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.
Authors
- Yiting Wu (ORCID: https://orcid.org/0009-0005-7449-0414)
- Kai Xiang Zhou (ORCID: https://orcid.org/0000-0003-4653-6436)
- Tian Tan (ORCID: https://orcid.org/0000-0003-4639-8301)
- Yida Li (ORCID: https://orcid.org/0000-0002-5675-582X)
- Haoyue Guo (ORCID: https://orcid.org/0000-0003-3477-9203)
- Linfeng Sun (ORCID: https://orcid.org/0000-0001-5851-8206)
- Xuewei Feng (ORCID: https://orcid.org/0000-0002-1463-8270)
- Maheswari Sivan (ORCID: https://orcid.org/0000-0002-1344-292X)
Institutions
- 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)
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
Funders
- National Natural Science Foundation of China
- Shenzhen Fundamental Research Program