Modeling the effect of deep traps on the capacitance–voltage characteristics of p-type Si-doped GaAs Schottky diodes grown on high index GaAs substrates

Numerical simulation, using SILVACO-TCAD, is carried out to explain experimentally observed effects of different types of deep levels on the capacitance–voltage characteristics of p-type Si-doped GaAs Schottky diodes grown on high index GaAs substrates. Two diodes were grown on (311)A and (211)A oriented GaAs substrates using Molecular Beam Epitaxy (MBE). Although, deep levels were observed in both structures, the measured capacitance–voltage characteristics show a negative differential capacitance (NDC) for the (311)A diodes, while the (211)A devices display a usual behaviour. The NDC is related to the nature and spatial distribution of the deep levels, which are characterized by the Deep Level Transient Spectroscopy (DLTS) technique. In the (311)A structure only majority deep levels (hole traps) were observed while both majority and minority deep levels were present in the (211)A diodes. The simulation, which calculates the capacitance–voltage characteristics in the absence and presence of different types of deep levels, agrees well with the experimentally observed behaviour.     

Al-pillared clays supported rare earths and palladium catalysts for deep oxidation of low concentration of benzene

Al-pillared clays supported rare earths (RE/Al-PILC) are prepared and used as supports of palladium catalysts for deep oxidation of low concentrations of benzene (130-160 ppm). The supports and catalysts are characterized by X-ray powder diffraction (XRD), FT-IR, BET, transmission electron microscopy (TEM) and temperature-programmed reduction (H₂-TPR). The results show that Al-pillaring results in a strong increase in the basal spacing (d_0 0 1) from about 1.2 to 1.8 nm, and an increase in the BET surface area from 63.6 (±3.2) to 238.8 (±11.9) m²/g. Activity tests of deep oxidation of low concentration benzene show catalysts supported on Al-PILC and RE/Al-PILC are obviously more active than that on raw clay. Pd/6% Ce/Al-PILC, in particular, can catalyze the complete oxidation of low concentration benzene at a temperature as low as about 290 °C.     

光刻胶灰化工艺与深亚微米线条的制作

随着器件尺寸的缩小，细线条的制作成为很关键的工艺，普通光学光刻已接近其分辨率的极限，而电子束光刻和Ｘ射线光刻技术复杂、费用昂贵。本文对光刻胶灰化工艺进行了分析和研究，并应用此工艺进行了深亚微米线条的制作，在普通光学光刻机上制作出宽度小于０．２５μｍ细线条。我们已将此工艺成功地应用在深亚微米ＭＯＳＦＥＴ的制作中。     

19F+27Al深部非弹性碰撞产物的角分布

完成了19F+27Al深部非弹性碰撞产物的角分布测量.初步分析了反应产B,C,N,O,F,Ne,Na,Mg和Al的实验室系角分布,展现出深部非弹性反应机制的特点,显示了反应系统随时间的演化过程. Angular distributions of fragments produced in the deep inelastic collision of~(19)F+~(27)Al have been measured for incident energy of 114 MeV at θ_(lab)= 9~(o), 24~(o), 40~(o), 55~(o), 70~(o) and 85~(o). Angular distributions of dissipative products B, C, N, O, F, Ne, Na, Mg and Al are analyzed to provide an evolution process of the intermediate dinuclear system formed in the reaction.     

DX centers in AlxGa1-xAs bulk alloy,AlAs/GaAs ordered,and disordered superlattices

Properties of theDX centers in Al_0.5Ga_0.5As bulk alloy (b-AL), (AlAs)₂ (GaSa)₂ ordered superlattice (o-SL) and (AlAs) _m (GaAs) _n disordered superlattice (d-SL) (m = 1, 2, 3,n = 1, 2, 3) with the same macroscopic composition were measured and compared. By deconvolution of deep level transient spectroscopy (DLTS) spectrum due to theDX center, we have found a decrease in the number of separate peaks in DLTS spectrum in an intentionally atomic ordered arrangement. Visiting Scholar of the Japan Society for the Promotion of Science. On leave from Department of Electrical Engineering, San Jose State University, San Jose, California 95192-0084, USA.     

