TRouter: Thermal-Driven PCB Routing via Nonlocal Crisscross Attention Networks
Tinghuan Chen, Silu Xiong, Huan He, Bei Yu
Abstract
In this article, we propose TRouter, a thermal-driven printed circuit board (PCB) routing framework via a machine-learning model. The model is designed to capture the long-range spatial information from the PCB layout and predict thermal distribution. The information contains pads, vias, components and wire segments. A gradient in each grid cell obtained from the backpropagation is integrated into a full-board routing algorithm to guide thermal-aware wire detour and via punching. To achieve a significant speedup, we construct a conflict graph according to whether overlapping among convex hulls of nets. A greedy-based method is adopted to remove nonroot nodes from all nodes. Then, a task graph is constructed to improve the parallelism. We conduct experiments on open-source benchmarks to illustrate our TRouter can achieve significant speedup and lower-temperature designs, compared with a state-of-the-art PCB routing algorithm.