宽带、紧凑且无需训练的光学处理器用于并行图像分类
Broadband, compact, and training-free optical processors for parallel image classification
AI总结：
本文提出了一种基于傅里叶表面的紧凑型光学处理器，实现无需训练的并行图像分类，具有宽带操作和高计算密度的优势。
摘要：
随着人工智能的日益普及，对更快、更节能的计算方法的需求也在增加。尽管光计算在带宽和能耗方面具有固有的优势，但现有实现仍存在体积大、波长特定且依赖复杂训练过程的限制，限制了可扩展性和并行操作。在本工作中，我们展示了一种基于波纹衍射特征的紧凑型、无需训练的光学处理器，称为傅里叶表面，用于并行图像分类。我们的设备在40×40μm²的足迹内实现了对数字数据集高达84%的分类准确率，以及对时尚数据集66%的分类准确率。衍射层会将入射波长自然地分离到不同的输出方向，使系统能够实现宽带操作，并允许多种颜色作为独立的计算通道。因此，这种被动系统支持单次光学通过内同时进行多达20次计算。这些结果突显了纳米级衍射系统实现高计算密度的潜力，为可扩展、低功耗的光处理器在机器学习和图像识别应用中的发展铺平了道路。
英文摘要：
As artificial intelligence becomes increasingly prevalent, the demand for faster and more energy-efficient computing approaches grows. While optical computing offers intrinsic advantages in bandwidth and power consumption, existing implementations remain bulky, wavelength-specific, and dependent on complex training procedures, limiting scalability and parallel operation. In this work, we demonstrate a compact, training-free optical processor based on wavy diffractive features, known as Fourier surfaces, for parallel image classification. Our device achieves classification accuracies of up to 84% for digit datasets and 66% for fashion datasets within a 40$\times$40 $μ$m$^2$ footprint. The diffractive layer inherently separates incident wavelengths into distinct output directions, enabling broadband operation and allowing multiple colors to function as independent computation channels. As a result, this passive system supports up to 20 simultaneous computations within a single optical pass. These results highlight the potential of nanoscale diffractive systems to achieve high compute densities, paving the way for scalable, low-power optical processors for machine learning and image-recognition applications.
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