Self‐Rectifying MoS<sub>2</sub> Memtransistor via Asymmetry Contact Metal Engineering for Neuromorphic Computing

Memtransistors, integrating the resistive switching behavior of memristors with the gate tunability of transistors, offer significant promise for neuromorphic computing and in-memory processing. However, their scalability in crossbar arrays is limited by sneak leakage currents. In this study, it is reported that a self-rectifying Molybenum Disulfide (MoS<sub>2</sub>) memtransistor is enabled by asymmetric metal contacts, where a Schottky Platinum (Pt) contact and a quasi-ohmic Bismuth (Bi) contact are employed. The asymmetric Schottky barrier, coupled with drain voltage-induced barrier narrowing, induces highly asymmetric current characteristics. The device exhibits exceptional performance metrics, including a high rectification ratio of 10<sup>4</sup>, a switching ratio of 10<sup>4</sup>, and a retention time exceeding 10<sup>5</sup> seconds. The dynamic modulation of the Schottky barrier height is validated through temperature-dependent studies, energy band analysis, and technology computer-aided design (TCAD) simulations. Compared to non-rectifying configurations, the asymmetric memtransistors reduce crossbar array leakage currents by five orders of magnitude while enhancing power efficiency by 61 times. Simulations using an Echo State Network (ESN) highlight the memtransistor's robustness under low-precision and noisy conditions. Overall, the approach presents a scalable, energy-efficient approach for memtransistor-based in-memory computing and neuromorphic architectures.

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Publication Details

Journal
Small
Published
2025-06-29
DOI
https://doi.org/10.1002/smll.202503716
Citations
5
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
3.65

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article

Self‐Rectifying MoS2 Memtransistor via Asymmetry Contact Metal Engineering for Neuromorphic Computing

Yiting Wu, Kai Xiang Zhou, Tian Tan, Yida Li et al.
5 citations
Small
Advanced Memory and Neural Computing
3.65
article

Self‐Rectifying MoS2 Memtransistor via Asymmetry Contact Metal Engineering for Neuromorphic Computing

Yiting Wu, Kai Xiang Zhou, Tian Tan, Yida Li, Haoyue Guo, Linfeng Sun, Xuewei Feng, Maheswari Sivan
article en
5 citations

Abstract

Memtransistors, integrating the resistive switching behavior of memristors with the gate tunability of transistors, offer significant promise for neuromorphic computing and in-memory processing. However, their scalability in crossbar arrays is limited by sneak leakage currents. In this study, it is reported that a self-rectifying Molybenum Disulfide (MoS<sub>2</sub>) memtransistor is enabled by asymmetric metal contacts, where a Schottky Platinum (Pt) contact and a quasi-ohmic Bismuth (Bi) contact are employed. The asymmetric Schottky barrier, coupled with drain voltage-induced barrier narrowing, induces highly asymmetric current characteristics. The device exhibits exceptional performance metrics, including a high rectification ratio of 10<sup>4</sup>, a switching ratio of 10<sup>4</sup>, and a retention time exceeding 10<sup>5</sup> seconds. The dynamic modulation of the Schottky barrier height is validated through temperature-dependent studies, energy band analysis, and technology computer-aided design (TCAD) simulations. Compared to non-rectifying configurations, the asymmetric memtransistors reduce crossbar array leakage currents by five orders of magnitude while enhancing power efficiency by 61 times. Simulations using an Echo State Network (ESN) highlight the memtransistor's robustness under low-precision and noisy conditions. Overall, the approach presents a scalable, energy-efficient approach for memtransistor-based in-memory computing and neuromorphic architectures.

Small
Beijing Institute of Technology (CN), National University of Singapore (SG), Shanghai Jiao Tong University (CN), Southern University of Science and Technology (CN), Beijing Academy of Quantum Information Sciences (CN)
National Natural Science Foundation of China, Shenzhen Fundamental Research Program
Openalex Percentile: Top 7%
Advanced Memory and Neural Computing
3.65
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