Broadband Diffractive Neural Networks Enabling Classification of Visible Wavelengths

Abstract

Diffractive neural networks (DNNs) are emerging as a new machine learning hardware based on optical diffraction with parallel and high-throughput information processing. The optical inputs to DNNs are spatially modulated by propagating through passive diffractive layers that work in succession to achieve an inference. Herein, visible wavelength classification using single- and two-layer DNNs fabricated using direct laser writing is demonstrated. The proposed DNN approach accepts the point spread function of two different wavelengths modeled after a microscope objective as the input and modulates the input field toward the target detector for classification. Of the three models trained to classify different wavelength pairs, the highest performance observed is for the classification of 561 and 785 nm, achieving over 90% accuracy. This work demonstrates the potential of all-optical artificial neural networks for applications requiring visible wavelengths, from visible light beam shaping to spectral analysis and optical imaging.