Opt Lett. 2023 Jan 15;48(2):219-222. doi: 10.1364/OL.477605.
Multilayer diffractive optical neural networks (DONNs) can perform machine learning (ML) tasks at the speed of light with low energy consumption. Decreasing the number of diffractive layers can reduce inevitable material and diffraction losses to improve system performance, and incorporating compact devices can reduce the system footprint. However, current analytical DONN models cannot accurately describe such physical systems. Here we show the ever-ignored effects of interlayer reflection and interpixel interaction on the deployment performance of DONNs through full-wave electromagnetic simulations and terahertz (THz) experiments. We demonstrate that the drop of handwritten digit classification accuracy due to reflection is negligible with conventional low-index THz polymer materials, while it can be substantial with high-index materials. We further show that one- and few-layer DONN systems can achieve high classification accuracy, but there is a trade-off between accuracy and model-system matching rate because of the fast-varying spatial distribution of optical responses in diffractive masks. Deep DONNs can break down such a trade-off because of reduced mask spatial complexity. Our results suggest that new accurate and trainable DONN models are needed to advance the development and deployment of compact DONN systems for sophisticated ML tasks.
多层衍射光学神经网络 (DONN) 可以以低能耗的光速执行机器学习 (ML) 任务。减少衍射层的数量可以减少不可避免的材料和衍射损耗，从而提高系统性能，而采用紧凑的设备可以减小系统占地面积。然而，目前的分析 DONN 模型无法准确描述这种物理系统。通过全波电磁模拟和太赫兹 (THz) 实验，我们展示了多层 DONN 系统中各层间反射和像素间相互作用对系统部署性能的忽略效应。我们证明，由于反射导致的手写数字分类准确率下降在传统的低折射率 THz 聚合物材料中可以忽略不计，但在高折射率材料中可能会很显著。我们进一步表明，单层和少数层 DONN 系统可以实现高分类准确率，但由于衍射掩模中光学响应的空间分布变化很快，准确性和模型系统匹配率之间存在权衡。深度 DONN 可以打破这种权衡，因为掩模的空间复杂性降低了。我们的结果表明，需要新的准确且可训练的 DONN 模型来推进紧凑 DONN 系统的开发和部署，以实现复杂的 ML 任务。
