Impact of dark current on pinned photo-diode capacitance of CMOS image sensor in low illumination regime

Applications for quanta and space sensing both depend on efficient low-light imaging.To precisely optimize and design image sensor pixels for these applications,it is crucial to analyze the mechanisms behind dark current generation,considering factors such as temperature,trap cross-section and trap concentration.The thresholds for these generating effects are computed using optoelectrical technology computer aided design(TCAD)simulations,and the ensuing changes in pinned photo-diode(PPD)dynamic capacitance are observed.Various generation models along with an interfacial trap model are used to compare PPD capacitance fluctuations during light and dark environments.With the use of this comparison study,current compact models of complementary metal oxide semiconductor(CMOS)image sensors can be modified to accurately capture the impacts of dark current in low-light conditions.The model developed through this study demonstrates a deviation of only 6.85%from the behavior observed in physical devices.These results not only enhance our understanding of dark current generation mechanisms but also offer practical applications by improving the performance and accuracy of image sensors.

Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.

N-Doped rGO-Like Carbon Prepared from Coconut Shell:Structure and Specific Capacitance

An rGO−like carbon compound has been synthesized from biomass,i.e.,old coconut shell,by a carbonization process followed by heating at 400°C for 5 h.The nitrogen doping was achieved by adding the urea(CH4N2O)and stirring at 70°C for 14 h.The morphology and structure of the rGO-like carbon were investigated by electron microscopies and Raman spectroscopy.The presence of C-N functional groups was analyzed by Fourier transform infrared and synchrotron X-ray photoemission spectroscopy,while the particle and the specific capacitance were measured by particle sizer and cyclic voltammetry.The highest specific capacitance of 72.78 F/g is achieved by the sample with 20%urea,having the smallest particles size and the largest surface area.The corresponding sample has shown to be constituted by the appropriate amount of C–N pyrrolic and pyridinic defects.

Design optimization of a silicon-germanium heterojunction negative capacitance gate-all-around tunneling field effect transistor based on a simulation study

The steep sub-threshold swing of a tunneling field-effect transistor(TFET)makes it one of the best candidates for lowpower nanometer devices.However,the low driving capability of TFETs prevents their application in integrated circuits.In this study,an innovative gate-all-around(GAA)TFET,which represents a negative capacitance GAA gate-to-source overlap TFET(NCGAA-SOL-TFET),is proposed to increase the driving current.The proposed NCGAA-SOL-TFET is developed based on technology computer-aided design(TCAD)simulations.The proposed structure can solve the problem of the insufficient driving capability of conventional TFETs and is suitable for sub-3-nm nodes.In addition,due to the negative capacitance effect,the surface potential of the channel can be amplified,thus enhancing the driving current.The gateto-source overlap(SOL)technique is used for the first time in an NCGAA-TFET to increase the band-to-band tunneling rate and tunneling area at the silicon-germanium heterojunction.By optimizing the design of the proposed structure via adjusting the SOL length and the ferroelectric layer thickness,a sufficiently large on-state current of 17.20μA can be achieved and the threshold voltage can be reduced to 0.31 V with a sub-threshold swing of 44.98 mV/decade.Finally,the proposed NCGAA-SOL-TFET can overcome the Boltzmann limit-related problem,achieving a driving current that is comparable to that of the traditional complementary metal-oxide semiconductor devices.

Structure-guided Capacitance Relationships in Oxidized Graphene Porous Materials Based Supercapacitors

Supercapacitors formed from porous carbon and graphene-oxide(GO)materials are usually dominated by either electric double-layer capacitance,pseudo-capacitance,or both.Due to these combined features,reduced GO materials have been shown to offer superior capacitance over typical nanoporous carbon materials;however,there is a significant variation in reported values,ranging between 25 and 350 F g^(−1).This undermines the structure(e.g.,oxygen functionality and/or surface area)-performance relationships for optimization of cost and scalable factors.This work demonstrates important structure-controlled charge storage relationships.For this,a series of exfoliated graphene(EG)derivatives are produced via thermal-shock exfoliation of GO precursors and following controlled graphitization of EG(GEG)generates materials with varied amounts of porosity,redox-active oxygen groups and graphitic components.Experimental results show significantly varied capacitance values between 30 and 250 F g^(−1)at 1.0 A g^(−1)in GEG structures;this suggests that for a given specific surface area the redox-active and hydrophilic oxygen content can boost the capacitance to 250–300%higher compared to typical mesoporous carbon materials.GEGs with identical oxygen functionality show a surface area governed capacitance.This allows to establish direct structure-performance relationships between 1)redox-active oxygen functional concentration and capacitance and 2)surface area and capacitance.

