A time-delay reservoir computing neural network based on a single microring resonator with external optical feedback
Donati, Giovanni (Supervisors: A. Argyris, C.R. Mirasso, L. Pavesi)
PhD Thesis (2023)
Artificial intelligence is a new paradigm of information processing where machines emulate human intelligence and perform tasks that cannot be done with standard computers. Neuromorphic computing is in particular inspired by how the brain computes. Large network of interconnected neurons whose synapses are varied during a learning phase, and where the information flows in parallel throughout different connections. Photonics platforms represent an interesting possibility where to implement neuromorphic processing schemes, exploiting light and its advantages in terms of speed, low energy consumption and inherent parallelism via wavelength division multiplexing. In particular, a candidate playing a diversity of key roles in integrated networks is the microring resonator. In silicon photonics, the microring resonator can implement the strength of a synapse, the spiking emission of a biological neuron, and it can exhibit a fading memory based on its multiple linear and nonlinear dynamical timescales. This thesis presents an overview of the main applications of silicon microring resonators in neuromorphic silicon photonics, and then focuses on its implementation in a processing scheme,named time delay reservoir computing (RC). Time delay RC is a hardware-friendly approach by which implement a large neural network, where this is folded in the nonlinear dynamical response of only one physical node, such as a dynamical system with delay feedback. The manuscript illustrates, both numerically and experimentally, how to make time delay RC exploiting the linear and nonlinear dynamical response of a silicon microring resonator. The microring is coupled to an external optical feedback and the results on a diversity of time series prediction tasks and delayed-boolean tasks are presented. Numerically, it is shown that the microring nonlinearities can be exploited to improve the performance on prediction tasks, such as the Santa Fe and Mackey Glass ones. Experimentally, it is shown how the network can be set to solve delayed boolean tasks with error-free operation, at 12 MHz operational speed, together with possible upgrades and alternative implementations that can boost its performances.