PECVD SiO2 薄膜内应力研究

研究了等离子体增强化学气相淀积(PWCVD)法生长SiO2薄膜的内应力.借助XP-2型台阶仪和椭偏仪测量计算了SiO2薄膜的内应力,通过改变薄膜淀积时的工艺条件,如淀积温度、气体流量、反应功率、腔体压力等,分析了这些参数对SiO2薄膜内应力的影响.同时讨论了内应力产生的原因以及随工艺条件变化的机理,对工艺条件的优化有一定参考价值.     

SiN介质薄膜内应力的实验研究

研究了低压化学气相淀积Sin(LPCVD)介质薄膜的内应力,采用XP-2型台阶仪测量了SiN介质薄膜的内应力,通过改变薄膜淀积时的工艺参数,观察了反应气体流量比、淀积温度、反应室压力等因素对SiN薄膜内应力的影响.讨论了应力产生的原因以及随工艺条件变化的机理,通过工艺条件的合理选择逐步优化工艺.     

Complete Synchronization of Coupled Hindmarsh-Rose Neurons with Ring Structure

Complete synchronization of coupled Hindmarsh-Rose (HR) neurons with a ring structure is studied. The criterion for complete synchronization of coupled neurons with the ring structure is obtained through the stability analysis of the linearized synchronization error system. Numerical simulation is given to test the criterion for a system with three coupled HR neurons.     

Hindmarsh-Rose神经元的随机自共振和噪声影响下的同步

研究了噪声对Hindmarsh-Rose(HR)神经元随机自共振和同步的影响。将高斯白噪声加入HR神经元模型的膜电位上，把外界直流电作为分岔参数，分别考虑参数处于Hopf分岔前、Hopf分岔附近和Hopf分岔后时，噪声影响下的随机自共振现象。两个未经耦合的全同HR神经元，如果接受相同的噪声激励，只要噪声强度高于某临界值，就能达到完全同步。进一步，噪声能够增强弱耦合神经元的完全同步。数值结果表明簇放电的神经元比峰放电的神经元更容易被噪声诱导而达到完全同步，耦合也增强了神经元对噪声激励的灵敏度。     

Rhythm Synchronization of Coupled Neurons with Temporal Coding Scheme

Encoding information by firing patterns is one of the basic neural functions, and synchronization is important collective behaviour of a group of coupled neurons. Taking account of two schemes for encoding information （that is, rate coding and temporal coding）, rhythm synchronization of coupled neurons is studied. There are two types of rhythm synchronization of neurons： spike and burst synchronizations. Firstly, it is shown that the spike synchronization is equivalent to the phase synchronization for coupled neurons. Secondly, the similarity function of the slow variables of neurons, which have relevant to the bursting process, is proposed to judge the burst synchronization. It is also found that the burst synchronization can be achieved more easily than the spike synchronization, whatever the firing patterns of the neurons are. Hence the temporal encoding scheme, which is closely related to both the spike and burst synchronizations, is more comprehensive than the rate coding scheme in essence.     

化学突触耦合神经元的簇放电同步

本文研究了经化学突触耦合的两个神经元的簇放电同步以及耦合后神经元的簇放电动力学性质.根据簇相位的定义,通过计算得到兴奋性耦合导致两个神经元达到同相簇放电同步,而抑制性耦合则使得两个神经元反相同步产生簇放电.本文给出了衡量单个神经元簇动力学的指标-宽度因子,根据此指标将簇放电模式分类为短簇和长簇两种,并且讨论了不同簇放电模式以及耦合方式对于耦合后神经元簇动力学性质的影响.结果表明兴奋性耦合有利于簇放电的整合,短簇的放电模式对于耦合作用具有鲁棒性.这一结果的研究对于将来神经实验中识别簇放电同步具有指导意义.     

Comparison of Synchronization Ability of Four Types of Regular Coupled Networks

We investigate the synchronization ability of four types of regular coupled networks. By introducing the proper error variables and Lyapunov functions, we turn the stability of synchronization manifold into that of null solution of error equations, further, into the negative definiteness of some symmetric matrices, thus we get the sufficient synchronization stability conditions. To test the valid of the results, we take the Chua's circuit as an example. Although the theoretical synchronization thresholds appear to be very conservative, they provide new insights about the influence of topology and scale of networks on synchronization, and that the theoretical results and our numerical simulations are consistent.     

Phase synchronization and its bursting neurons： theoretical transition in two coupled and numerical analysis

It is crucially important to study different synchronous regimes in coupled neurons because different regimes may correspond to different cognitive and pathological states. In this paper, phase synchronization and its transitions are discussed by means of theoretical and numerical analyses. In two coupled modified Morris-Lecar neurons with a gap junction, we show that the occurrence of phase synchronization can be investigated from the dynamics of phase equation, and the analytical synchronization condition is derived. By defining the phase of spike and burst, the transitions from burst synchronization to spike synchronization and then toward nearly complete synchronization can be identified by bifurcation diagrams, the mean frequency difference and time series of neurons. The simulation results suggest that the synchronization of bursting activity is a multi-time-scale phenomenon and the phase synchronization deduced by the phase equation is actually spike synchronization.