Pin Accessibility Prediction and Optimization With Deep-Learning-Based Pin Pattern Recognition

Abstract

With the continuous scaling down of process nodes, standard cells become much smaller and cell counts are dramatically increased. Pin accessibility becomes one of the major issues causing design rule violations (DRVs). To tackle this problem, many recent works apply machine-learning-based techniques to predict whether a local region has DRV or not by regarding global routing (GR) congestion and local pin density as the main features during the training process. Empirically, however, DRV occurrence is not necessary to be strongly correlated with the two features in advanced nodes. In this article, we propose the first work of deep-learning-based DRV prediction using pin pattern as our major feature to directly identify whether a DRV will exist or not due to bad pin accessibility of the given pin pattern. Unlike most of the existing models that can only be used for DRV prediction, the proposed models can be applied to guide detailed placement for pin accessibility optimization during physical design. Experimental results show that the proposed models are greatly superior than those of previous studies in terms of all quantitative metrics. Additionally, the numbers of DRVs can be dramatically reduced by applying the proposed model-guided detailed placement flow.