GeTe/MoTe 2 Van der Waals Heterostructures: Enabling Ultralow Voltage Memristors for Nonvolatile Memory and Neuromorphic Computing Applications.
Atul C KhotKiran A NirmalTukaram D DongaleTae Geun KimPublished in: Small (Weinheim an der Bergstrasse, Germany) (2024)
Advanced electronic semiconducting Van der Waals heterostructures (HSs) are promising candidates for exploring next-generation nanoelectronics owing to their exceptional electronic properties, which present the possibility of extending their functionalities to diverse potential applications. In this study, GeTe/MoTe 2 HS are explored for nonvolatile memory and neuromorphic-computing applications. Sputter-deposited Ag/GeTe/MoTe 2 /Pt HS cross-point devices are fabricated, and they demonstrate memristor behavior at ultralow switching voltages (V SET : 0.15 V and V RESET : -0.14 V) with very low energy consumption (≈30 nJ), high memory window, long retention time (10 4 s), and excellent endurance (10 5 cycles). Resistive switching is achieved by adjusting the interface between the Ag top electrode and the heterojunction switching layer. Cross-sectional transmission electron microscope images and conductive atomic force microscopy analysis confirm the presence of a conducting filament in the heterojunction switching layer. Further, emulating various synaptic functions of a biological synapse reveals that GeTe/MoTe 2 HS can be utilized for energy-efficient neuromorphic-computing applications. A multilayer perceptron is implemented using the synaptic weights of the Ag/GeTe/MoTe 2 /Pt HS device, revealing high pattern accuracy (81.3%). These results indicate that HS devices can be considered a potential solution for high-density memory and artificial intelligence applications.
Keyphrases
- artificial intelligence
- working memory
- high density
- atomic force microscopy
- visible light
- deep learning
- cross sectional
- machine learning
- solar cells
- highly efficient
- skeletal muscle
- high speed
- high intensity
- single molecule
- optical coherence tomography
- mass spectrometry
- convolutional neural network
- body composition
- gold nanoparticles
- electron microscopy