CTRL-B: Back-End-Of-Line Configuration Optimization using Cross-Domain Transferable Reinforcement Learning

In advanced technology nodes, the back-end-of-line (BEOL) process is increasingly critical for determining chip timing and power consumption. This paper presents CTRL-B, a transferable reinforcement (RL) learning framework that automates and accelerates this process. CTRL-B integrates two key innovations: (1) a graph attention network (GAT)-based encoder, pretrained on out-of-domain datasets to learn generalizable circuit representations, and (2) a cross-domain transfer layer that reuses knowledge from previously optimized source-domain. This architecture provides the RL agent with an effective initial policy, which significantly reduces redundant exploration and accelerates convergence to high-quality solutions. We demonstrate the effectiveness of CTRL-B against conventional methods, achieving a 0.4% power, 57.6% reduction in worst negative slack and a 41.7% reduction in total negative slack, alongside 1.58% area reductions. The practical viability of the optimized BEOL configurations is confirmed through stable static IR drop and successful design rule violation checks. A series of ablation and sensitivity analyses further validates the robustness of our architecture across various designs. These results establish CTRL-B as a robust and scalable solution for automated BEOL optimization, highlighting its potential to shorten modern chip development cycles.