On-chip Reconfigurable Optical Neural Networks

Abstract Implementing artificial neural networks on integrated platforms has generated significant interest in recent years. Several architectures for on-chip optical networks with basic functionalities have been successfully demonstrated, for example, optical spiking neurosynaptic, photonic convolution accelerator, and nanophotonic/electronic hybrid deep neuron networks. In this work, we propose a layered coherent silicon-on-insulator diffractive optical neural network, of which the inter-layer phase delay can be actively tuned. By forming a close-loop with control electronics, we further demonstrate that our fabricated on-chip neural network can be trained in-situ and consequently reconfigured to perform various tasks, including full adder operation and vowel recognition, while achieving almost the same accuracy as networks trained on conventional computers. Our results show that the proposed optical neural network could potentially pave the way for future optical artificial intelligence hardware.