自加热非晶锗热电阻MEMS流速传感器的制造与测试

为扩大流速传感器的测量范围并降低功耗,制造并测试了一种基于自加热非晶锗薄膜热电阻的MEMS流速传感器,它是由嵌入氮化硅薄膜的四个非晶锗热敏电阻和一对环境测温补偿电阻组成。四个非晶锗热电阻同时作为自加热热源和测温元件,相互连接以形成惠斯通电桥。给出了MEMS工艺流程,微加工制造了尺寸为8.9 mm×5.6 mm×0.4 mm的流速传感器芯片。搭建了低流速和高流速气流通道实验装置,对传感器的惠斯通电桥施加50μA的恒定电流(CCA),实现了0～50 m/s范围内的流速测量。结果表明,传感器在低流速(0～2 m/s)时的灵敏度约为81.6 mV/(m/s),在高流速(2～50 m/s)时的灵敏度约为51.9 mV/(m/s),最大功耗仅约为1.03 mW。     

碳纳米管场效应晶体管紧凑模型研究进展

随着摩尔定律逼近极限,碳纳米管场效应晶体管(CNTFET)被认为是5 nm以下CMOS晶体管的有力替代者。CNTFET具有准一维结构,栅极可有效控制导电沟道的导通/关断;同时,载流子在沟道内可实现近弹道输运,具有极高的迁移率。因此,CNTFET在低电压环境下,可提供较大的电流传输能力,为实现纳米级超大规模模拟/逻辑电路提供了解决方案。文章综述了CNTFET紧凑模型的发展现状,分析了现阶段面临的漏极电流精确模型、隧穿效应、寄生效应、多纳米管模型等存在的问题,重点探讨了针对这些问题的解决方案。最后对该紧凑模型未来的应用前景进行了讨论。     

基于微纳电离的有毒有害气体传感器

为了有效避免有害气体浓度的超标对经济效益和人类生命财产安全造成损害,提出并设计了一种新型检测方法,通过对微纳电离型传感器进行一系列的测试,设计的传感器展现了较强的抗弯能力及较快的响应-恢复能力。在室温常压、相对湿度75%的实验条件下,对多个浓度的苯、甲苯气体展开了测试,并基于特征噪声强度识别气体的浓度,构建了对应的识别模型。实验结果表明：传感器对有害气体检测时的测量值和真实值的拟合度可达99%以上,体现了较高的检测精度,且在检测有害气体时传感器处于可逆电离平衡状态,重复性良好,不需要进行预热、安全无毒,因此适合于应用到苯类气体的检测中,能够满足效率、精度的要求,具备了一定的实用性。     

基于transformer自适应特征向量融合的图像分类

针对目前基于transformer的图像分类模型直接应用在小数据集上性能较差的问题,本文提出了transformer自适应特征向量融合网络,该网络在特征提取器中将不同阶段的特征进行融合,减少特征信息丢失的同时获得更多不同感受野下的信息,同时利用最大池化来去除特征中的冗余信息,从而使提取的特征更具有判别性。此外,为了充分利用图像的各级特征信息来进行分类预测,本文将网络各阶段产生的特征向量进行融合,使融合后的特征向量更具有表征能力,从而减少网络对大数据集的依赖,使网络在小数据集中也能获得很好的性能。实验表明,本文提出的 算法在数据集Mini-ImageNet-100、CIFAR-100和ImageNet-1k上的TOP-1准确率分别达到了74.22%、85.86%和81.4%。在没有增加计算量的情况下,在baseline上分别提高了6.0%、3.0%和0.1%,且参数量减少了18.3%。本文代码开源在“https://github.com/xhutongxue/afvf”。     

低电平扫描电流高强辐射场试验方法研究

低电平扫描电流试验(LLSC)是飞机高强辐射场(HIRF)试验的一部分,其实质是在2~400 MHz 频段 内对飞机进行电磁辐照,以确定外部HIRF 环境与设备线束感应电流之间的传递函数。基于RTCA/ DO-160G 和SAE ARP5583A 标准建立HIRF 测试环境,通过对AC312E 直升机开展低电平扫描电流试验,得到了线束传递函数随频 率、天线极化方式、电磁辐照方向、线束布置的变化规律并分析了其影响机理。结果表明:发射天线以不同极化方式 辐照机体时,不同布置的线束其传递函数有一定差别;发射天线以垂直极化在0°方向辐照时,行李舱附近右发动机 控制单元(EECUR)线束的传递函数比位于驾驶舱的座舱显示器(MFD)线束的传递函数要小;发射天线以垂直极化 方式在不同方向辐照机体时,EECUR 线束在某些频段下在右侧90°方向辐照比左侧270°方向辐照的传递函数要大。 所得结论可为直升机适航符合性验证和低电平扫描电流试验的开展提供参考与支撑。     

