Design of Wideband Microstrip-to-Microstrip Vertical Transition With Pixel Structures Based on Reinforcement Learning

Abstract

This article proposes a microstrip-to-microstrip (MS-to-MS) vertical transition with pixel structures and then proposes a knowledge-assisted proximal policy optimization (PPO), which is a reinforcement learning (RL) for the design of this transition. The transition requires fully connected structures and a novel mechanism to generate the pixel structures with fully connected shape is proposed and incorporated into PPO. The proposed method is compared with the particle swarm optimization (PSO) and the genetic algorithm (GA) and demonstrates benefits in improving design efficiency. The designed MS-to-MS transition is fabricated using the PCB process. Measurement results indicate that the designed MS-to-MS vertical transition operates in the band from 3.4 to 14.8 GHz with low insertion loss.