Integration of pore structure modulation and B,N co-doping for enhanced capacitance deionization of biomass-derived carbon

Biomass-derived carbon has demonstrated great potentials as advanced electrode for capacitive deionization(CDI),owing to good electroconductivity,easy availability,intrinsic pores/channels.However,conventional simple pyrolysis of biomass always generates inadequate porosity with limited surface area.Moreover,biomass-derived carbon also suffers from poor wettability and single physical adsorption of ions,resulting in limited desalination performance.Herein,pore structure optimization and element co-doping are integrated on banana peels(BP)-derived carbon to construct hierarchically porous and B,N co-doped carbon with large ions-accessible surface area.A unique expansionactivation(EA)strategy is proposed to modulate the porosity and specific surface area of carbon.Furthermore,B,N co-doping could increase the ions-accessible sites with improved hydrophilicity,and promote ions adsorption.Benefitting from the synergistic effect of hierarchical porosity and B,N co-doping,the resultant electrode manifest enhanced CDI performance for NaCl with large desalination capacity(29.5 mg g^(-1)),high salt adsorption rate(6.2 mg g^(-1)min^(-1)),and versatile adsorption ability for other salts.Density functional theory reveals the enhanced deionization mechanism by pore and B,N co-doping.This work proposes a facile EA strategy for pore structure modulation of biomass-derived carbon,and demonstrates great potentials of integrating pore and heteroatoms-doping on constructing high-performance CDI electrode.

Compensation of Parasitic Capacitance of Quartz Tuning Fork in AFM

We have built an atomic force microscope using a quartz tuning fork as sensor. The excitation method we adopted, the electrical excitation, introduces stray capacitance into the signal-processing circuit. In this report, we demonstrated a simple but effective method to compensate for this parasitic capacitance by adding a compensator circuit consisting of an inverting amplifier and a capacitor. The capacitor is connected in series with the inverting amplifier and the compensator is connected in parallel with the quartz tuning fork. The resonance curve of the system measured after adding the homemade compensator resembles that of a pure RLC circuit, meaning that the stray capacitance is successfully eliminated. Furthermore, we tried to use our equipment to measure PDMS sample and got clean data. This system can be further combined with confocal microscope and diamond with NV defect to build scanning NV magnetometry.

分布电容对引信共线装定系统信息传输特性的影响及其优化

针对引信共线装定过程中分布电容影响装定可靠性的问题,建立基于装定器模块、能量与信息传输模块、引信电路模块的等效电路模型。分别研究在装定输出信号为高电平时和低电平时两种情况下,分布电容对共线装定系统信息传输特性的影响。得出高电平时分布电容对共线装定系统影响较小的结论,并推导低电平时引信识别到的接收信号的低电平脉宽与分布电容的关系式。提出两种优化方法,一种利用二极管屏蔽外部因素如并入电缆引入的电容;另一种利用并联一个阻值较小电阻的方法来降低系统的时间常数,从而提高引信识别到的低电平脉宽,提高装定可靠性。经仿真和实验验证,两种方法均提高了引信识别接收信号的可靠性。

基于MEMS技术的集成压力-湿度传感器

设计了一种高灵敏度的集成压力和湿度传感单元的芯片。压力传感单元基于SOI和蛇形电阻结构。室温下,传感器在载压范围为3~129 kPa内灵敏度为0.026 mV/kPa,与有限元仿真基本吻合。传感器在25~120℃范围内的热灵敏度漂移为0.004‰FS/℃,热零点漂移为0.25%FS/℃。湿度传感单元采用的叉指电极和电容式结构,引入含氟PI作湿度敏感膜。设计Mo电阻加热结构加快传感器降湿过程,缩短降湿时间近32%。在10%~90%RH的湿度范围,含氟PI湿度传感器的灵敏度为0.121 pF/%RH,略低于无氟PI器件。含氟基团的引入,使得传感器的湿滞较无氟PI降低16%。“电容-湿度”曲线呈指数分布,相关系数R^(2)=0.996。测试结果发现,湿度传感单元和压力传感单元拥有良好的独立工作性能。

基于Modbus/TCP的电容式扭矩传感器数据采集系统设计

扭矩传感器通常安装于机械设备的旋转关节处,用于扭矩的测量。电容式扭矩传感器需要配备专门的数据采集系统才能使用,且数据采集系统的性能直接影响传感器的精确测量。针对实验室自主研制的电容式扭矩传感器的应用需求,设计了一种基于Modbus/TCP协议的数据采集系统。系统以STM32为主控芯片,通过控制电容数字转换芯片AD7147实现电容信号采集与转换,然后由W5500以太网芯片将数据上传到自主开发的上位机软件中进行处理、显示与存储。实验测试表明:系统最大测量误差为1.47%,数据输出频率为1000 Hz。

