Ultra‐Compact and NonVolatile Nanophotonic Neural Networks

Abstract A nanophotonic neural network (N‐PNN) architecture is proposed with compact nanophotonic scattering units and a hybrid structure of silicon and nonvolatile arrayed Sb₂Se₃. This PNN can execute deep neural networks (DNN) classification and identification tasks with a broad operation bandwidth and very compact footprint. The reconstruction of the convolutional kernel core is realized by digitally switching the phase state of the Sb₂Se₃ array. Based on a three‐dimensional finite‐difference time‐domain analysis, the core unit received only 4.92 × 2.34 µm² footprint. The convolution kernel unit weights are reconfigured with high‐accuracy (7‐bit) image processing and recognition in the wavelength C‐band (1530–1570 nm). Furthermore, various deep‐learning tasks (speech, digital patterns, and clothing patterns) are investigated. The accuracy of the classification and recognition efficiency reached almost the same level as that of a 64‐bit computer. The size of the N‐PNN is almost two orders of magnitude smaller than that of classic Mach–Zehnder interferometer meshes. It is conducive for scalability, high‐radix DNN, and optoelectronic fusion of photonic integrated circuits and electronic integrated circuits.