An optimized optical diffractive deep neural network with OReLU function based on genetic algorithm

Abstract

The diffractive deep neural network (D2NN) has demonstrated advantages in artificial intelligence image processing tasks, such as classification and lens-free imaging. Due to the lack of nonlinear layers, the nonlinear representation and generalization capability of D2NN needs to be improved. To solve this problem, we introduce an Optical Rectified Linear Units (OReLU) function utilizing beam splitting, sensing, and modulation of optoelectronic devices. We experimentally verified the functionality of the proposed OReLU and analyzed the impact of the actual error on the whole network. The simulation result shows that such a nonlinear layer increases the sparsity of the network, which reduces the interdependence between parameters and also improves the classification accuracy of the network. Since the model's convergence speed and classification accuracy are influenced by the truncation threshold associated with the nonlinear function, we also optimize the threshold factor to achieve the highest classification accuracy of 97.97% and 87.85% on the MNIST and Fashion-MNIST datasets using the genetic algorithm.