Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning

arXiv:2402.14236v1 Announce Type: new
Abstract: 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 …

abstract arxiv automated cs.ai cs.ar cs.lg design designing distributed efficiency electronics engineers experience expertise filtering low novel optimization performance reinforcement reinforcement learning study type via