A Dynamical Compact Model of Diffusive and Drift Memristors for Neuromorphic Computing
A Dynamical Compact Model of Diffusive and Drift Memristors for Neuromorphic Computing
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DOI：
10.1002/aelm.202100696
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发表时间：
2021-10
影响因子：
6.2
通讯作者：
Ye Zhuo;Rivu Midya;Wenhao Song;Zhongrui Wang;Shiva Asapu;Mingyi Rao;Peng Lin;Hao Jiang;Qiangfei Xi
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文献类型：
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作者：
Ye Zhuo;Rivu Midya;Wenhao Song;Zhongrui Wang;Shiva Asapu;Mingyi Rao;Peng Lin;Hao Jiang;Qiangfei Xi
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，而且还利用开关动力学进行计算，这需要忆阻器件的紧凑动力学模型。在这项工作中，提出了一个通用的模型来模拟扩散和漂移忆阻器具有相同的一组方程，这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力学特性，难以有效地建模。收集了SiO2：Ag扩散忆阻器的统计测量数据集，以验证通用模型的有效性。作为一个应用示例，尖峰时间依赖的可塑性被证明与人工突触组成的扩散忆阻器和漂移忆阻器，都与这个全面的紧凑模型建模。
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.