研究了富勒烯对六官能团脂肪族聚氨酯丙烯酸酯/环氧树脂(RJ423/ EPIKOTE 828)体系光致聚合物的全息性能的影响，分析了富勒烯(C₆₀)材料以及曝光光强对光致聚合物衍射效率的影响。通过吸收光谱结合X射线衍射图谱分析，掺杂的富勒烯既没有和聚合物中的其他成分发生化学反应，也不影响材料本身的结构和结晶性能。实验结果表明，富勒烯掺杂提高了单体聚合反应的速率，并且参与活性单体分子之间的扩散。在光强20 mW/cm²、时间40～50 s、厚度200 μm时，光致聚合物的衍射效率提升到86%，感光灵敏度达到1.32 cm²/J，收缩率降低了约80%。由于C₆₀促进聚合，抑制体积收缩，从而增强全息存储的稳定性，经过全息图像存储实验对比，证明该光致聚合物掺杂富勒烯之后具有优秀的全息存储性能，同时也表明富勒烯掺杂的光致聚合物在全息储存领域具有较大的应用前景。

As an optical processor, a diffractive deep neural network (D²NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees of freedom, D²NNs can perform arbitrary complex-valued linear transformations using spatially coherent light. Similarly, D²NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination; however, under spatially incoherent light, these transformations are nonnegative, acting on diffraction-limited optical intensity patterns at the input field of view. Here, we expand the use of spatially incoherent D²NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light. Through simulations, we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products, a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination. The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution. In this work, an all-optical diffractive neural network (DPENet) based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed. Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges. Simulation results show that DPENet achieves F-scores of 0.9308 (MNIST) and 0.9352 (NIST) and enables real-time edge detection of biological cells, achieving an F-score of 0.7462.

对于宽入射角工作的衍射光学元件，提出了高带宽积分平均衍射效率的优化设计方法。通过复合带宽积分平均衍射效率的最大化设计，得到设计波长和设计入射角度，进一步可以计算得到微结构高度。分析了衍射效率对折衍射混合光学系统调制传递函数的影响。优化设计了一套含有衍射光学元件的折衍射混合目镜系统。目镜由三片透镜组成。考虑衍射光学元件的衍射效率，最大视场处目镜系统的调制传递函数高于0.42。该方法为混合光学系统的像质评价提供了依据。

Diffractive optical elements (DOEs) are intricately designed devices with the purpose of manipulating light fields by precisely modifying their wavefronts. The concept of DOEs has its origins dating back to 1948 when D. Gabor first introduced holography. Subsequently, researchers introduced binary optical elements (BOEs), including computer-generated holograms (CGHs), as a distinct category within the realm of DOEs. This was the first revolution in optical devices. The next major breakthrough in light field manipulation occurred during the early 21st century, marked by the advent of metamaterials and metasurfaces. Metasurfaces are particularly appealing due to their ultra-thin, ultra-compact properties and their capacity to exert precise control over virtually every aspect of light fields, including amplitude, phase, polarization, wavelength/frequency, angular momentum, etc. The advancement of light field manipulation with micro/nano-structures has also enabled various applications in fields such as information acquisition, transmission, storage, processing, and display. In this review, we cover the fundamental science, cutting-edge technologies, and wide-ranging applications associated with micro/nano-scale optical devices for regulating light fields. We also delve into the prevailing challenges in the pursuit of developing viable technology for real-world applications. Furthermore, we offer insights into potential future research trends and directions within the realm of light field manipulation.