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Memristors based on 2D semiconductors such as MoS2 and its derivative materials exhibit analog switching behaviors capable of emulating some synaptic functions, including short-term plasticity, long-term potentiation, and spike-time-dependent-plasticity. Additional investigation is needed to realize reliable control of such synaptic behaviors for practical device implementation. To meet this scientific need, we fabricated MoS2-based memristors and studied their paired-pulse facilitation (PPF) and long-term memory characteristics under different pulse programming settings. This research has provided a guideline for identifying the programming settings for different neuromorphic processes. For example, a specific setting resulting in PPF > 30% and long-term conductance change < 20% has been identified to be suited for processing real-time temporal information. Furthermore, this research also indicates that the MoS2 memristor keeps having an almost constant relative change in conductance but greatly enhanced drive current level under laser illumination. This behavior can enable an easy integration of such memristive devices with state-of-the-art controller circuits for practice neuromorphic control applications.
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This content will become publicly available on December 1, 2026
High-throughput numerical modeling of the tunable synaptic behavior in 2D MoS2 memristive devices
Memristive devices based on two-dimensional (2D) materials have emerged as potential synaptic candidates for next-generation neuromorphic computing hardware. Here, we introduce a numerical modeling framework that facilitates efficient exploration of the large parameter space for 2D memristive synaptic devices. High-throughput charge-transport simulations are performed to investigate the voltage pulse characteristics for lateral 2D memristors and synaptic device metrics are studied for different weight-update schemes. We show that the same switching mechanism can lead to fundamentally different pulse characteristics influencing not only the device metrics but also the weight-update direction. A thorough analysis of the parameter space allows simultaneous optimization of the linearity, symmetry, and drift in the synaptic behavior that are related through tradeoffs. The presented modeling framework can serve as a tool for designing 2D memristive devices in practical neuromorphic circuits by providing guidelines for materials properties, device functionality, and system performance for target applications.
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- PAR ID:
- 10621523
- Publisher / Repository:
- Springer Nature
- Date Published:
- Journal Name:
- npj 2D Materials and Applications
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2397-7132
- Page Range / eLocation ID:
- 17
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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