Designing distributed filtering circuits (DFCs) is complex and
time-consuming, with the circuit performance relying heavily on the expertise
and experience of electronics engineers. However, manual design methods tend to
have exceedingly low-efficiency. This study proposes a novel end-to-end
automated method for fabricating circuits to improve the design of DFCs. The
proposed method harnesses reinforcement learning (RL) algorithms, eliminating
the dependence on the design experience of engineers. Thus, it significantly
reduces the subjectivity and constraints associated with circuit design. The
experimental findings demonstrate clear improvements in both design efficiency
and quality when comparing the proposed method with traditional engineer-driven
methods. In particular, the proposed method achieves superior performance when
designing complex or rapidly evolving DFCs. Furthermore, compared to existing
circuit automation design techniques, the proposed method demonstrates superior
design efficiency, highlighting the substantial potential of RL in circuit
design automation.

提出的自动化方法利用强化学习算法来改善分布式过滤电路（DFCs）的设计，消除了工程师设计经验的依赖，从而显著降低了电路设计的主观性和限制性。实验结果表明，与传统的工程师驱动方法相比，该方法在设计效率和质量上都有明显的改进，特别是在设计复杂或快速演化的 DFCs 时表现出卓越性能。此外，与现有的电路自动化设计技术相比，该方法展现出更高的设计效率，凸显了强化学习在电路设计自动化中的巨大潜力。