Explainable DRC Hotspot Prediction with Random Forest and SHAP Tree Explainer
Wei Zeng1,a, Azadeh Davoodi1,b and Rasit Onur Topaloglu2,c
1Department of Electrical and Computer Engineering University of Wisconsin–Madison, Madison, WI, USA
awei.zeng@wisc.edu
badavoodi@wisc.edu
2IBM Hopewell Junction, NY, USA
crasit@us.ibm.com
ABSTRACT
With advanced technology nodes, resolving design rule check (DRC) violations has become a cumbersome task, which makes it desirable to make predictions at earlier stages of the design flow. In this paper, we show that the Random Forest (RF) model is quite effective for the DRC hotspot prediction at the global routing stage, and in fact significantly outperforms recent prior works, with only a fraction of the runtime to develop the model. We also propose, for the first time, to adopt a recent explanatory metric—the SHAP value—to make accurate and consistent explanations for individual DRC hotspot predictions from RF. Experiments show that RF is 21%–60% better in predictive performance on average, compared with promising machine learning models used in similar works (e.g. SVM and neural networks) while exhibiting good explainability, which makes it ideal for DRC hotspot prediction.
Keywords: Design Rule Check, Machine Learning, Random Forest, Explainability, Global Routing.
