Nonlinear Photonic Signal Processing

Abstract

While optics was traditionally considered an excellent high-bandwidth conduit of information, the increased demand for bandwidth in modern applications makes it a great candidate for not just signal transmission but also signal processing.In addition, proliferation of silicon photonics in data centers accelerated the progress in photonic technology and research in the last decade. The combination of these makes optics a viable candidate for signal processing applications today. This thesis mainly explores two optical signal processing applications that utilize the nonlinear properties of silicon photonic devices. The first chapter focuses on developing a thresholder-like transfer function using silicon photonic devices for improving signal readout in a cryogenic radio frequency receiver. It covers the challenges associated with current state-of-the-art receivers and reports experimental implementations of five devices based on silicon and silicon nitride platforms that can boost the signal contrast of return-to-zero pulses. The following chapters focus on developing photonic devices that mimic functional units of photonic neural networks (PNNs). We cover demonstration of all-optical programmable nonlinear activation functions for artificial photonic neurons based on integrated silicon and silicon nitride platforms. We then present design and simulations of a novel photonic spiking neuron, based on a hybrid graphene-on-silicon photonic cavity, which offers a more scalable way of implementing spiking neurons in photonics. Finally, we explore two other devices for PNNS, such as a novel synapse based on a nanophotonic cavity and a bipolar junction transistor for signal amplification in the electronic link of optoelectronic neurons.