A Dynamical Compact Model of Diffusive and Drift Memristors for Neuromorphic Computing,Advanced Electronic Materials

当前位置： X-MOL 学术 › Adv. Electron. Mater. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

A Dynamical Compact Model of Diffusive and Drift Memristors for Neuromorphic Computing
Advanced Electronic Materials ( IF 5.3 ) Pub Date : 2021-10-13 , DOI: 10.1002/aelm.202100696
Ye Zhuo ₁ , Rivu Midya ₂ , Wenhao Song ₁ , Zhongrui Wang ₃ , Shiva Asapu ₂ , Mingyi Rao ₂ , Peng Lin ₂ , Hao Jiang ₂ , Qiangfei Xia ₂ , R. Stanley Williams ₃ , J. Joshua Yang _{1,

2}

Affiliation

Different from nonvolatile memory applications, neuromorphic computing applications utilize not only the static conductance states but also the switching dynamics for computing, which calls for compact dynamical models of memristive devices. In this work, a generalized model to simulate diffusive and drift memristors with the same set of equations is presented, which have been used to reproduce experimental results faithfully. The diffusive memristor is chosen as the basis for the generalized model because it possesses complex dynamical properties that are difficult to model efficiently. A data set from statistical measurements on SiO₂:Ag diffusive memristors is collected to verify the validity of the general model. As an application example, spike-timing-dependent plasticity is demonstrated with an artificial synapse consisting of a diffusive memristor and a drift memristor, both modeled with this comprehensive compact model.

中文翻译：

用于神经形态计算的扩散和漂移忆阻器的动态紧凑模型

与非易失性存储器应用不同，神经形态计算应用不仅利用静态电导状态，还利用开关动态进行计算，这需要忆阻器件的紧凑动态模型。在这项工作中，提出了一个通用模型来模拟具有相同方程组的扩散和漂移忆阻器，该模型已被用来忠实地再现实验结果。扩散忆阻器被选为广义模型的基础，因为它具有复杂的动态特性，难以有效建模。来自 SiO _{2统计测量的数据集}：收集Ag扩散忆阻器，验证通用模型的有效性。作为一个应用示例，使用由扩散忆阻器和漂移忆阻器组成的人工突触证明了尖峰时间相关的可塑性，两者都使用这种综合紧凑模型进行建模。

更新日期：2021-10-13

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11