Accurate measurement of capture cross sections in deep level transient spectroscopy: Application to EL2 in GaAs

A rigorous formulation of capacitance changes during trap filling processes is presented and used to accurately determine the electron capture cross section of EL2 in GaAs at a particular temperature, 377K, in this case. The value, σ_n (377K) = 2.7 × 10⁻¹⁶ cm², is compared with that predicted from the emission dependence.     

基于自适应对比度增强的红外小目标检测网络

红外探测系统需要尽早发现目标以便及时拦截,但是红外图像上的小目标检测是一个挑战十足的任务。为了提高检测准确率,提出一种基于自适应对比度增强的红外小目标检测方法。为了利用自注意力机制和卷积各自的优势,设计了一个高效的特征提取网络和一个面向小目标的检测头。同时为了解决实际应用中出现的弱目标,在检测子网络前添加了一个图像预处理子网络,该模块可以自适应地调节图像对比度。在红外空中小目标数据集上的实验表明,提出的方法能达到93.76%的检测精度,与经典的检测方法相比,能够更好地平衡检测精度和召回率,证明了方法的巨大应用潜力。     

基于拓扑链路识别的光网络流量数据合成算法

基于深度学习的光网络流量诊断与预测等场景中,由于保密等原因,光链路的流量数据采集和存储工作受限。针对数据量少而无法支撑深度学习的问题,文章提出了一种基于拓扑链路识别的光网络流量数据合成算法,其核心思想是在生成对抗网络框架下,联合基于光网络拓扑的条件生成模型和基于光网络流量的数据合成模型,以自监督的方式合成指定光链路的流量数据。仿真结果表明,所提算法合成的光网络流量数据在自相关系数指标上与真实数据接近且使得基于全连接神经网络的流量预测模型准确率达到95%以上。     

基于深度学习的基金客服情绪安抚算法

构建基金智能客服情绪安抚算法,增加人性化设计以有效提升客户使用体验。由基于OCC模型的情绪粗粒度分类得到有负面情感的文本信息,对该文本信息进行相关处理,而后基于SVM模型进行情绪细粒度分类,得到文本的负面情绪类别并在囊括专业知识和情绪应答的基金领域知识库中匹配答案和情绪应答语句,将查询结果展示给客户。     

基于无人机影像的电网绝缘子自爆识别

电网因其在电能传输方面的关键性作用,在我国民生项目建设领域一直扮演着至关重要的角色。电网杆塔上的绝缘子一旦发生自爆(也称“缺陷”),绝缘子会自动剥落,输电线路就会产生安全隐患,严重时会降低输电线路的运行寿命,甚至会引发供电中断,发生大范围的停电事故,造成巨大的财产损失。目前,主流的巡检方法为人工巡检,该方法不仅耗时耗力,而且也存在一定主观出错率,已不适用于目前电路巡检的实际情况。本设计采用YOLO V5网络模型,对无人机航拍影像中绝缘子串及绝缘子自爆进行自动识别。首先通过平移、翻转、裁剪等,对航拍绝缘子影像数据集进行数据增广,并对增广后的数据集在LabelImg中进行标注,然后利用YOLO V5网络模型对绝缘子串及绝缘子自爆进行识别,最后采用PyQt5框架在PyCharm中设计了绝缘子自爆识别的系统界面,对模型进行调用,实现了绝缘子串及绝缘子自爆识别。本设计采用从网络上下载、国家电网提供、数据增广所得到的500张无人机航拍影像作为数据集,对所得数据集进行人工标注,再使用YOLO V5网络模型进行训练和测试,结果表明YOLO V5网络模型对绝缘子串具有较高的识别精度,最高识别精度为90.2%,对绝缘子自爆的最高检测精度为80.8%。这说明了YOLO V5网络模型在绝缘子串识别方面有较好的表现,但是由于训练集中绝缘子自爆的样本影像数量有限,所以该网络模型对绝缘子的自爆识别存在一定局限性,本实验能够部分代替人力实现电网绝缘子智能巡检,提高了检测效率。