Complementary Digital and Analog Resistive Switching Based on AlOx Monolayer Memristors for Mixed-Precision

Neuromorphic computing is a potential can-didate to break the von Neumann bottleneck, in which the trade-off between computational precision and energy consumption remains challenging. In this brief, a com-plementary memristor cell based on monolayer AlOx film is proposed, whose two components exhibit analog (N-Si/AlOx/TiN) and digital (N-Si/AlOx/Cu) resistive switch-ing behaviors. Typical synapse behaviors including spike time-dependent plasticity (STDP) and long-term potentia-tion/depression (LTP/LTD) are emulated in different work-ing modes. Moreover, with the high/low resistive state (HRS/LRS) in the digital component, the cell shows low/high accuracies with different power consumption, which are-82% and-94% in the Modified National Institute of Standards and Technology (MNIST) recognition task. Our findings may provide the potential to develop energy-efficient, feasible integrating, and mixed-precision neuro-morphic computing based on AlOx monolayer memristors.

Automated summary

A complementary memristor cell based on monolayer AlOx film is proposed, with analog and digital resistive switching behaviors. Typical synapse behaviors are emulated in different working modes, and the cell shows low/high accuracies with different power consumption in the MNIST recognition task. These findings provide potential for energy-efficient and feasible neuro-morphic computing based on AlOx monolayer memristors.