金刚石基底上氮化硼薄膜的场发射特性研究

 利用射频磁控溅射方法,在金刚石膜上沉积了氮化硼薄膜。红外光谱分析表明,氮化硼薄膜的结构为六角氮化硼。在超高真空系统中测量了样品的场发射特性,沉积在金刚石膜上的氮化硼薄膜的阈值电场为12 V/μm,最大发射电流密度为272 μA/cm²。并且沉积在金刚石膜上的氮化硼薄膜的场发射特性明显优于金刚石薄膜本身的场发射特性。这说明,氮化硼薄膜可以有效地改善金刚石膜的场发射特性。场发射Fowler-Nordheim（F-N）曲线表明,电子发射是通过遂穿表面势垒完成的。     

外延法生长金刚石薄膜场发射特性研究

研究了外延法生长金刚石薄膜的场发射特性.金刚石薄膜用热丝CVD法生长,甲烷与氢气比例为2.5%,生长于事先电泳沉积在硅衬底的金刚石微晶上.实验数据的计算结果表明：金刚石薄膜的阈值电压为1.8 V/μm,有效功函数降低为纯金刚石颗粒的0.11倍.通过SEM、XRD和Raman等手段表征了金刚石薄膜的结构,并对其场发射机制作出理论分析.     

非晶金刚石薄膜的场致电子发射性能研究

利用真空磁过滤弧沉积技术制备出一种高sp³含量的非晶碳膜———非晶金刚石薄膜，并对这种非晶金刚石薄膜的场电子发射特性及其发射机理进行了研究．实验结果表明，在阈值电场低于20V/μm情况下，得到的场发射电流达20—40μA，薄膜的电子发射行为符合Fowler-Nordheim场发射理论．研究表明，这种非晶金刚石薄膜具有负的电子亲合势和较小的有效功函数以及相对较低的禁带宽度 关键词：     

金刚石锥阵列的无掩膜刻蚀制备及其形貌演变机制

为获得具有优良场发射性能的金刚石锥阵列,利用偏压热灯丝化学气相沉积系统分别在高质量大颗粒金刚石厚膜与纳米金刚石薄膜上进行了无掩膜刻蚀研究,系统比较了高质量大颗粒金刚石厚膜与纳米金刚石薄膜的刻蚀特性,制备了大面积均匀金刚石锥阵列和高长径比(20∶1)金刚石纳米线阵列,探讨了金刚石锥的刻蚀形成机理。     

单颗粒CVD金刚石的场发射

利用改造的扫描电子显微镜(SEM)设备，在SEM腔体中利用钨(W)探针测试了单颗粒金刚石的I-V与场发射特性，结果表明结晶良好的金刚石的I-V特性服从欧姆定律，而孤立的菜花状金刚石颗粒(cauliflower-like diamond)的I-V特性基本符合Pool-Frenkel输运特性.场发射特性表明，结晶良好的金刚石薄膜基本没有场发射，而孤立的菜花状的金刚石颗粒具有一定的场发射.CVD金刚石的场发射过程中，缺陷对电子的输运起主导作用.     

硅基籽晶上化学气相沉积金刚石薄膜及其场发射特性

通过控制电泳沉积(EPD)时间,在硅基片上沉积不同密度的金刚石籽晶。再用热丝化学气相沉积(HFCVD)设备,在硅基籽晶上合成多晶金刚石薄膜。薄膜中通常含有非金刚石相碳成分。用扫描电子显微镜(SEM)和Raman光谱对样品的表面形貌和成分进行了表征,测量了样品的场发射特性。比较并分析了样品的表面形貌和非金刚石成分上的差异对金刚石薄膜场发射特性的影响。     

金刚石薄膜场发射进展

评述了国际上金刚石薄膜场发射的研究进展和存在的问题，主要包括金刚石薄膜场发射现象，场发射的可能机制和改善场发射的措施。     

类球状微米金刚石聚晶膜场发射的稳定性

在覆盖金属钛层的陶瓷上,利用微波等离子体化学气相沉积(MPCVD)法制备出类球状微米金刚石聚晶膜。通过二极管结构测试了聚晶膜的场致电子发射特性,利用扫描电子显微镜、拉曼光谱、XRD分析了场发射前后薄膜的结构和表面形貌的变化。发现在高场、大电流密度的场发射中,对类球状微米金刚石聚晶薄膜中的金刚石聚晶颗粒影响很小,而对金刚石聚晶颗粒间的非晶碳层影响很大。对类球状微米金刚石聚晶变化机理进行了研究。     

