Diffraction Neural Network for Multi-Source Information of Arrival Sensing

Abstract

All-optical diffractive neural networks (DNNs) based on passive structures have been shown to perform complicated functions by optical acceleration, thus reforming in situ applications requiring digital computing processing. Its stable, compact, and passive features make it suitable for many conventional applications in complex electromagnetic environments. Here, a DNN based on a multilayer passive metasurface array is presented to estimate the information of arrival (IOA) over a wide range of frequency bands and incident angles. Unlike traditional methods, multiple correlated or coherent electromagnetic signals interfere with each other, resulting in difficult separation which is a fundamental obstacle in multi-source IOA detection. Here, the proposed diffraction system creates separate virtual channels to isolate and process different incoming waves. It recognizes and classifies the beams, focuses them on the output plane divided by the arrival angle and working frequency, and displays the IOA and number of sources in real time at the speed of light. Furthermore, the massive-parallelism and data-post-processing-free strategy allows for broader applications in harsh environments, such as seismic detection and underwater circumstances.