Second-harmonic generation for enhancing the performance of diffractive neural networks

TL;DR

This paper explores the integration of second-harmonic generation (SHG) nonlinearities into diffractive neural networks to enhance their classification performance and discusses the optimal placement and practical constraints for experimental implementation.

Contribution

It introduces the concept of incorporating SHG nonlinearities into DNNs, analyzes the impact of SHG layer positioning, and estimates the efficiency for potential experimental realization.

Findings

SHG layer placement significantly affects classification accuracy.

Optimal arrangement of SHG enhances class contrast.

Practical constraints for experimental implementation are outlined.

Abstract

Diffractive neural networks (DNNs) are an emerging approach for the realization of photonic artificial intelligence, especially due to their suitability for machine-vision applications and high-dimensional photonic information processing at lower power consumption. However, incorporating optical nonlinear activation functions to make DNNs a feasible alternative to their electronic counterpart remains a challenge. Here, we investigate the inclusion of second-harmonic generation (SHG), as one of the simplest and most efficient types of optical nonlinearities, in DNNs. We numerically investigate the impact of SHG on the performance of classification tasks in an all-optical nonlinear DNNs. Specifically, we investigate and discuss the essential requirements for an effective arrangement of the SHG layer in single and multilayer DNNs. We find that the performance, in terms of classification accuracy and class contrast, is affected strongly by the positioning of the SHG layer. Finally, we discuss and outline the constraints for including SHG in an experimental realization. Taking these constraints into account, we estimate the power-related efficiency of the nonlinear DNN system. Overall, our results provide a path towards implementing nonlinear DNNs using the SHG process.