On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors. The dominant paradigm for designing on-chip photonics is to make networks of relatively bulky discrete components connected by one-dimensional waveguides. A far more compact alternative is to avoid explicitly defining any components and instead sculpt the continuous substrate of the photonic processor to directly perform the computation using waves freely propagating in two dimensions. We propose and demonstrate a device whose refractive index as a function of space, $n(x,z)$, can be rapidly reprogrammed, allowing arbitrary control over the wave propagation in the device. Our device, a 2D-programmable waveguide, combines photoconductive gain with the electro-optic effect to achieve massively parallel modulation of the refractive index of a slab waveguide, with an index modulation depth of $10^{-3}$ and approximately $10^4$ programmable degrees of freedom. We used a prototype device with a functional area of $12\,\text{mm}^2$ to perform neural-network inference with up to 49-dimensional input vectors in a single pass, achieving 96% accuracy on vowel classification and 86% accuracy on $7 \times 7$-pixel MNIST handwritten-digit classification. This is a scale beyond that of previous photonic chips relying on discrete components, illustrating the benefit of the continuous-waves paradigm. In principle, with large enough chip area, the reprogrammability of the device's refractive index distribution enables the reconfigurable realization of any passive, linear photonic circuit or device. This promises the development of more compact and versatile photonic systems for a wide range of applications, including optical processing, smart sensing, spectroscopy, and optical communications.

翻译：用于神经网络的片上光子处理器在速度和能效方面具有潜在优势，但尚未达到超越电子处理器的规模。设计片上光子器件的主流范式是构建由一维波导连接、相对笨重的分立元件网络。一种更为紧凑的替代方案是避免明确定义任何元件，而是直接对光子处理器的连续基底进行塑形，利用在二维空间中自由传播的波来直接执行计算。我们提出并演示了一种器件，其折射率作为空间函数$n(x,z)$可被快速重新编程，从而实现对器件内波传播的任意控制。我们的器件——一种二维可编程波导——结合了光电导增益与电光效应，实现了对平板波导折射率的大规模并行调制，折射率调制深度达$10^{-3}$，并具有约$10^4$个可编程自由度。我们使用功能面积为$12\,\text{mm}^2$的原型器件，在单次传播中执行最多含49维输入向量的神经网络推理，在元音分类任务上达到96%的准确率，在$7 \times 7$像素MNIST手写数字分类任务上达到86%的准确率。这一规模超越了以往依赖分立元件的光子芯片，体现了连续波范式的优势。原则上，只要芯片面积足够大，器件折射率分布的可重编程性便能在配置上实现任何无源线性光子电路或器件。这为开发更紧凑、更通用的光子系统提供了前景，可广泛应用于光学处理、智能传感、光谱学和光通信等领域。