CTRL-B: Back-End-Of-Line Configuration Pathfinding Using Cross-Technology Transferable Reinforcement Learning

In advanced technology nodes, the impact of the back-end-of-line (BEOL) on chip performance and power consumption becomes progressively significant. This paper presents a BEOL configuration pathfinding framework using the proposed cross-technology transferable reinforcement learning (CTRL) model. We optimize the BEOL technology parameters, including the metal stack, geometry, and design rules, to enhance the power efficiency and performance resulting from the physical design process. First, we extract various design and technology features and embed them into metal-type-wise deep neural networks. Our multi-policy model selects a BEOL parameter configuration that is estimated to improve chip power efficiency and performance. We employ a policy gradient algorithm complemented by various training strategies, such as data normalization, action-reward buffer, and network optimization, to expedite training convergence. Furthermore, we transfer the pathfinding knowledge from the trained model in the old node to the new model for efficient BEOL configuration pathfinding in the advanced node, referred to as cross-technology transfer learning. The proposed framework achieved 19 % reduced total power consumption, 68 % improved worst negative slack, and 87 % improved total negative slack with a high reward efficiency, on average, in several designs. Further, we demonstrate that the reward efficiency of our proposed CTRL model outperforms that of the conventional fine-tuning method in transferring knowledge by 24 %.