Photonic Neural Networks Based on Integrated Silicon Microresonators

Abstract

Recent progress in artificial intelligence (AI) has boosted the computational possibilities in fields in which standard computers are not able to perform adequately. The AI paradigm is to emulate human intelligence and therefore breaks the familiar architecture on which digital computers are based. In particular, neuromorphic computing, artificial neural networks (ANNs), and deep learning models mimic how the brain computes. There are many applications for large networks of interconnected neurons whose synapses are individually strengthened or weakened during the learning phase. In this respect, photonics is a suitable platform for implementing ANN hardware owing to its speed, low power dissipation, and multi-wavelength opportunities. One photonic device that could serve as an optical neuron is the optical microring resonator. Indeed, microring resonators exhibit a nonlinear response and the capability for optical energy storage, which can be used to implement fading memory. In addition, their characteristic resonant behavior makes them extremely sensitive to input wavelengths, which promotes wavelength division multiplexing (WDM) applications and enables their use as WDM-based synapses (weight banks) in the linear regime. Remarkably, using silicon photonics, photonic integrated circuits can be fabricated in volume and with integrated electronics onboard. For these reasons, here, we describe the physics of silicon microring resonators and arrays of microring resonators for application in neuromorphic computing. We describe different types of ANNs, from feedforward networks to photonic extreme learning machines, and reservoir computing. In addition, we discuss hybrid systems in which silicon microresonators are coupled with other active materials. This review introduces the basics and discusses the most recent developments in the field.

Exploring Types of Photonic Neural Networks for Imaging and Computing—A Review

“…PNNs embrace a spectrum of architectures, spanning feedforward, recurrent, convolutional, and spiking neural networks, each meticulously crafted for distinct tasks and domains [ 5 , 6 ]. Noteworthy advantages of PNNs encompass their capacity for lightning-fast computation, vast parallelism, and innate adaptability to certain data processing challenges like image recognition and optimization tasks [ 7 , 8 ]. Furthermore, PNNs demonstrate potential in surmounting emerging hurdles in artificial intelligence (AI), photonics, and information processing, heralding a new era of computing paradigms poised to revolutionize an array of fields, from healthcare to telecommunications [ 9 , 10 ].…”