Dynamic NiO_x-based memristors for edge computing

Resistive random-access memory (RRAM) devices, which leverage resistance state modulation for data storage and retrieval, have garnered considerable interest due to their high-speed performance, low energy consumption, and exceptional scalability. These advanced characteristics make RRAM devices highly suitable for neuromorphic computing, a rapidly emerging paradigm aimed at developing autonomous systems capable of real-time learning, adaptation, and environmental interaction. In neuromorphic architecture, RRAM is increasingly viewed as a promising candidate for computing-in-memory. This research investigates the realization of neuromorphic systems by fine-tuning conductance using the DC sweep and electrical pulse on ITO/NiO_X/n^{+ +} Si stacked RRAM devices, based on their distinct resistance states. Key properties crucial for neuromorphic functionality, including Spike Amplitude-Dependent Plasticity (SADP), Spike Number-Dependent Plasticity (SNDP), Spike Duration-Dependent Plasticity (SDDP), were systematically examined. The potentiation and depression dynamics, along with the long-term plasticity characteristics demonstrated by the RRAM device, underscore its promising potential for neuromorphic applications. The demonstrated multi-state operational capability highlights the potential of the device for high-efficiency data processing and storage, which are essential for advanced edge computing architectures.