“…This kind of memory switches between two resistance states called HRS (high-resistance state) and LRS (low-resistance state) . Resistive switching (RS) depends on the switching layer and electrode materials. , The main RS mechanism consists of forming a conductive filament within the insulator layer that connects the bottom electrode (BE) to the top electrode (TE). , The conductive filament originates from an active top electrode (such as Ag, Cu) or emerged oxygen vacancies from the switching layer. − RRAMs with attractive properties such as multilevel switching, fast programming, and electronic synapse are promising candidates to replace traditional storage devices. , Furthermore, the conventional von Neumann architecture of computers is not efficient for significant data processing due to high power consumption and data traffic jam between cores and memory units. ,, According to this issue, brain-inspired computing as neuromorphic computing offers worth noting characteristics for energy- and time-efficient processing. , Brain-inspired algorithms such as artificial neural networks (ANN) have brought about a significant revolution in machine learning. , However, in these algorithms, the core and memory unit are separated, which has caused several issues with a considerable amount of data. This separation lowers the processing data and raises the processing energy consumption.…”