Polarization-based all-optical logic gates using diffractive neural networks

Abstract

Optical logic operations are an essential part of optical computing. The inherent stability and low susceptibility of polarization to the external environment make it a suitable choice for acting as the logical state in computational tasks. Traditional polarization-based optical logic devices often rely on complex cascading structures to implement multiple logic gates. In this work, by leveraging the framework of deep diffractive neural networks (D2NN), we proposed a uniform approach to designing polarization-encoded all-optical logic devices with simpler and more flexible structures. We have implemented AND, OR, NOT, NAND, and NOR gates, as well as High-order Selector and Low-order Selector. These polarization-based all-optical logic devices using D2NN offer passive nature, stability, and high extinction ratio features, paving the way for a broader exploration of optical logic computing in the future.