Dynamic FeO_<i>x</i>/FeWO_<i>x</i> nanocomposite memristor for neuromorphic and reservoir computing

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_<i>x</i>/FeWO_<i>x</i>/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_<i>x</i>/FeWO_<i>x</i>/Pt memristor enhances its potential for AI applications, particularly in temporal and sequential data processing.