同面阵列电极敏感场优化方法

同面阵列电极敏感场的非线性特性导致图像重建过程存在严重的不适定性,为提高同面3×4阵列电容成像系统的成像质量与稳定性,提出了一种数据降维并结合平方增敏的方法对敏感场进行优化。该方法依据最大熵原理实现敏感场的自适应降维寻优,重构得到的特征敏感场与实际敏感场具有更高的特征匹配度。另外,平方化的增敏方式可以增强敏感场特征数据与和非特征数据的对比度,同时还可以减弱敏感场矢量求解造成的特征边缘波动性。最后通过同面3×4阵列电容成像系统,设计了多组实验对所提优化方法的有效性进行验证,结果表明敏感场优化后的重建图像质量与稳定性明显提高。

发电机定子环形励磁绕组寄生电容解析模型与容值降低方法

励磁控制器是调节发电机励磁电流,控制发电机电压的核心部件。采用宽禁带功率器件(例如碳化硅、氮化镓器件)的励磁控制器具有损耗低、开关频率高的优点,而电路中的寄生电容会影响功率管的开关性能,增加开关损耗,带来震荡和电磁干扰问题等。励磁绕组寄生电容是励磁控制电路的重要组成部分,降低其容值可有效提升控制器性能。为了探寻有效的容值降低方法,建立考虑匝间距离、匝到铁心距离和层间距离的多层绕组寄生电容解析模型;通过田口法计算参数灵敏度得到影响寄生电容的关键参数,研究寄生电容的变化规律;在考虑槽空间约束情况下得到降低多层定子环形励磁绕组寄生电容的有效方法;通过实验验证解析模型的准确性和降低寄生电容方法的有效性。

舰船内高能量密度电容过载爆炸损伤分析

文中通过建立电容单元的过载爆炸能量等效模型,运用任意拉格朗日-欧拉(ALE)方法求解冲击波与结构的流固耦合过程,模拟电容组中电容单元的爆炸过程随不同电容单元间距,不同位置电容单元爆炸对整个电容组的损伤.分析不同工况下电容组的损伤结果,发现增加电容单元间距可以有效地降低电容组整体的损伤程度.在相同位置电容单元爆炸时,不同电容单元间距对正前方和正侧方的电容单元损伤不同:间距增加至2倍时,正前方最大位移约降低32.17%,正侧方电容单元的最大位移约降低24.19%;间距增至3倍时,正前方电容单元的最大位移约降低48.69%,正侧方电容单元的最大位移约降低41.13%.

基于非凸与不可分离正则化算法的电容层析成像图像重建

搅拌器内两相混合是化工生产中常见的现象,电容层析成像(ECT)技术主要对两相分布进行可视化重构,以达到监测的目的。受稀疏贝叶斯学习的启发,提出了一种非凸与不可分离正则化(NNR)算法重建ECT图像。在稀疏先验的基础上引入矩阵低秩特性,采用最大后验估计在潜在空间中提出一个新的优化问题,利用对偶变量将潜在空间的目标函数映射到原始空间进行迭代求解,用来恢复同时稀疏与低秩的矩阵。与凸近似L1范数相比,NNR算法可获得更准确的重建图像,同时比非凸可分离方法更容易收敛到全局最优解。为验证NNR算法的重建效果,通过数值仿真与静态实验的方法分别与其他5种算法进行重建对比。结果表明:NNR算法可以有效减少重建伪影,提升中心物体的重建质量,为搅拌器内两相分布提供了高质量的重建算法。

基于容性耦合的钢轨压力传感器电磁干扰分析

目前随着弱电装置在铁路通信、传感测量等方面的普及,电磁干扰问题越来越引起人们的重视,但是关于电磁干扰机理的耦合分析等方面的工作开展较少。本文针对一则典型的钢轨压力传感器电磁干扰问题,从干扰源排查入手,建立了电源线与信号线的容性耦合物理模型,提取线缆间分布电容参数,定量分析了耦合电场大小;同时找出干扰源,确定为电源线容性耦合导致的串扰电压干扰,给出了相应抑制干扰的措施,该分析与实测相符合。研究结果表明,接地装置对电源线地线的电磁干扰影响很大,从而进一步影响了信号线的信号电压。当接地电阻的埋入深度从0.1 m增加到0.6 m时,电源线的干扰电压得到了抑制;信号线的连接端头是容易耦合到外界电磁干扰的薄弱环节,是改善屏蔽效能的关键点。本文所提出的关于容性耦合的机理分析、计算方法、以及传感器信号的电磁干扰抑制措施,能够为今后电气设备的电磁干扰分析抑制提供普遍的借鉴意义。

