利用Sentaurus搭建了碳化硅漂移阶跃恢复二极管（DSRD）与雪崩整形二极管（DAS）全电路仿真模型，研究了碳化硅等离子体器件在脉冲锐化方面的能力，并且通过器件内部等离子浓度分布解释了这两种器件实现脉冲锐化的机制。借助碳化硅DSRD可以将峰值超过千伏的电压脉冲的前沿缩短到300 ps；碳化硅DSRD与DAS的组合可以输出脉冲前沿在35 ps、峰值超过2 kV的电压脉冲。仿真与实验发现当触发脉冲与碳化硅DAS匹配时，可以实现快速开启后快速关断，得益于碳化硅DAS这种神奇现象，可以将峰值在两千伏以上脉冲的半高宽缩小到百皮秒量级；通过频谱分析发现脉冲经过DAS整形后，其最高幅值−30 dB对应的频谱带宽扩大了37倍，达到7.4 GHz。

The high-density, vertically aligned retinal neuron array provides effective vision, a feature we aim to replicate with electronic devices. However, the conventional complementary metal-oxide-semiconductor (CMOS) image sensor, based on separate designs for sensing, memory, and processing units, limits its integration density. Moreover, redundant signal communication significantly increases energy consumption. Current neuromorphic devices integrating sensing and signal processing show promise in various computer vision applications, but there is still a need for frame-based imaging with good compatibility. In this study, we developed a dual-mode image sensor based on a high-density all-inorganic perovskite nanowire array. The device can switch between frame-based standard imaging mode and neuromorphic imaging mode by applying different biases. This unique bias-dependent photo response is based on a well-designed energy band diagram. The biomimetic alignment of nanowires ensures the potential for high-resolution imaging. To further demonstrate the imaging ability, we conducted pattern reconstruction in both modes with a 10 × 10 crossbar device. This study introduces a novel image sensor with high compatibility and efficiency, suitable for various applications including computer vision, surveillance, and robotics.

为了满足复杂多变的电磁环境对正弦电流精度和变频调幅稳定输出的要求，提出了LC串联谐振理论与高频PWM调制技术相结合的设计思想。研制了一种多频点(10～3800 Hz)、电流高达8000 A的正弦波电源。FTD电源采用直流开关电源技术、SPWM调制技术和LC串联谐振理论。输出电流工作点包括10 Hz@8000 A、20 Hz@6000 A、30～110 Hz@5000 A和1.3～3.8 kHz@2400 A。采用电流反馈和频率反馈控制策略，输出电流精度可达5%。测试结果表明，FTD电源能够满足系统要求，实现了对偏滤器偏转磁场的强度和频率的调节，为偏滤器靶板的粒子热沉积效果的对比实验提供了支持。

提出了一种基于多维高斯贝叶斯分类算法的复杂系统影响因素的分析方法，并利用大数据方法建立了不同PM_2.5范围的分类模型，结合马氏距离开展了影响因素的关键性分析。以合肥市2013―2018年间的天气与空气质量数据为基础，筛选了PM₁₀、SO₂、NO₂、CO、O₃等8个大气污染的关键影响因素，采用散点矩阵对PM_2.5与这些影响因素的相关性进行了分析。利用高斯贝叶斯分类器建立了基于8个主要参量的PM_2.5大气污染分析模型，研究发现，PM_2.5与CO浓度具有较强的正相关性，对NO₂具有选择性，与O₃具有负相关性，而CO与SO₂对PM_2.5的产生存在某种竞争机制。