently consult a global router adds to the problem. In this paper, we propose a machine learning framework to predict detailed routing short violations from a placed netlist. Factors contributing to routing violations are determined and a supervised neural network model is implemented to detect these violations. Experimental results show that the proposed method is able to predict on average 90% of the shorts with only 7% false alarms and considerably reduced computational time.