  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.
