Microwave Integrated Circuits Design with Relational Induction Neural Network

TL;DR

This paper introduces a novel Relational Induction Neural Network that automates microwave integrated circuit design, significantly reducing convergence time and learning relationships without prior knowledge, bridging AI and electronics.

Contribution

The study presents the first neural network capable of automatically learning MWIC structural relationships, achieving rapid convergence and broadening AI applications in electronics.

Findings

Achieved rapid convergence in MWIC design tasks

Automatically learned MWIC structural relationships

Outperformed traditional reinforcement learning methods

Abstract

The automation design of microwave integrated circuits (MWIC) has long been viewed as a fundamental challenge for artificial intelligence owing to its larger solution space and structural complexity than Go. Here, we developed a novel artificial agent, termed Relational Induction Neural Network, that can lead to an automotive design of MWIC and avoid brute-force computing to examine every possible solution, which is a significant breakthrough in the field of electronics. Through the experiments on microwave transmission line circuit, filter circuit and antenna circuit design tasks, strongly competitive results are obtained respectively. Compared with the traditional reinforcement learning method, the learning curve shows that the proposed architecture is able to quickly converge to the pre-designed MWIC model and the convergence rate is up to four orders of magnitude. This is the first study which has been shown that an agent through training or learning to automatically induct the relationship between MWIC's structures without incorporating any of the additional prior knowledge. Notably, the relationship can be explained in terms of the MWIC theory and electromagnetic field distribution. Our work bridges the divide between artificial intelligence and MWIC and can extend to mechanical wave, mechanics and other related fields.