Recent Progress of Neuromorphic Computing Based on Silicon Photonics: Electronic–Photonic Co-Design, Device, and Architecture

Abstract

The rapid development of neural networks has led to tremendous applications in image segmentation, speech recognition, and medical image diagnosis, etc. Among various hardware implementations of neural networks, silicon photonics is considered one of the most promising approaches due to its CMOS compatibility, accessible integration platforms, mature fabrication techniques, and abundant optical components. In addition, neuromorphic computing based on silicon photonics can provide massively parallel processing and high-speed operations with low power consumption, thus enabling further exploration of neural networks. Here, we focused on the development of neuromorphic computing based on silicon photonics, introducing this field from the perspective of electronic–photonic co-design and presenting the architecture and algorithm theory. Finally, we discussed the prospects and challenges of neuromorphic silicon photonics.

Editorial: Editor’s challenge in optics and photonics: Advancing electronics with photonics

“…By using photons as the information carrier rather than electrons, photonics can process more data at higher frequencies with less power consumption than conventional electronics [2]. This is particularly evident in the field of photonic computing or photonic neural network [3]. In fact, simple mathematical functions such as matrix multiplication can be performed in photonics quite easily while electronics are power-hungry [4].…”

VCSEL-based photonic spiking neural networks for ultrafast detection and tracking

“…phase-change materials [5], optical modulators [6,7], and semiconductor lasers [8][9][10][11], are a few highlight photonic devices that have achieved neuromorphic or neuromimetic operation. Similarly, electro-optic technologies such quantum resonant tunnelling structures [12,13], amongst others (see [14] for a review), have now also been proposed as suitable device platforms for brain-inspired computing hardware components. With one of the major benefits of photonics being the highly-reduced energy requirements, the recent demonstrations of photonic artificial neural networks (pANNs) [15][16][17][18][19] and reservoir computing systems [20][21][22][23] make an exciting case for photonics being the platform at the forefront of potentially sustainable neuromorphic computing.…”

Exploring nonlinear activation function within microring resonators for all-photonic neuromorphic computing

“…The research uses two temperature models to analyze how changes in refractive index affect the transmission at the output and its peak at 1550 nm. Additionally, in a different setup cited as [6], GST is applied to a Silicon ring, resulting in a transmission peak at a wavelength of 1556 nm [6]. This approach highlights the intricate relationship between material properties, temperature changes, and optical behavior in micro-ring resonators.…”