Exploitation of temporal dynamics and synaptic plasticity in multilayered ITO/ZnO/IGZO/ZnO/ITO memristor for energy-efficient reservoir computing

As the demand for advanced computational systems capable of handling large data volumes rises, nano-electronic devices, such as memristors, are being developed for efficient data processing, especially in reservoir computing (RC). RC enables the processing of temporal information with minimal training costs, making it a promising approach for neuromorphic computing. However, current memristor devices often suffer from limitations in dynamic conductance and temporal behavior, which affects their performance in these applications. In this study, we present a multilayered indium-tin-oxide (ITO)/ZnO/indium–gallium–zinc oxide (IGZO)/ZnO/ITO memristor fabricated via radiofrequency sputtering to explore its filamentary and nonfilamentary resistive switching (RS) characteristics. High-resolution transmission electron microscopy confirmed the polycrystalline structure of the ZnO/IGZO/ZnO active layer. Dual-switching modes were demonstrated by controlling the current compliance (I_CC). In the filamentary mode, the memristor exhibited a large memory window (10³), low-operating voltages (± 2 V), excellent cycle-to-cycle stability, and multilevel switching with controlled reset-stop voltages, making it suitable for high-density memory applications. Nonfilamentary switching demonstrated stable on/off ratios above 10, endurance up to 10² cycles, and retention suited for short-term memory. Key synaptic behaviors, such as paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), and spike-rate dependent plasticity (SRDP) were successfully emulated by modulating pulse amplitude, width, and interval. Experience-dependent plasticity (EDP) was also demonstrated, further replicating biological synaptic functions. These temporal properties were utilized to develop a 4-bit reservoir computing system with 16 distinct conductance states, enabling efficient information encoding. For image recognition tasks, convolutional neural network (CNN) simulations achieved a high accuracy of 98.45% after 25 training epochs, outperforming the accuracy achieved following artificial neural network (ANN) simulations (87.79%). These findings demonstrate that the multilayered memristor exhibits high performance in neuromorphic systems, particularly for complex pattern recognition tasks, such as digit and letter classification.