Standard Cell Routing with Reinforcement Learning and Genetic Algorithm in Advanced Technology Nodes

Abstract

Standard cell layout in advanced technology nodes are done manually in the industry today. Automating standard cell layout process, in particular the routing step, are challenging because of the constraints of enormous design rules. In this paper we propose a machine learning based approach that applies genetic algorithm to create initial routing candidates and uses reinforcement learning (RL) to fix the design rule violations incrementally. A design rule checker feedbacks the violations to the RL agent and the agent learns how to fix them based on the data. This approach is also applicable to future technology nodes with unseen design rules. We demonstrate the effectiveness of this approach on a number of standard cells. We have shown that it can route a cell which is deemed unroutable manually, reducing the cell size by 11%.