Optical machine learning has emerged as an important research area that, by leveraging the advantages inherent to optical signals, such as parallelism and high speed, paves the way for a future where optical hardware can process data at the speed of light. In this work, we present such optical devices for data processing in the form of single-layer nanoscale holographic perceptrons trained to perform optical inference tasks. We experimentally show the functionality of these passive optical devices in the example of decryptors trained to perform optical inference of single or whole classes of keys through symmetric and asymmetric decryption. The decryptors, designed for operation in the near-infrared region, are nanoprinted on complementary metal-oxide–semiconductor chips by galvo-dithered two-photon nanolithography with axial nanostepping of 10 nm^1,2, achieving a neuron density of >500 million neurons per square centimetre. This power-efficient commixture of machine learning and on-chip integration may have a transformative impact on optical decryption³, sensing⁴, medical diagnostics⁵ and computing^6,7.

光学机器学习已成为一个重要的研究领域，通过利用光信号固有的优势（例如并行性和高速），为光学硬件能够以光速处理数据的未来铺平道路。在这项工作中，我们以单层纳米级全息感知器的形式提出了这种用于数据处理的光学设备，经过训练来执行光学推理任务。我们在解密器示例中通过实验展示了这些无源光学设备的功能，该解密器经过训练，可以通过对称和非对称解密对单个或整个类别的密钥进行光学推理。这些解密器专为在近红外区域运行而设计，采用振镜抖动双光子纳米光刻技术，轴向纳米步进为 10 nm ^{1 , 2} ，在互补金属氧化物半导体芯片上进行纳米印刷，实现了超过 5 亿个神经元的神经元密度每平方厘米。机器学习和片上集成的这种^高能效混合可能会对光学解密³ 、传感⁴ 、医疗诊断⁵和^{计算 6、7产生}变革性影响。