Automated Design and Optimization of Distributed Filtering Circuits via
  Reinforcement Learning
Peng Gao Tao Yu Fei Wang Ru-Yue Yuan
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.