一种变压器巴伦馈电的超短波紧耦合阵列天线

紧耦合阵列天线(Tightly Coupled Array, TCA)具有超宽带、低剖面、宽角扫描等特点,近年来受到国内外学者的广泛关注。然而,在甚高频(VHF)、特高频(UHF)频段TCA 由于馈电网络设计困难等原因现有的研究报道很少。为了填补这一空白,本文设计并测试了一款变压器巴伦馈电的TCA,利用变压器巴伦解决了单元平衡馈电困难问题,同时采用短路枝节抑制E面共模谐振,采用频率选择表面(FSS)匹配层改善宽角扫描阻抗匹配。实测结果表明,该天线在110~520 MHz 频带内、±40°扫描角范围内电压驻波比<2.5,和仿真结果吻合。     

Probing into Reverse Bias Dark Current in Perovskite Photodiodes: Critical Role of Surface Defects

Minimizing reverse bias dark current density (J_dark) while retaining high external quantum efficiency is crucial for promising applications of perovskite photodiodes, and it remains challenging to elucidate the ultimate origin of J_dark. It is demonstrated in this study that the surface defects induced by iodine vacancies are the main cause of J_dark in perovskite photodiodes. In a targeted way, the surface defects are thoroughly passivated through a simple treatment with butylamine hydroiodide to form ultrathin 2D perovskite on its 3D bulk. In the passivated perovskite photodiodes, J_dark as low as 3.78 × 10^-10 A cm^-2 at -0.1 V is achieved, and the photoresponse is also enhanced, especially at low light intensities. A combination of the two improvements realizes high specific detectivity up to 1.46 × 10¹² Jones in the devices. It is clarified that the trap states induced by the surface defects can not only raise the generation-recombination current density associated with the Shockley–Read–Hall mechanisms in the dark (increasing J_dark), but also provide additional carrier recombination paths under light illumination (decreasing photocurrent). The critical role of surface defects on J_dark of perovskite photodiodes suggests that making trap-free perovskite thin films, for example, by fine preparation and/or surface engineering, is a top priority for high-performance perovskite photodiodes.     

Transformer-based global–local feature learning model for occluded person re-identification

Most recent occluded person re-identification (re-ID) methods usually learn global features directly from pedestrian images, or use additional pose estimation and semantic analysis model to learn local features, while ignoring the relationship between global and local features, thus incorrectly retrieving different pedestrians with similar attributes as the same pedestrian. Moreover, learning local features using auxiliary models brings additional computational cost. In this work, we propose a Transformer-based dual-branch feature learning model for occluded person re-ID. Firstly, we propose a global–local feature interaction module to learn the relationship between global and local features, thus enhancing the richness of information in pedestrian features. Secondly, we randomly erase local areas in the input image to simulate the real occlusion situation, thereby improving the model’s adaptability to the occlusion scene. Finally, a spilt group module is introduced to explore the local distinguishing features of pedestrian. Numerous experiments validate the effectiveness of our proposed method.     

CuI Encapsulated within Single-Walled Carbon Nanotube Networks with High Current Carrying Capacity and Excellent Conductivity

High current carrying capacity and high conductivity are two important indicators for materials used in microscale electronics and inverters. However, it is challenging to obtain high conductivity and high current carrying capacity at the same time since high conductivity requires a weakly bonded system to provide free electrons, while high current carrying capacity requires a strongly bonded system. In this paper, CuI@SWCNT networks by filling the single-walled carbon nanotubes (SWCNTs) with CuI is ingeniously prepared. CuI@SWCNT shows good stability due to the confinement protection of SWCNTs. Through the host-guest hybridization, CuI@SWCNT networks exhibit a current carrying capacity of 2.04 × 10⁷ A cm⁻² and a conductivity of 31.67 kS m⁻¹. Their current carrying capacity and conductivity are significantly improved compared with SWCNT. The Kelvin probe force microscopy measurements show a drop of surface potential energy after SWCNT filled with CuI, indicating that the CuI guest molecules regulate the position of the Fermi level of SWCNTs, increasing carrier concentration, achieving high conductivity and high current carrying capacity. This study offers ideas and solutions for the regulation of high-performance carbon tube networks, which hold great promise for future applications in carbon-based electronic devices.     

基于改进麻雀搜索算法优化BN的变压器故障诊断研究

针对变压器故障诊断准确率低和稳定性差的问题,文中提出了一种改进麻雀搜索算法优化贝叶斯网络的变压器故障诊断方法。首先,通过计算互信息建立最大支撑树并进行定向处理得到贝叶斯网络初始结构即初始种群。然后,在算法中引入一种新的合作机制和正弦余弦算法,提高算法收敛速度和全局搜索能力,并利用油中溶解气体分析,创建基于改进麻雀搜索算法优化贝叶斯网络的变压器故障诊断模型。最后,为了证明所提方法的优越性,将所提的方法与现有变压器故障诊断方法进行对比。结果表明,文中所提出的方法故障诊断率最高,可以更精准地对变压器进行故障诊断。