# Photonic neural field on a silicon chip: large-scale, high-speed neuro-inspired computing and sensing

arXiv: https://arxiv.org/abs/2105.10672 · HTML: https://mlsanity.com/paper/2105.10672/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2021.

## Built on by (most cited first, 6 of 6)

- [Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing](https://mlsanity.com/paper/2302.10761/) (2023)
- [Conflict-free joint decision by lag and zero-lag synchronization in laser network](https://mlsanity.com/paper/2307.15373/) (2023)
- [Time-shift selection for reservoir computing using a rank-revealing QR algorithm](https://mlsanity.com/paper/2211.17095/) (2022)
- [1-bit Quantized On-chip Hybrid Diffraction Neural Network Enabled by Authentic All-optical Fully-connected Architecture](https://mlsanity.com/paper/2404.07443/) (2024)
- [Photonic reservoir computing with complex networks](https://mlsanity.com/paper/2607.23285/) (2026)
- [Approximate reservoir computing with a semiconductor laser for reducing energy consumption](https://mlsanity.com/paper/2607.23288/) (2026)

## Abstract (arXiv)

Photonic neural networks have significant potential for high-speed neural processing with low latency and ultralow energy consumption. However, the on-chip implementation of a large-scale neural network is still challenging owing to its low scalability. Herein, we propose the concept of a photonic neural field and implement it experimentally on a silicon chip to realize highly scalable neuro-inspired computing. In contrast to existing photonic neural networks, the photonic neural field is a spatially continuous field that nonlinearly responds to optical inputs, and its high spatial degrees of freedom allow for large-scale and high-density neural processing on a millimeter-scale chip. In this study, we use the on-chip photonic neural field as a reservoir of information and demonstrate a high-speed chaotic time-series prediction with low errors using a training approach similar to reservoir computing. We discuss that the photonic neural field is potentially capable of executing more than one peta multiply-accumulate operations per second for a single input wavelength on a footprint as small as a few square millimeters. In addition to processing, the photonic neural field can be used for rapidly sensing the temporal variation of an optical phase, facilitated by its high sensitivity to optical inputs. The merging of optical processing with optical sensing paves the way for an end-to-end data-driven optical sensing scheme.