DRC Violation Prediction with Pre-global-routing Features Through Convolutional Neural Network

Jhen Gang Lin, Yu Guang Chen, Yun Wei Yang, Wei Tse Hung, Cheng Hong Tsai, De Shiun Fu, Mango Chia Tso Chao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Design Rule Checking (DRC) is one of the most important metrices in physical design procedure to evaluate quality of a detail route. The prediction of DRC violation (DRV) in the early stage can reduce the iterations of design procedure and improve the efficiency of the physical design closure. Several researchers have applied machine-learning techniques to predict the DRVs of a detail route at different design stages with various input features. In this paper, we proposed a machine learning model to predict DRVs with the information obtained after placement stage. Specifically, we build a ResNet-like CNN model to predict whether a DRV may occur in a targeted grid after detail route. Our features consist of not only quantified placement information but also layout-image features to take pin accessibility into account for better prediction result. Moreover, we apply an under-sampling technique to select critical training samples to improve the training efficiency. A series of experiments have been conducted and the results show that compared with previous works, our prediction result can outperform Fully Convolutional Network (FCN) based approaches.

Original languageEnglish
Title of host publicationGLSVLSI 2023 - Proceedings of the Great Lakes Symposium on VLSI 2023
PublisherAssociation for Computing Machinery
Number of pages7
ISBN (Electronic)9798400701252
StatePublished - 5 Jun 2023
Event33rd Great Lakes Symposium on VLSI, GLSVLSI 2023 - Knoxville, United States
Duration: 5 Jun 20237 Jun 2023

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI


Conference33rd Great Lakes Symposium on VLSI, GLSVLSI 2023
Country/TerritoryUnited States


  • cnn
  • drv prediction
  • pre-global-routing features
  • under-sampling


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