Neuromorphic Synapses with High Switching Uniformity and Multilevel Memory Storage Enabled through a Hf-Al-O Alloy for Artificial Intelligence

Due to their high data-storage capability, oxide-based 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 (OFF-state) have become essential for their implementation in neural networks. In this study, we fabricate a Pt/HfO₂/HfAlO_x/TiN memristor incorporating atomic-layer-deposited HfO₂/HfAlO_xhigh-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 (10³), a high current ON/OFF ratio of >130, stable retention (10⁴s), 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/HfO₂/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.