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arXiv · 2302.10203

Nonlinear response of Silicon Photonics microresonators for reservoir computing neural network

Emiliano Staffoli·Davide Bazzanella·Stefano Biasi·Giovanni Donati·Mattia Mancinelli·Paolo Bettotti·Lorenzo Pavesi
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Abstract

Nowadays, Information Photonics is extensively studied and sees applications in many fields. The interest in this breakthrough technology is mainly stimulated by the possibility of achieving real-time data processing for high-bandwidth applications, still implemented through small-footprint devices that would allow for breaking the limit imposed by Moore's law. One potential breakthrough implementation of information photonics is via integrated photonic circuits. Within this approach, the most suitable computational scheme is achieved by integrated photonic neural networks. In this chapter, we provide a review of one possible way to implement a neural network by using silicon photonics. Specifically, we review the work we performed at the Nanoscience Laboratory of the University of Trento. We present methodologies, results, and future challenges about a delayed complex perceptron for fast data processing, a microring resonator exploiting nonlinear dynamics for a reservoir computing approach, and a microring resonator with the addition of a feedback delay loop for time series processing.

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Emiliano Staffoli, Davide Bazzanella, Stefano Biasi, Giovanni Donati, Mattia Mancinelli, Paolo Bettotti, Lorenzo Pavesi. 2023-02-20. Nonlinear response of Silicon Photonics microresonators for reservoir computing neural network. https://arxiv.org/abs/2302.10203

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