Surface topography dictates the deterministic functionality of diffraction by a surface. In order to maximize the efficiency with which a diffractive optical component, such as a grating or a diffractive lens, directs light into a chosen order of diffraction, it is necessary that it be "blazed". The efficiency of most diffractive optical components reported so far varies with the wavelength, and blazing is achieved only at a specific nominal energy, the blaze wavelength. The existence of spurious light in undesirable orders represents a severe limitation that prevents using diffractive components in broadband systems. Here we experimentally demonstrate that broadband blazing over almost one octave can be achieved by combining advanced optical design strategies and artificial dielectric materials that offer dispersion chromatism much stronger than those of conventional bulk materials. The possibility of maintaining an efficient funneling of the energy into a specific order over a broad spectral range may empower advanced research to achieve greater control over the propagation of light, leading to more compact, efficient and versatile optical components.
A single diffractive optical element for implementing spectrum-splitting and beam-concentration functions simultaneously with high diffraction efficiency
Massively Parallel Universal Linear Transformations using a Wavelength-Multiplexed Diffractive Optical Network
我们报告了一个大规模并行宽带衍射神经网络的基于深度学习的设计，用于全光学地执行输入和输出视场之间的一大组任意选择的复值线性变换，每个变换分别具有N_i和N_o像素。这种宽带衍射处理器由N_w个波长通道组成，每个通道被唯一地分配给不同的目标变换。可以通过相同的衍射网络在不同的照明波长下同时或顺序地（波长扫描）单独执行大量任意选择的线性变换。我们证明了这样一个宽带衍射网络，无论其材料色散如何，当其设计中的衍射神经元数目（N）匹配或超过2 x N_w x N_i x N_o时，可以成功地逼近N_w个唯一的复值线性变换，误差可以忽略不计。我们进一步报告，频谱复用能力（N_w）可以通过增加N来增加;我们的数值分析证实了N_w > 180的这些结论，其可以进一步增加到例如，~2000取决于近似误差的上限。大规模并行，波长复用衍射网络将是有用的设计高通量智能机器视觉系统和高光谱处理器，可以执行统计推断和分析具有独特的光谱特性的对象/场景。
