Memristors are crucial in computing due to their potential for miniaturization, energy efficiency, and rapid switching, making them particularly suited for advanced applications such as neuromorphic computing and in-memory operations. However, these tasks often require different operational modes—volatile or nonvolatile. This study introduces a forming-free Ag/FeO_x/FeWO_x/Pt nanocomposite memristor capable of both operational modes, achieved through compliance current (CC) adjustment and structural engineering. Volatile switching occurs at low CC levels (<500 μA), transitioning to nonvolatile at higher levels (mA). Operating at extremely low voltages (<0.2 V), this memristor exhibits excellent uniformity, data retention, and multilevel switching, making it highly suitable for high-density data storage. The memristor successfully mimics fundamental biological synapse functions, exhibiting potentiation, depression, and spike-rate dependent plasticity (SRDP). It effectively emulates transitions from short-term memory (STM) to long-term memory (LTM) by varying pulse characteristics. Leveraging its volatile switching and STM features, the memristor proves ideal for reservoir computing (RC), where it can emulate dynamic reservoirs for sequence data classification. A physical RC system, implemented using digits 0 to 9, achieved a recognition rate of 93.4% in off-chip training with a deep neural network (DNN), confirming the memristor's effectiveness. Overall, the dual-mode switching capability of the Ag/FeO_x/FeWO_x/Pt memristor enhances its potential for AI applications, particularly in temporal and sequential data processing.

中文翻译：

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用于神经形态和储层计算的动态 FeOx/FeWOx 纳米复合忆阻器


忆阻器在计算中至关重要，因为它们具有小型化、能效和快速开关的潜力，使其特别适合神经形态计算和内存操作等高级应用。但是，这些任务通常需要不同的操作模式 - 易失性或非易失性。本研究介绍了一种无成型的 Ag/FeO_x/FeWO_x/Pt 纳米复合忆阻器，能够通过顺从电流 （CC） 调整和结构工程实现两种操作模式。易失性切换发生在低 CC 电平 （<500 μA） 下，在较高电平 （mA） 下转变为非易失性。该忆阻器在极低电压 （<0.2 V） 下工作，具有出色的均匀性、数据保持和多级开关，使其非常适合高密度数据存储。忆阻器成功模拟了基本的生物突触功能，表现出增强、抑制和尖峰速率依赖性可塑性 （SRDP）。它通过改变脉冲特性来有效地模拟从短期记忆 （STM） 到长期记忆 （LTM） 的转变。利用其易失性开关和 STM 功能，忆阻器被证明是储层计算 （RC） 的理想选择，它可以模拟动态储层以进行序列数据分类。使用数字 0 到 9 实现的物理 RC 系统在使用深度神经网络 （DNN） 的片外训练中实现了 93.4% 的识别率，证实了忆阻器的有效性。总体而言，Ag/FeO_x/FeWO_x/Pt 忆阻器的双模式开关能力增强了其在 AI 应用中的潜力，尤其是在时间和顺序数据处理方面。