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

Binwu Zhu, Xinyun Zhang, Yibo Lin, Bei Yu, Martin Wong

Research output: Contribution to journalJournal articlepeer-review

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.

Original languageEnglish
Article number80
Pages (from-to)1–18
Number of pages18
JournalACM Transactions on Design Automation of Electronic Systems
Volume28
Issue number5
DOIs
Publication statusPublished - Sept 2023

Scopus Subject Areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Design Rule Checking
  • key information extraction
  • natural language processing

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