对高密度差多层球壳结构件进行双能CT检测，需要研究同时基于高低能射线两套投影数据的图像重建方法。先分别重建再进行图像融合的方法，不能充分利用两种能量射线投影的互补信息，重建图像质量不高。针对高密度差多层球壳结构特征，通过设计限定投影扫描方式获取区域分布明确的高、低能射线投影正弦图，基于正弦图区域分割重组和交集数据等值化一致性处理实现双能投影数据融合，在此基础上完成图像重建。根据以上设计归纳出基于投影融合的多层球壳双能CT重建算法，仿真结果表明算法可行，且重建结果明显好于基于图像融合的方法。

为提高X射线探测器的标定精度，在荧光X射线源的基础上，提出在荧光X射线出射通道设置滤光片的方法提高X射线纯度。通过蒙特卡罗建立仿真模型，分析了辐射体发生K层光电效应的概率与原子序数的关系，并得到荧光强度和纯度随滤光片厚度的变化曲线。在大气环境下，采用硅漂移半导体探测器测试了荧光X射线源的能谱分布和光子流量，分析X射线管管电压对光子流量和荧光纯度的影响。在辐射体材料为铜，滤光片(镍)厚度为0 μm、10 μm和30 μm时，测得的荧光X射线纯度分别为75.61%、85.38%和84.25%，光子流量分别为3425 phs/s、2023 phs/s和1192 phs/s，确认了滤光片厚度对荧光X射线纯度和强度的影响，为解决荧光X射线光源单色性不足难以对X射线探测器进行高精度标定的问题提供了方向。

Optical neural networks (ONNs), enabling low latency and high parallel data processing without electromagnetic interference, have become a viable player for fast and energy-efficient processing and calculation to meet the increasing demand for hash rate. Photonic memories employing nonvolatile phase-change materials could achieve zero static power consumption, low thermal cross talk, large-scale, and high-energy-efficient photonic neural networks. Nevertheless, the switching speed and dynamic energy consumption of phase-change material-based photonic memories make them inapplicable for in situ training. Here, by integrating a patch of phase change thin film with a PIN-diode-embedded microring resonator, a bifunctional photonic memory enabling both 5-bit storage and nanoseconds volatile modulation was demonstrated. For the first time, a concept is presented for electrically programmable phase-change material-driven photonic memory integrated with nanosecond modulation to allow fast in situ training and zero static power consumption data processing in ONNs. ONNs with an optical convolution kernel constructed by our photonic memory theoretically achieved an accuracy of predictions higher than 95% when tested by the MNIST handwritten digit database. This provides a feasible solution to constructing large-scale nonvolatile ONNs with high-speed in situ training capability.

太阳能电池片在生产过程中，因工序或材料原因会导致其存在缺陷。基于光致发光成像原理，提出了一种基于背景评估的太阳能电池片图像增强方法，以及一种基于形态特征和HOG特征融合的缺陷识别方法。首先分析了电池片缺陷的形态和位置特点，提出了缺陷两步分割法，对分割的缺陷提取多方向HOG特征，采取拉普拉斯特征映射法对HOG特征进行降维；然后融合长宽比、圆形度等形态特征；最后针对支持向量机（support vector machines，SVM）中的核函数和惩罚因子，采用粒子群算法（particle swarm optimization，PSO）加以优化，改善了缺陷分类效果。应用所确立的算法对50幅图像进行检测，分类识别的准确率最高可达98.3%。将新算法与传统的SVM算法以及Le-Net网络等进行对比，可知新算法具有较高的识别准确率。

全球网络流量急速增长，数据传输所需带宽和能源消耗也随之快速增加，传统电子信息互联架构已无法满足日益增长的带宽和节约能耗的需求。硅基光电子技术具有带宽高、能耗低并且可以利用成熟的互补金属氧化物半导体（CMOS）技术将光子集成电路和电子集成电路大规模集成在硅衬底上等优势，能满足下一代数据传输系统的迫切需求。2.5D/3D硅基光电子集成技术可以有效缩短光芯片和电芯片之间电学互连长度、减小芯片尺寸，从而减小寄生效应、提高集成密度和降低功耗。文章介绍了硅基光电子集成技术的不同方案和最新进展，并展望了硅基光电子芯片结合2.5D/3D集成技术在数据通信、激光雷达、生化传感以及光计算等领域的应用前景。

As a promising spectral window for optical communication and sensing, it is of great significance to realize on-chip devices at the 2 µm waveband. The development of the 2 µm silicon photonic platform mainly depends on the performance of passive devices. In this work, the passive devices were fabricated in the silicon photonic multi-project wafer process. The designed micro-ring resonator with a 0.6 µm wide silicon ridge waveguide based on a 220 nm silicon-on-insulator platform achieves a high intrinsic quality factor of 3.0×105. The propagation loss is calculated as 1.62 dB/cm. In addition, the waveguide crossing, multimode interferometer, and Mach–Zehnder interferometer were demonstrated at 2 µm with good performances.