The layout of analog ICs requires making complex trade-offs, while addressing device physics and variability of the circuits. This makes full automation with learning-based solutions hard to achieve. However, reinforcement learning (RL) has recently reached significant results, particularly in solving the floorplanning problem. This paper presents a hybrid method that combines RL with a beam (BS) strategy. The BS algorithm enhances the agent's inference process, allowing for the generation of flexible floorplans by accomodating various objective weightings, and addressing congestion without without the need for policy retraining or fine-tuning. Moreover, the RL agent's generalization ability stays intact, along with its efficient handling of circuit features and constraints. Experimental results show approx. 5-85% improvement in area, dead space and half-perimeter wire length compared to a standard RL application, along with higher rewards for the agent. Moreover, performance and efficiency align closely with those of existing state-of-the-art techniques.

本研究解决了模拟集成电路布局自动化的复杂性和挑战，提出了一种将强化学习与束搜索算法相结合的新方法。该方法能够在不同目标权重下灵活生成布局，显著提升了面积、死区和半周长等指标，表现出与现有技术相媲美的性能和效率。

通过束搜索增强模拟集成电路布局的强化学习