Performing optical logic operations on linear polarization modes using diffractive deep neural networks

Optical logic operations are pivotal for optical signal processing and digital computing, enabling rapid parallel computation. However, traditional optical logic operations often adhere to the electronic logic paradigm, representing logic variables based on intensity distribution. These methods contain limitations regarding the range of logic functions and energy consumption. Herein, we introduce linear polarization (LP) modes as optical logic variables, offering a departure from these constraints by harnessing the weakly coupled processing of the two-dimensional asymmetric spatial distribution of LP modes. The weak coupling mechanism within LP modes empowers the diffractive deep neural network (D²NN) to accomplish coupling conversion between lateral and longitudinal components. This not only surmounts the limitations in conventional LP mode processing but also significantly enhances the degree of modulation freedom with minimal energy loss. Through establishing linear mapping relationships for input and output LP mode vectors, our study demonstrates that a three-layered D²NN successfully executes fundamental logical operations (AND, OR, and NOT) alongside combined logic operations (NAND and NOR). Numerical results showcase a diffraction efficiency exceeding 92%, with mode purity surpassing 97%. In addition, by integrating AND and OR logic operations within a single D²NN, we achieve modal purity exceeding 97%. These findings confirm the potential of employing the D²NN for logical operations on weakly coupled LP modes, offering promising avenues for precise manipulation of other intricate modal fields.