We thoroughly investigate the performance of the Dynamic Memdiode Model (DMM) when used for simulating the synaptic weights in large RRAM-based cross-point arrays (CPA) intended for neuromorphic computing. The DMM is in line with Prof. Chua’s memristive devices theory, in which the hysteresis phenomenon in electroformed metal-insulator-metal structures is represented by means of two coupled equations: one equation for the current-voltage characteristic of the device based on an extension of the quantum point-contact (QPC) model for dielectric breakdown and a second equation for the memory state, responsible for keeping track of the previous history of the device. By considering ex-situ training of the CPA aimed at classifying the handwritten characters of the MNIST database, we evaluate the performance of a Write-Verify iterative scheme for setting the crosspoint conductances to their target values. The total programming time, the programming error, and the inference accuracy obtained with such writing scheme are investigated in depth. The role played by parasitic components such as the line resistance as well as some CPA’s particular features like the dynamical range of the memdiodes are discussed. The interrelationship between the frequency and amplitude values of the write pulses is explored in detail. In addition, the effect of the resistance shift for the case of a CPA programmed with no errors is studied for a variety of input signals, providing a design guideline for selecting the appropriate pulse’s amplitude and frequency.

我们彻底研究了动态内存二极管模型 (DMM) 在用于模拟用于神经形态计算的基于 RRAM 的大型交叉点阵列 (CPA) 中的突触权重时的性能。该数字万用表符合蔡教授的忆阻器件理论，其中电铸金属-绝缘体-金属结构中的磁滞现象用两个耦合方程表示：一个方程基于扩展的器件电流-电压特性方程介电击穿的量子点接触 (QPC) 模型和存储状态的第二个方程，负责跟踪设备的先前历史记录。通过考虑异地CPA 的训练旨在对 MNIST 数据库的手写字符进行分类，我们评估了 Write-Verify 迭代方案的性能，用于将交叉点电导设置为其目标值。深入研究了使用这种编写方案获得的总编程时间、编程错误和推理精度。讨论了诸如线路电阻之类的寄生元件所起的作用以及一些 CPA 的特殊功能（如 memdiodes 的动态范围）。详细探讨了写入脉冲的频率和幅度值之间的相互关系。此外，针对各种输入信号，研究了在 CPA 编程没有错误的情况下电阻偏移的影响，