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

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.