Methodology of Resolving Design Rule Checking Violations Coupled with Fully Compatible Prediction Model

Resolving the design rule checking (DRC) violations at the pre-route stage is critically important to reduce the time-consuming design closure process at the post-route stage. Recently, noticeable methodologies have been proposed to predict DRC hotspots using Machine Learning based prediction models. However, little attention has been paid to how the predicted DRC violations can be effectively resolved. In this paper, we propose a pre-route DRC violation resolution methodology that is tightly coupled with fully compatible prediction model. Precisely, we devise different resolution strategies for two types of DRC violations: (1) pin accessibility (PA)-related and (2) routing congestion (RC)-related. To this end, we develop a fully predictable ML-based model for both PA and RC-related DRC violations, and propose completely different resolution techniques to be applied depending on the DRC violation type informed by the compatible prediction model such that for (1) PA-related DRC violation, we extract the DRC violation mitigating regions, then improve placement by formulating the whitespace redistribution problem on the regions into an instance of Bayesian Optimization problem to produce an optimal cell perturbation, while for (2) RC-related DRC violation, we manipulate the routing resources within the regions that have high potential for the occurrence of RC-related DRC violation. Through experiments, it is shown that our methodology is able to resolve the number of DRC violations by 26.54%, 25.28%, and 20.34% further on average over that by a conventional flow with no resolution, a commercial ECO router, and a state-of-the-art academic predictor/resolver, respectively, while maintaining comparable design quality.