Enhanced Switching Properties in TaO_x Memristors Using Diffusion Limiting Layer for Synaptic Learning

To move towards a new generation powerful computing system, brain-inspired neuromorphic computing is expected to transform the architecture of the conventional computer, where memristors are considered to be potential solutions for synapses part. We propose and demonstrate a novel approach to achieve remarkable improvement of analog switching linearity in TaN/Ta/TaO_x/Al₂O₃/Pt/Si memristors by varying Al₂O₃ layer thickness. Presence of the Al₂O₃ layer is confirmed from the Auger Electron Spectroscopy study. Good analog switching ratio of about 100× and superior switching uniformity are observed for the 1 nm Al₂O₃ based device. Multilevel capability of the memristive devices is also explored for prospective use as a synapse. More than 10⁴ and 4×10⁴ cycles nondegradable dc and ac endurances, respectively, alongwith 10⁴ second retention are achieved for the optimized device. Improved linearities of 2.41 and -2.77 for potentiation and depression, respectively are obtained for such 1 nm Al₂O₃-based devices. The property of gradual resistance changed by pulse amplitudes confirms that the TaO_x memristors can be potentially used as an electronic synapse.