Enhanced Switching Properties in TaOx 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
/Al
₂
O
₃
/Pt/Si memristors by varying Al
₂
O
₃
layer thickness. Presence of the Al
₂
O
₃
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 Al
₂
O
₃
based device. Multilevel capability of the memristive devices is also explored for prospective use as a synapse. More than 10
⁴
and 4 × 10
⁴
cycles nondegradable dc and ac endurances, respectively, along with 10
⁴
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 Al
₂
O
₃
-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.