High-Performance MoTe<sub>2</sub> Vertical Memristors Featuring Semi-Metallic Antimony Electrodes for an Expanded Memory Window and Improved Stability
MoTe2 exhibits the smallest energy difference between its various phases among all transition metal dichalcogenides, making it a promising candidate for phase-change resistive switching (RS) devices. However, its low on/off current (ION/OFF) ratio limits practical applications. In this study, we developed a MoTe2 vertical memristor using semimetallic Sb as the contact electrode, significantly enhancing the RS ratio by more than an order of magnitude. The Sb-contact MoTe2 memristors exhibit excellent endurance, maintaining a high ION/OFF ratio and stable operating voltages for over 100 cycles in DC endurance tests. Additionally, they exhibit stable bipolar resistive switching with a cycling endurance exceeding 2 × 105, a high ION/OFF ratio, and fast switching speeds of 50 ns for SET and 70 ns for RESET. By employing Sb-based MoTe2 vertical memristors as artificial synapses, we achieved excellent long-term potentiation (LTP) and long-term depression (LTD) characteristics. As a result, the MoTe2 device exhibits an enhanced dynamic range (∼10), superior linearity (0.19 for LTP and −0.16 for LTD), and high-accuracy handwritten digit recognition (>91%). This work presents an effective strategy for improving the performance of MoTe2 vertical memristors, paving the way for their application in advanced neuromorphic computing systems.
Authors
- Enzi Chen
- Xiao‐Tong Zheng (ORCID: https://orcid.org/0000-0001-6335-1383)
- Mingkang Zhang (ORCID: https://orcid.org/0009-0000-2810-2883)
- Xi Wan (ORCID: https://orcid.org/0000-0002-8661-661X)
- Xiaoying Wang (ORCID: https://orcid.org/0000-0001-5396-2279)
- Kun Chen (ORCID: https://orcid.org/0000-0002-3524-289X)
- Weijian Li (ORCID: https://orcid.org/0009-0002-9014-6734)
- Zhenye Lu (ORCID: https://orcid.org/0009-0000-9404-1169)
- Dawei Li (ORCID: https://orcid.org/0009-0006-3709-3070)
- Tianao Liu (ORCID: https://orcid.org/0009-0009-6097-2905)
Institutions
- Jiangnan University (CN)
- Sun Yat-sen University (CN)
- Technology Applications (United States) (US)
- Key Laboratory of Guangdong Province (CN)
- State Key Laboratory of Optoelectronic Materials and Technology
Publication Details
- Journal
- ACS Applied Nano Materials
- Published
- 2025-07-14
- DOI
- https://doi.org/10.1021/acsanm.5c02422
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00