金红石TiO_2中本征缺陷扩散性质的第一性原理计算

基于密度泛函理论的爬坡弹性带方法,对金红石相二氧化钛晶体中钛间隙、钛空位、氧间隙、氧空位4种本征缺陷的扩散特征进行了研究.对比4种本征缺陷在晶格内部沿不同扩散路径的过渡态势垒后发现,缺陷扩散过程呈现出明显的各向异性.其中,钛间隙和氧间隙沿[001]方向具有最小的扩散势垒路径,激活能分别为0.505 eV和0.859 eV;氧空位和钛空位的势垒最小的扩散路径分别沿[110]方向和[111]方向,激活能分别为0.735 eV和2.375 eV.     

An improved memristor model for brain-inspired computing

Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors' performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.