EvoPCB: Evolving PCB Physical Design to Expert Alignment via Policy Optimization

Despite substantial progress in automated printed circuit board (PCB) physical design, mainly including placement and routing (PnR), industrial physical design still relies heavily on human expert engineers. Existing frameworks can generate design-rule-compliant designs, but conventional objectives alone do not ensure satisfactory performance or compliance with industrial standards. Moreover, existing frameworks often cannot adapt to user specifications or domain-specific constraints, which also limits their applicability. To handle these problems, we propose EvoPCB, an end-to-end, LLM-based PCB physical design framework. EvoPCB introduces RePCB, a performance-aware supervised representation model that embeds PnR topology and measures similarity to expert layouts. A two-stage specialization, supervised fine-tuning (SFT) followed by customized decoupled clip and dynamic sampling policy optimization (DAPO), assists EvoPCB in generating high-quality initial designs. EvoPCB then refines the layouts through a multi-agent and multi-objective script evolution method, MM-Evolve, which coordinates geometric and multi-physics optimization. Experimental results demonstrate that EvoPCB achieves the best overall performance compared to existing state-of-the-art methods across all evaluated metrics.