纳米金刚石的变温场发射

研究了温度变化对沉积在钛基底上的纳米金刚石的场发射特性的影响,发现纳米金刚石场发射电流随温度和电场的升高而增大,场发射特性偏离了传统的Fowler-Nordheim理论,场发射电流的稳定性基本没有变化.分析了场发射电流增大的机理,表明是由于纳米金刚石的尺度效应以及外电场下金刚石产生了大量的热载流子共同作用的结果.研究还表明基底钛在温度升高到一定程度后,在外加电场下会有较大的电流产生,对场发射造成较大的影响,表明基底钛具有一定的温度敏感性和电压敏感性. 关键词： 场发射 纳米金刚石 尺度效应 热载流子     

纳米金刚石的变温场发射

研究了温度变化对沉积在钛基底上的纳米金刚石的场发射特性的影响,发现纳米金刚石场发射电流随温度和电场的升高而增大,场发射特性偏离了传统的Fowler-Nordheim理论,场发射电流的稳定性基本没有变化.分析了场发射电流增大的机理,表明是由于纳米金刚石的尺度效应以及外电场下金刚石产生了大量的热载流子共同作用的结果.研究还表明基底钛在温度升高到一定程度后,在外加电场下会有较大的电流产生,对场发射造成较大的影响,表明基底钛具有一定的温度敏感性和电压敏感性.     

薄膜场发射开启电场的小波神经网络预测模型研究

关键词：     

Mean Field Analysis of Large-Scale Interacting Populations of Stochastic Conductance-Based Spiking Neurons Using the Klimontovich Method

We investigate the dynamics of large-scale interacting neural populations, composed of conductance based, spiking model neurons with modifiable synaptic connection strengths, which are possibly also subjected to external noisy currents. The network dynamics is controlled by a set of neural population probability distributions (PPD) which are constructed along the same lines as in the Klimontovich approach to the kinetic theory of plasmas. An exact non-closed, nonlinear, system of integro-partial differential equations is derived for the PPDs. As is customary, a closing procedure leads to a mean field limit. The equations we have obtained are of the same type as those which have been recently derived using rigorous techniques of probability theory. The numerical solutions of these so called McKean–Vlasov–Fokker–Planck equations, which are only valid in the limit of infinite size networks, actually shows that the statistical measures as obtained from PPDs are in good agreement with those obtained through direct integration of the stochastic dynamical system for large but finite size networks. Although numerical solutions have been obtained for networks of Fitzhugh–Nagumo model neurons, which are often used to approximate Hodgkin–Huxley model neurons, the theory can be readily applied to networks of general conductance-based model neurons of arbitrary dimension.     

Mean Field Theory of Self-Organizing Memristive Connectomes

Biological neuronal networks are characterized by nonlinear interactions and complex connectivity. Given the growing impetus to build neuromorphic computers, understanding physical devices that exhibit structures and functionalities similar to biological neural networks is an important step toward this goal. Self-organizing circuits of nanodevices are at the forefront of the research in neuromorphic computing, as their behavior mimics synaptic plasticity features of biological neuronal circuits. However, an effective theory to describe their behavior is lacking. This study provides for the first time an effective mean field theory for the emergent voltage-induced polymorphism of circuits of a nanowire connectome, showing that the behavior of these circuits can be explained by a low-dimensional dynamical equation. The equation can be derived from the microscopic dynamics of a single memristive junction in analytical form. The effective model is tested on experiments of nanowire networks and show that it fits both the potentiation and depression of these synapse-mimicking circuits. It is shown that this theory applies beyond the case of nanowire networks by formulating a general mean-field theory of conductance transitions in self-organizing memristive connectomes.     

Control modeling of ash wood drying using process neural networks

For the control and system identification problems of the deceleration phase of the ash wood drying process, we propose a deceleration phase modeling method of ash wood drying using process neural networks with double hidden layers. This method applies time-varying characteristics of process neural networks and the ability to extract time-space cumulative effects. The time-varying characteristics of wood drying deceleration phase modeling under time series background are directly incorporated into the model. By comparison with traditional neural network modeling results, we prove that the model of process neural networks has better control accuracy, providing an idea to solve control and nonlinear system identification problems under a time series background.     

场耦合忆阻神经网络的电活动

神经元之间除了突触耦合，还存在磁通耦合.因此在传统的神经元模型中引入磁通量，并研究场耦合下神经网络的放电活动具有实际意义.建立一个含场耦合的Hodgkin-Huxley忆阻神经网络，引入神经元节点之间的距离权重，用磁通量描述时变电磁场，采用磁控忆阻器实现膜电位和磁通量之间的耦合.探讨距离权重和系统大小对神经网络放电活动的影响.研究发现，随着权重增大，神经网络放电活动增强，且系统规模越大，诱导神经元兴奋性的权重阈值越大，系统大小不影响神经网络活性随距离权重变化的规律.在不同的权重值下，神经网络活性随系统大小变化的规律明显不同.研究表明，距离权重和系统大小对含场耦合的忆阻神经网络放电活动有重要影响，其中距离权重起主导作用.     

