Solution-Processed Robust Multifunctional Memristor of 2D Layered Material Thin Film
Memristors have gained significant attention recently due to their unique ability to exhibit functionalities for brain-inspired neuromorphic computing. Here, we demonstrate a high-performance multifunctional memristor using a thin film of liquid-phase exfoliated (LPE) 2D MoS2 pinched between two ele...
Gespeichert in:
Veröffentlicht in: | ACS nano 2024-01, Vol.18 (1), p.1137-1148 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | Memristors have gained significant attention recently due to their unique ability to exhibit functionalities for brain-inspired neuromorphic computing. Here, we demonstrate a high-performance multifunctional memristor using a thin film of liquid-phase exfoliated (LPE) 2D MoS2 pinched between two electrodes. Nanoscale inspection of a solution-processed MoS2 thin film using scanning electron and scanning probe microscopies revealed the high-quality and defect-free nature. Systematic current–voltage (I–V) characterizations depict a facile, nonvolatile resistive switching behavior of our 2D MoS2 thin film device with a current On/Off ratio of 103 and energy cost of only a few picojoules. Excellent performance metrics, including at least 103 cycle endurance, 104 s retention, and switching speed down to a few nanoseconds, reflect robust high-performance data storage capability. Charge carriers trapping and detrapping at the sulfur vacancy defect sites in MoS2 nanosheets mainly display the resistive switching property, supported by the impedance analysis and theoretical fitting results. Multifunctionality is leveraged through implementing two-input logic gate operations, edge computation, and crucial adaptive learning via a Pavlov’s dogs experiment. Overall, our solution-processed MoS2 memristor has the potential for tremendous future opportunities in integrated circuits and different computing paradigms, including energy-efficient neuromorphic computing hardware in artificial intelligence. |
---|---|
ISSN: | 1936-0851 1936-086X |
DOI: | 10.1021/acsnano.3c10775 |