Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale

A photonic neural network utilizes photons instead of electrons to process information, with the prospect of higher computing efficiency, lower power consumption, and reduced latency. This paper reviews several recent breakthroughs in largescale photonic neural networks incorporating nonlinear operations. Specifically, we highlight our recent work, which leverages multiple light scattering and second harmonic generation in a slab of disordered lithium niobate nanocrystals for high-performance nonlinear photonic computing. The interplay of these optical effects not only enhances the computational capabilities of photonic neural networks but also increases the number of photonic computing operations. In addition, we discuss current challenges and outline future directions of nonlinear photonic computing. These advancements pave the way for exploring new frontiers in optical computing, unlocking opportunities for innovative experimental implementations, broad applications, and theoretical foundations of photonic neural networks.

Publication Details

Journal
Repository for Publications and Research Data (ETH Zurich)
Published
2025-04-01
DOI
https://doi.org/10.3929/ethz-b-000726246
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale

Repository for Publications and Research Data (ETH Zurich)
Neural Networks and Reservoir Computing
article

Photonics Breakthroughs 2024: Nonlinear Photonic Computing at Scale

article en

Abstract

A photonic neural network utilizes photons instead of electrons to process information, with the prospect of higher computing efficiency, lower power consumption, and reduced latency. This paper reviews several recent breakthroughs in largescale photonic neural networks incorporating nonlinear operations. Specifically, we highlight our recent work, which leverages multiple light scattering and second harmonic generation in a slab of disordered lithium niobate nanocrystals for high-performance nonlinear photonic computing. The interplay of these optical effects not only enhances the computational capabilities of photonic neural networks but also increases the number of photonic computing operations. In addition, we discuss current challenges and outline future directions of nonlinear photonic computing. These advancements pave the way for exploring new frontiers in optical computing, unlocking opportunities for innovative experimental implementations, broad applications, and theoretical foundations of photonic neural networks.

Repository for Publications and Research Data (ETH Zurich)
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Neural Networks and Reservoir Computing
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