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Radio-Frequency Linear Analysis and Optimization of Silicon Photonic Neural Networks

Broadband analog signal processors utilizing silicon photonics have demonstrated a significant impact in numerous application spaces, offering unprecedented bandwidths, dynamic range, and tunability. In the past decade, microwave photonic techniques have been applied to neuromorphic processing, resulting in the development of novel photonic neural network architectures. Neuromorphic photonic systems can enable machine learning capabilities at extreme bandwidths and speeds. Herein, low-quality factor microring resonators are implemented to demonstrate broadband optical weighting. In addition, silicon photonic neural network architectures are critically evaluated, simulated, and optimized from a radio-frequency performance perspective. This analysis highlights the linear front-end of the photonic neural network, the effects of linear and nonlinear loss within silicon waveguides, and the impact of electrical preamplification.

Link Loss Analysis of Integrated Linear Weight Bank within Silicon Photonic Neural Network

Over the last decade, silicon photonic neural networks have demonstrated the possibility of photonic-enabled machine learning at the edge. These systems enable low-latency ultra-wideband classifications, channel estimations, and many other signal characterization tasks within wireless environments. While these proof-of-concept experiments have yielded promising results, poor device and architectural designs have resulted in sub-optimal bandwidth and noise performance. As a result, the application space of this technology has been limited to GHz bandwidths and high signal-to-ratio input signals. By applying a microwave photonic perspective to these systems, the authors demonstrate high-bandwidth operation while optimizing for RF performance metrics: instantaneous bandwidth, link loss, noise figure, and dynamic range. The authors explore the extended capabilities due to these improved metrics and potential architectures to continue further optimization. The authors introduce novel architectures and RF analysis for RF-optimized neuromorphic photonic hardware.