PMONN

We propose an optical neural network algorithm, named Phase of Micro-resonator Optical Neural Network (PMONN), to address the gap in algorithms for Photonic Integrated Circuits (PICs) based on micro-resonator (MRR). PMONN's core architecture features a Convolutions and Batch Normalization (CB) unit, comprising a phase-based (PB) convolutional layer, a Depth-Point-Wise (DPW) convolutional layer, and a reconstructed Batch Normalization (RBN) layer. The PB convolution kernel uses modulable phase shifts of Add-drop MRRs as learnable parameters and their optical transfer function as convolution weights. The DPW convolution kernel amplifies PB convolution weights by learning the amplification factors. To address the internal covariate shift during training, the RBN layer normalizes DPW outputs by reconstructing the BN layer of the electronic neural network, which is then merged with the DPW layer in the test stage. We employ DAs in PICs to implement the merged layer. PMONN achieved 99.07% and 91.68% accuracy on MNIST and Fashion-MNIST datasets, respectively. This work presents a method for implementing optical neural network algorithm on PICs based on MRRs and Wavelength Division Multiplex. PMONN has potential applications as the backbone for future optical object detection neural networks. The code is available at https://github.com/ISCLab-Bistu/PMONN.