  Designing distributed filter circuits (DFCs) is complex and time-consuming,
involving setting and optimizing multiple hyperparameters. Traditional
optimization methods, such as using the commercial finite element solver HFSS
(High-Frequency Structure Simulator) to enumerate all parameter combinations
with fixed steps and then simulate each combination, are not only
time-consuming and labor-intensive but also rely heavily on the expertise and
experience of electronics engineers, making it difficult to adapt to rapidly
changing design requirements. Additionally, these commercial tools struggle
with precise adjustments when parameters are sensitive to numerical changes,
resulting in limited optimization effectiveness. This study proposes a novel
end-to-end automated method for DFC design. 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 design efficiency and quality when comparing
the proposed method with traditional engineer-driven methods. Furthermore, the
proposed method achieves superior performance when designing complex or rapidly
evolving DFCs, highlighting the substantial potential of RL in circuit design
automation. In particular, compared to the existing DFC automation design
method CircuitGNN, our method achieves an average performance improvement of
8.72%. Additionally, the execution efficiency of our method is 2000 times
higher than CircuitGNN on the CPU and 241 times higher on the GPU.
