Flex multimode neural network for complete optical computation

Abstract

Compact and efficient photonic integrated circuits (PICs) are promising route to solving modern computing challenges. Traditional PICs using cascaded Mach-Zehnder Interferometers (MZIs) or micro-ring resonators (MRRs) are limited to rigid linear matrix operations, requiring electronics for data compression, nonlinear activation, and post-processing. The dependence on electronic processing counteracts the advantages brought by photonics. Here we propose a photonic chip that tackles this problem. The idea is to apply two sets of electrodes on a multimode waveguide: one set for data loading and the other for shaping the neural network by manipulating the multimode light interference flexibly. The shaping process, following a genetic algorithm, resorts again to optical computation to bypass the gradient acquisition problem. Once trained, the chip handles computation completely in the optical domain. Experimentally 91% classification accuracy is achieved on the Iris dataset. Our approach may bring PICs closer to practical computation applications without electronics overload.