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Photonic neural networks have significant potential for high-speed neuralprocessing with low latency and ultralow energy consumption. However, theon-chip implementation of a large-scale neural network is still challengingowing to its low scalability. Herein, we propose the concept of a photonicneural field and implement it experimentally on a silicon chip to realizehighly scalable neuro-inspired computing. In contrast to existing photonicneural networks, the photonic neural field is a spatially continuous field thatnonlinearly responds to optical inputs, and its high spatial degrees of freedomallow for large-scale and high-density neural processing on a millimeter-scalechip. In this study, we use the on-chip photonic neural field as a reservoir ofinformation and demonstrate a high-speed chaotic time-series prediction withlow errors using a training approach similar to reservoir computing. We discussthat the photonic neural field is potentially capable of executing more thanone peta multiply-accumulate operations per second for a single inputwavelength on a footprint as small as a few square millimeters. In addition toprocessing, the photonic neural field can be used for rapidly sensing thetemporal variation of an optical phase, facilitated by its high sensitivity tooptical inputs. The merging of optical processing with optical sensing pavesthe way for an end-to-end data-driven optical sensing scheme.

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