Efficient Design Optimization for Diffractive Deep Neural Networks

Abstract

Since diffractive deep neural network (D2NN) provides a full optical solution to implement deep neural networks (DNNs), it offers ultrafast operation speed and virtually unlimited bandwidth, yielding an alternative-yet-competitive approach for computer-based neural networks. A D2NN is composed of several 3D-printed phase masks as hidden layers and a number of optical detectors at the output. To enable automatic and efficient design of D2NNs, we propose an iterative optimization method to determine the optimal design parameters of D2NNs. During each iteration step, we first optimize the physical parameters for masks (e.g., thicknesses) while fixing the detector parameters (e.g., locations). Next, we exhaustively search the detector parameters with fixed masks. These two steps are repeated until convergence is reached. Our numerical experiments demonstrate that the proposed optimization algorithm can produce a high-performance D2NN achieving 97% accuracy for recognizing handwritten digits.