  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.