Field coupling-induced synchronization via a capacitor and inductor

Resistor-based voltage coupling is often used to realize complete synchronization between identical nonlinear circuits while phase synchronization is investigated between non-identical nonlinear circuits (periodic or chaotic oscillation). Indeed, the coupling resistor used to consume certain Joule heat and energy before reaching the synchronization target when continuous current passed across the coupling device. In this paper, capacitor and inductor is paralleled with one coupling resistor, respectively, and the coupling devices are used bridge connection between two LC hyperchaotic circuits for investigating synchronization problems. As a result, the coupling channel can be activated to propagate energy and balance the outputs voltage from the two circuits. The dimensionless dynamical equations are obtained by applying scale transformation on the circuit equations when field coupling is switched on. It is found that the threshold of coupling intensity for reaching synchronization and the power consumption of controller can be decreased when the coupling resistor is paralleled with on capacitor or inductor. The mechanism could be that involvement of coupling capacitor(or inductor) can trigger time-varying electric field (or magnetic field), and the energy flow of field coupling via coupling capacitor (or inductor) can contribute the exchange of energy in the coupled nonlinear circuits. It can give insights to investigate synchronization on chaotic systems, neural circuits and neural networks including synapse coupling and field coupling. Finally, the experimental results on circuits are also supplied for further verification.     

A Hopfield Neural Network Model in a Transverse Field

A Hopfield model with transverse fields that induce tunneling among neurons is presented. A set of equations for the order parameters describing retrieval and spin glass phases of Hopfield model in a transverse field are obtained without using the tedious replica method, and the memory storage capacity of networks is andysed as a function of the temperatureand transverse field.     

基于神经网络的翼型气动力计算和反设计方法

开展了机器学习在翼型气动力计算和反设计方法中的应用研究,实现了在更大翼型空间范围内,人工神经网络的训练和优化,建立了翼型气动力计算模型,和给定目标压力分布的翼型反设计优化模型.作为机器学习领域兴起的研究热点,人工神经网络的研究工作不断深入,有研究者尝试将其应用于流体力学的学科范畴内.文章实现人工神经网络在翼型计算领域中应用的方法如下:首先通过Parsec参数化方法,围绕基准翼型构造了一定翼型空间范围的翼型库,利用XFOIL进行数值模拟,搭建了和翼型库具有一一映射关系的流场信息库.通过训练和优化神经网络,实现了基于此模型的快速、高可信度的翼型气动力预测,以及新型的翼型优化设计方法.通过自动化编程实现样本库的批量生成,实现了不同翼型空间的样本量下,神经网络的训练和优化过程.实验结果表明,在机器学习领域中,基于神经网络的翼型反设计模型的精确性高度依赖于训练样本量的大小和覆盖范围.      

红外光谱评价内燃机油抗氧化性能的研究

红外光谱快速检测石油产品性能是近年来发展的新技术,目前国内外在该领域的研究仅限于测试燃料油性能,由于润滑油组成、结构复杂,红外光谱技术测试润滑油性能的研究还未见报道。文章研究了润滑油组成、结构的红外光谱特征,提出了根据内燃机油组成、结构对抗氧化性能的贡献来提取其光谱信息的技术路线。结合BP神经网络和自组织神经网络的优点,发展了量化自组织神经网络数学模型,该数学模型具有自组织神经网络的定性聚类功能和BP神经网络的定量分析功能,与BP神经网络相比较,量化自组织神经网络具有更好的鲁棒性,测试结果优于BP神经网络,该论文的研究为润滑油性能的快速检测提供了一种新的技术手段。     

基于CNN的红外图像边缘检测算法的FPGA实现

提出了一种以现场可编程门阵列为硬件处理器实现基于细胞神经网络的红外图像边缘检测方法.首先利用simulink的算法行为特性搭建红外图像输入模块,获得相关的红外图像头信息并对红外图像像素值范围进行相应变化,然后根据细胞神经网络模板所创建的查找表设计单个细胞元软核,再利用细胞神经网络阵列的规则性和互联的局域性,将单个细胞元软核扩展成细胞神经网络阵列.最后采用modelsim将细胞神经网络阵列与红外图像输入、输出模块相关联,从而达到实时处理的效果.实验结果表明:基于现场可编程门阵列为硬件处理器平台实现的细胞神经网络对红外图像进行边缘检测取得了较好的效果,且与MATLAB软件仿真的结果进行对比得出两者只有极其微小的差别.在Xilinx公司Virtex-6系列的现场可编程门阵列平台上,综合后占用极少资源的情况下得到142.693 MHz的最高频率,并且达到了2.378 Mpixels/sec处理速度.