Appl Opt. 2021 Mar 1;60(7):2100-2108. doi: 10.1364/AO.417935.
Intelligent photonic circuits (IPCs) tuned with an appropriate phase-shift vector could enable a photonic intelligent matrix possibly implemented in multiple neural layers for a task-oriented topologies. A photonic Mach-Zehnder Interferometer (MZI) is a fundamental photonic component in IPCs, whose matrix representation could be broadcasted into an arbitrary matrix that is equipped with an optimized phase-shift vector. The initialized MZIs' phases are tentatively probed between analytical elements and a digital weight matrix that is learned from samples with efficient compatible learning for complex-valued neural networks. Nonlinear least squares is utilized to formulate a phase determination system to refine the optimal phase-shift solutions. The robustness of phase determination system for photonic neural networks is discussed in detail. For a preliminary implementation, a basic 4×4 intelligent photonic neural network is utilized to verify the proof of concept on phase-shift determination in IPC through numerical experiments.
智能光子电路（IPCs）通过适当的相移矢量进行调谐，可以实现可能在多个神经层中实现的面向任务的拓扑结构的光子智能矩阵。光子马赫-曾德尔干涉仪（MZI）是 IPC 中的基本光子元件，其矩阵表示可以广播到具有优化相移矢量的任意矩阵中。初始化的 MZI 相位在分析元件和数字权重矩阵之间进行初步探测，该权重矩阵是从具有高效兼容学习的样本中学习得到的，适用于复值神经网络。非线性最小二乘法用于构建相位确定系统，以优化相移解。详细讨论了光子神经网络的相位确定系统的鲁棒性。为了初步实现，利用基本的 4×4 智能光子神经网络通过数值实验验证了在 IPC 中进行相移确定的概念验证。