基于新型CD单元的两相交错并联高增益Boost变换器

为减少基于电容-二极管(CD)升压单元的两相交错并联高增益Boost变换器的CD单元数量,提升变换器电压增益,提出一种最后两级CD单元电容并联充电、串联供电的新型两相交错Boost变换器拓扑结构,进一步发挥CD单元的升压能力。分析新型3CD、4CD两相交错并联Boost变换器的拓扑演化过程,提出新型NCD两相交错并联Boost变换器的拓扑演化规律。以新型4CD两相交错并联Boost变换器为例,分析变换器工作原理,以及电感、电容寄生电阻对变换器电压增益的影响。最后在StarSim硬件在环实验平台搭建1 kW的新型4CD单元交错并联Boost变换器,验证该文所提拓扑的正确性。

轻小型高可靠回收控制器设计与实现

针对某商用返回式卫星回收系统轻小型、高可靠的约束要求,设计了一种新型回收控制器产品,该装置基于微电子机械系统(Micro Electro Mechanical Systems,MEMS)过载传感器进行弹道过载测量,采用双核四进程“与或”逻辑实现冗余控制,基于容性释能半导体桥点火器起爆技术实现火工品管理。策划了针对返回过程的弹道过载触发验证试验,针对容性释能电容阵列放电特性,开展了容性释能系统不同初始电压、不同电容阵列容值以及不同回路阻值下的点火效能仿真分析。结果表明该回收控制器在轻小型约束下返回弹道过载感知精度达到±0.28 g,可实现开伞逻辑高可靠控制。半导体桥点火器容性释能电路优化参数为初始电压大于24 V、回路阻值小于0.3Ω、初始电容大于22μF,可实现点火器高可靠点火控制。该设计在不显著增加硬件基础上,大大降低产品质量,可为航空航天用同类电子系统设计提供参考。

基于电容传感器的CO_(2)气液两相流流型识别

为了实现碳捕捉与封存(CCS)管道内CO_(2)流量的精确测量,需要对CO_(2)的流型进行辨识。基于递归定量分析和模糊C均值(FCM)聚类算法,建立了CO_(2)气液两相流的流型识别模型。提出利用小波变换在多尺度下分析CO_(2)气液两相流电容信号递归图结构特性的方法,对各个尺度下的递归图分别进行定量分析,提取出不同流型下CO_(2)流动的电容传感器递归特征参数。采用FCM算法,对特征参数进行流型聚类,完成了对CO_(2)气液两相流的分层流、气泡流和雾状流3种流型的识别。

一种组合电极电容层析成像重建图像融合方法

提出一种基于电容层析成像(ECT)组合电极重建图像及层次聚类的ECT图像融合方法。首先,构建组合8电极及12电极ECT阵列传感器,研究2种组合传感器激励测量方案;其次,采用层次聚类法对两种组合电极ECT重建图像进行聚类,从而消除重建过程中产生的伪影;最后,将聚类后的2种图像再进行小波融合重建。实验结果表明,相较于原始重建图像,所提方法重建图像平均相对误差最小,最大降幅达到23.4%,平均相关系数最大,最大增幅达到10.56%。

考虑倒伏与粮仓容积的再生稻头季机收路径规划

为提升机具作业效率和质量、减少土壤碾压等,该研究提出一种同时考虑作物倒伏状态、碾压面积和收获机粮仓容积的路径规划算法(harvester grain bin capacitated arc routing problem,HGBCARP)。该算法由作业信息处理模块和作业路径规划模块组成,作业信息处理模块将农田边界、卸粮点位置、作物倒伏方向及面积、位置等信息转化成可处理的数据形式并传输给路径规划模块,然后由路径规划模块进行作业行方向划分、作业行遍历顺序寻优、转弯方式生成、碾压面积计算等,最终得到最优路径规划结果。采用改进遗传算法,分别以3种再生稻收获机、2种传统水稻收获机和3种不同田块为对象,以行驶路径长度、碾压面积、收获粮食量为评价指标,采用回转式收获路径和HGBCARP式收获路径规划开展对比试验。结果表明,HGBCARP式比回转式收获路径碾压面积每公顷减少11.79%~27.20%,可使倒伏的机收头季稻增收1.64%~1.95%;同时在3种不同田块条件下进行仿真试验,HGBCARP式比回转式收获路径可使碾压面积每公顷减少7.25%~20.09%。使用电动无人履带式底盘对不同收获路径进行田间模拟收获试验,HGBCARP式收获路径与传统牛耕往复式及回转式收获路径相比,碾压面积减少约11.21%~28.03%,在路径长度减少约6.81%~23.46%,验证了HGBCARP式路径规划方法的有效性,研究结果可为智能化作业路径规划研究提供参考。