RouteDiff: A Constraint-Aware Diffusion for PCB Routing with Geometry-Preserving Route Realization

Recent learning-based routing methods have focused primarily on VLSI and IC routing, while learned PCB routing remains comparatively underexplored. Printed circuit board (PCB) routing similarly admits multiple valid solutions for a given placement and netlist, with routing geometries that include non-orthogonal segments and are subject to per-net rules such as track width and clearance. We consider PCB routing as sequential conditional generation, where each target-net route is conditioned on the current routing state, target-pad geometry, explicit per-net design rules, and information from the remaining netlist. To model these conditions, we propose RouteDiff, a constraint-aware latent diffusion transformer that integrates spatial routing conditions, scalar design rules, and target-pad geometry through heterogeneous conditioning pathways. We further introduce Geometry-Preserving Route Realization (GPRR), a realization protocol that converts generated routing images into physical PCB traces while limiting modification of the generated geometry. On a public PCB benchmark and an industrial PCB dataset, RouteDiff achieves the highest F1 to the reference routes and, after GPRR realization, the highest routability and the fewest design-rule violations among the learned baselines, while responding distinctly to width and clearance interventions. Our evaluations support constraint-aware generation and geometry-preserving realization as complementary components for learned PCB routing.