Policy-Guided Monte-Carlo Tree Search for CAD-Grounded PCB Routing

Learned printed circuit board (PCB) routing policies can produce suboptimal solutions on unseen boards. We propose PW-MCTS, a test-time search method that refines a pretrained routing policy through Monte Carlo tree search over native CAD operations. Board-wise iterated Bellman calibration aligns critic outputs with target-board returns. Engine determinization enables reproducible branch evaluation; incremental checkpointing, memoized branch-packed inference, and invalid-action filtering reduce search overhead. We evaluate PW-MCTS on a subset of PCBWorld-Bench, an open-source PCB dataset, using a policy trained only on synthetic boards. At equal draws, PW-MCTS raises the fraction of attempts that complete a board without error-severity design-rule violations from 42.2% to 63.2%, and mean routability from $0.742$ to $0.885$. Under matched rollout-time budgets after calibration, search raises board coverage from 84.7% to 89.3% with eight simulations per decision and roughly one-quarter as many trajectories as policy sampling. These results support test-time refinement by balancing search effort and repeated sampling, with attainable performance still shaped by the routing MDP and prior quality.