High-stability resistive switching memristor with high-retention memory window response for brain-inspired computing

In this work, we demonstrate the stable resistive switching (RS) and interesting neuromorphic features of Ag/NiHfO2/P**-Si memristors. This unique technique stacks a Ni-HfO2 resistive switching (RS) layer on top of a P**-Si layer, considerably improving the stability, switching efficiency, and synaptic characteristics of memristors. A detailed physical model describes the RS filamentary process, which involves Ag+ ions migrating and forming electrical filaments with applied voltage, shifting the memristor consistent response from low-resistance and high-resistance phases. The memristor maintains consistent RS properties for 96 h with low deterioration, because of the strong Ni-HfO2 layer that improves switching stability. The memristor chip performs successfully in both voltage sweeping and pulse mode processes. The pulse-mode endurance results show that the lowresistance state (LRS) and high-resistance state (HRS) are stable after 100 cycles, with SET and RESET reaction times of 960 and 1636 ms, correspondingly. These findings show the memristors capacity for quick, energyefficient switching. Furthermore, the memristor shows synaptic action, which resembles biological activities for example short-term (STP) and long-term plasticity (LTP). The conductivity regulation, like neurotransmitter release and synaptic weight correction, is accomplished by ion migration during voltage pulses. Also, the pairedpulse facilitation (PPF) reveals the memristors capacity to simulate synaptic activities, with a PPF index of 130 %. The variations in pulse height and width indicate the progressive change from STP to LTP. Thus, the new device design indicates potential in neuromorphic computing, combining robust resistive switching with sophisticated synaptic properties to simulate essential brain activities such as memory retention and adaptation. These findings indicate that Ag/Ni-HfO2/P**-Si memristors have potential consistent switching efficiency and synaptic abilities serve as promising contenders for future artificial intelligence and computer hardware applications.