Deep learning methods for solving non-uniqueness of inverse design in photonics

Abstract

Deep learning has been an efficient technique for inverse design in integrated photonics. However, there is a typical non-uniqueness or one-to-many problem that impacts the convergence of network models. In this paper, we construct a multilayer structure and implement the inverse design through three main deep learning algorithms: tandem neural networks (TNNs), mixture density networks (MDNs), and conditional generative adversarial networks (CGANs). In our design, although the MDN and the CGAN is more complicated, they are more accurate and could generate multiple solutions. In particular, the MDN shows the best performance, and we proposed a new way to locate all the possible positions related to multiple solutions. As a result, for one desired input response in our test, the MDN could generate more than one design option with the mean square error (MSE) less than 0.005.