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.

翻译：设计分布式滤波电路（DFC）复杂且耗时，其性能高度依赖电子工程师的专业知识与经验。然而，人工设计方法效率极其低下。本研究提出一种新颖的端到端自动化电路制造方法，以改进DFC的设计。该方法利用强化学习（RL）算法，消除了对工程师设计经验的依赖，从而显著降低了电路设计中的主观性与约束条件。实验结果表明，与传统工程师驱动的方法相比，所提方法在设计效率与质量上均有显著提升。特别是在设计复杂或快速演进的DFC时，该方法展现出卓越性能。此外，与现有电路自动化设计技术相比，本方法具备更优的设计效率，凸显了强化学习在电路设计自动化领域的巨大潜力。