Retention Secured Nonlinear and Self‐Rectifying Analog Charge Trap Memristor for Energy‐Efficient Neuromorphic Hardware

Abstract

A memristive crossbar array (MCA) is an ideal platform for emerging memory and neuromorphic hardware due to its high bitwise density capability. A charge trap memristor (CTM) is an attractive candidate for the memristor cell of the MCA, because the embodied rectifying characteristic frees it from the sneak current issue. Although the potential of the CTM devices has been suggested, their practical viability needs to be further proved. Here, a Pt/Ta 2 O 5 /Nb 2 O 5‐ x /Al 2 O 3‐ y /Ti CTM stack exhibiting high retention and array‐level uniformity is proposed, allowing a highly reliable selector‐less MCA. It shows high self‐rectifying and nonlinear current‐voltage characteristics below 1 µA of programming current with a continuous analog switching behavior. Also, its retention is longer than 10 5 s at 150 °C, suggesting the device is highly stable for non‐volatile analog applications. A plausible band diagram model is proposed based on the electronic spectroscopy results and conduction mechanism analysis. The self‐rectifying and nonlinear characteristics allow reducing the on‐chip training energy consumption by 71% for the MNIST dataset training task with an optimized programming scheme.

Memristor Based on a Lotus Root/Lactalbumin Bilayer as a Biocompatible Artificial Synapse for Neuromorphic Electronics

“…After fitting, the experimental data follow the linear law well, which better simulates the inhibitory characteristics of the weight of biological synapses under the continuous excitation of signals. The conductive state of this continuously changing device corresponds to the plasticity of synapses . The experimental results show that our device has the ability to mimic an electronic synaptic function.…”

“…The conductive state of this continuously changing device corresponds to the plasticity of synapses. 38 The experimental results show that our device has the ability to mimic an electronic synaptic function.…”

Memristor-based neural networks: a bridge from device to artificial intelligence

“…Recently, Kim et al proposed a new material stacking method in order to improve the switching performance of memristors. 163 A memristive cross-array stack with the Pt/Ta 2 O 5 /Nb 2 O 5− x /Al 2 O 3− y /Ti structure exhibited a high retention characteristic (retention time over 10 5 s at 150 °C). Su et al designed a new multilevel nonvolatile lead-free cesium halide memristor with lead-free cesium halide in the MoO x interface layer as the functional layer, and a highly uniform and reproducible memristor was obtained.…”

Emerging memristive artificial neuron and synapse devices for the neuromorphic electronics era

“…8a as an example, the device that Kim et al investigated in a comparative study. 92 They discussed devices without Ta 2 O 5 layers, which suffer from LRS failure in retention and the ones without Al 2 O 3- x layers, which suffer from HRS failure in retention and concluded with the illustration, as shown in Fig. 8b, that aluminium oxide helps with buffering Ti diffusion whereas tantalum oxide stops spontaneous de-trapping.…”