DRC-SG 2.0: Efficient Design Rule Checking Script Generation via Key Information Extraction

Abstract

Design Rule Checking (DRC) is a critical step in integrated circuit design. DRC requires formatted scripts as the input to design rule checkers. However, these scripts are manually generated in the foundry, which is tedious and error prone for generation of thousands of rules in advanced technology nodes. To mitigate this issue, we propose the first DRC script generation framework, leveraging a deep learning-based key information extractor to automatically identify essential arguments from rules and a script translator to organize the extracted arguments into executable DRC scripts. We further enhance the performance of the extractor with three specific design rule generation techniques and a multi-task learning-based rule classification module. Experimental results demonstrate that the framework can generate a single rule script in 5.46 ms on average, with the extractor achieving 91.1% precision and 91.8% recall on the key information extraction. Compared with the manual generation, our framework can significantly reduce the turnaround time and speed up process design closure.