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 show 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. The operation of the neural field is energy efficient due to a passive scattering process, for which the required power comes only from the optical input. We also show that 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.

中文翻译：

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硅芯片上的光子神经场：大规模、高速的神经启发计算和传感

光子神经网络在具有低延迟和超低能耗的高速神经处理方面具有巨大潜力。然而，由于其低可扩展性，大规模神经网络的片上实现仍然具有挑战性。在此，我们提出了光子神经场的概念并在硅芯片上通过实验实现它，以实现高度可扩展的神经启发计算。与现有的光子神经网络相比，光子神经场是一个空间连续的场，对光输入进行非线性响应，其高空间自由度允许在毫米级芯片上进行大规模和高密度的神经处理。在这项研究中，我们使用片上光子神经场作为信息库，并使用类似于储层计算的训练方法展示了具有低错误率的高速混沌时间序列预测。我们表明，光子神经场可能能够在几平方毫米的占地面积上针对单个输入波长每秒执行 1 次以上的 peta 乘法累加操作。由于被动散射过程，神经场的操作是节能的，为此所需的功率仅来自光输入。我们还表明，除了处理之外，光子神经场还可用于快速感知光相位的时间变化，这得益于其对光输入的高灵敏度。光学处理与光学传感的融合为端到端数据驱动的光学传感方案铺平了道路。