Orbital angular momentum logic gates based on optical diffraction neural network

Abstract

Vortex beams with orthogonal orbital angular momentum (OAM) modes have potential applications in optical computing. By exploiting the learning capability of deep neural network and the complex light-field manipulation ability of multilayer diffraction layers, the spatial position of the vortex beam is manipulated by a five-layer diffraction deep neural network as the input of the logic gate. The result of the logic gate is expressed as the light intensity in the output plane. The simulation results show that the accuracy of AND gate and OR gate is 0.9912 and 0.9887 respectively, which proves that the method of implementing logic gate based on diffraction deep neural network modulated OAM is feasible.

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

“…The diffractive nature of these neural networks offers a high level of flexibility in designing and implementing various logic operations. While D 2 NN have been employed to achieve spatially encoded optical intensity logic gates [21,54] and orbital angular momentum logic gates [48,55], logic gates utilizing the polarization degree of freedom have not been previously reported. Here, we utilized two beams of mutually orthogonal linearly polarized light for encoding '0' and '1' logic states.…”

“…During the training process, the design of the passive diffractive layers, or neurons, is optimized such that the network performs a specific function. D 2 NN has been applied to image recognition [29][30][31][32][33][35][36][37][38][39][40][41][42][43][44][45][46], optical logic operations [21,47,48], terahertz pulse shaping [49], phase retrieval [50], and image reconstruction [34,[51][52][53] etc.…”