Reinforcement Learning-based Auto-router considering Signal Integrity

Abstract

In this paper, we propose artificial intelligent (AI)router, a reinforcement learning (RL)-based auto-router considering signal integrity (SI), for the first time. Our algorithm has two main stages. At first, we design the transformer-based novel neural architecture considering the keep-out region, crosstalk region, and the number of vias for SI optimization. Then, the designed neural network is optimized by the policy gradient, one of the RL algorithms. Compared with the conventional maze routers, the A* algorithm, and the lee algorithm, it is verified that our AI-router outperforms the algorithms in terms of wire-length and crosstalk in a specific test case. Furthermore, it is shown that AI-router successfully performs multi-layer routing which is not feasible with conventional maze routers.