Due to their high data-storage capability, oxidebased memristors with controllable conductance properties have attracted great interest in electronic devices for high integration density and neuromorphic synapses. However, high switching uniformity and controllable conductance of memristors during the conversion from a low (ON-state) to a high resistance state (OFFstate) have become essential for their implementation in neural networks. In this study, we fabricate a Pt/HfO 2 /HfAlO x /TiN memristor incorporating atomic-layer-deposited HfO 2 /HfAlO x high-k dielectric thin films as the active material to achieve excellent resistive switching performance with negligible parameter dispersion, multilevel conductance, and neuromorphic synapses for artificial intelligence (AI) systems. This two-terminal memristor exhibits a forming-free switching behavior with outstanding direct current endurance cycles (10 3 ), a high current ON/OFF ratio of >130, stable retention (10 4 s), and multilevel ON-and OFF-state, respectively. Also, memristor conductance/resistance could be modulated through current limits in the set-switching and stop voltage during the reset process, which is useful to acquire a trustworthy analogue switching conduct to mimic the biological neuromorphic synapses. The diverse features of synapses, such as potentiation, depression, spike-rate-dependent plasticity, paired-pulsed facilitation, and spike-time-dependent plasticity, are successfully mimicked in the Pt/HfO 2 /HfAlO x /TiN memristor. Furthermore, the experimental potentiation and depression data are employed for image processing of 28 × 28 pixels comprising 200 synapses. In the Modified National Institute of Standards and Technology database (MNIST), handwritten numbers can be successfully trained to recognize 6000 input images with a training accuracy of about 80%. This Hf-Al-O alloy-based memristor may enable high-density storage memory and realize controllable resistance/weight alteration as a neuromorphic synapse for AI systems.

Memristors, owing to their uncomplicated structure and resemblance to biological synapses, are predicted to see increased usage in the domain of artificial intelligence. Additionally, to augment the capacity for multilayer data storage in high-density memory applications, meticulous regulation of quantized conduction with an extremely low transition energy is required. In this work, an a-HfSiOx-based memristor was grown through atomic layer deposition (ALD) and investigated for its electrical and biological properties for use in multilevel switching memory and neuromorphic computing systems. The crystal structure and chemical distribution of the HfSiOx/TaN layers were analyzed using X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS), respectively. The Pt/a-HfSiOx/TaN memristor was confirmed by transmission electron microscopy (TEM) and showed analog bipolar switching behavior with high endurance stability (1000 cycles), long data retention performance (104 s), and uniform voltage distribution. Its multilevel capability was demonstrated by restricting current compliance (CC) and stopping the reset voltage. The memristor exhibited synaptic properties, such as short-term plasticity, excitatory postsynaptic current (EPSC), spiking-rate-dependent plasticity (SRDP), post-tetanic potentiation (PTP), and paired-pulse facilitation (PPF). Furthermore, it demonstrated 94.6% pattern accuracy in neural network simulations. Thus, a-HfSiOx-based memristors have great potential for use in multilevel memory and neuromorphic computing systems. Graphical Abstract

In this work, the sputtered deposited WOx/TaOx switching layer has been studied for resistive random-access memory (RRAM) devices. Gradual SET and RESET behaviors with reliable device-to-device variability were obtained with DC voltage sweep cycling without an electroforming process. The memristor shows uniform switching characteristics, low switching voltages, and a high RON/ROFF ratio (~102). The transition from short-term plasticity (STP) to long-term potentiation (LTP) can be observed by increasing the pulse amplitude and number. Spike-rate-dependent plasticity (SRDP) and paired-pulse facilitation (PPF) learning processes were successfully emulated by sequential pulse trains. By reducing the pulse interval, the synaptic weight change increases due to the residual oxygen vacancy near the conductive filaments (CFs). This work explores mimicking the biological synaptic behavior and further development for next-generation neuromorphic applications.

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In this work, the resistive switching behavior of bilayer ZnO/Al2O3-based resistive-switching random access memory (RRAM) devices is demonstrated. The polycrystalline nature of the ZnO layer confirms the grain boundary, which helps easy oxygen ion diffusion. Multilevel resistance states were modulated under DC bias by varying the current compliance from 0.1 mA to 0.8 mA, the SET operations where the low resistance state of the memristor device was reduced from 25 kΩ to 2.4 kΩ. The presence of Al2O3 acts as a redox layer and facilitates oxygen vacancy exchange that demonstrates stable gradual conductance change. Stepwise disruption of conductive filaments was monitored depending on the slow DC voltage sweep rate. This is attributed to the atomic scale modulation of oxygen vacancies with four distinct reproducible quantized conductance states, which shows multilevel data storage capability. Moreover, several crucial synaptic properties such as potentiation/depression under identical presynaptic pulses and the spike-rate-dependent plasticity were implemented on ITO/ZnO/Al2O3/TaN memristor. The postsynaptic current change was monitored defining the long-term potentiation by increasing the presynaptic stimulus frequency from 5 Hz to 100 Hz. Moreover, the repetitive pulse voltage stimulation transformed the short-term plasticity to long-term plasticity during spike-number-dependent plasticity.