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Published on: August 2, 2019
Conductance quantization and quantum-point-contact formation in hBN/HfO2bilayer memristive devices
Tanmayee Parida1, Arpan Bhattacharyya1, Ummiya Qamar2
1Department of Physics, School of Natural Sciences, Shiv Nadar Institution of Eminence, NH 91, Tehsil Dadri, Gautam Buddha Nagar, Uttar Pradesh 201314, India.
This study demonstrates quantized conductance and quantum point contact formation in memristive devices using hexagonal boron nitride (hBN) over HfO2. These atomic-scale constrictions are formed by hBN flakes, enabling novel memristive device applications.
Area of Science:
- Materials Science
- Nanotechnology
- Solid State Physics
Background:
- Memristive devices offer non-volatile memory and neuromorphic computing potential.
- Atomic-scale constrictions are crucial for advanced electronic functionalities.
- Hexagonal boron nitride (hBN) and Hafnium oxide (HfO2) are key materials in advanced electronics.
Purpose of the Study:
- To investigate the formation of quantum point contacts (QPCs) in memristive devices.
- To explore the role of hexagonal boron nitride (hBN) in HfO2-based memristors.
- To understand the mechanism behind quantized conductance in these heterostructures.
Main Methods:
- Fabrication of memristive devices with exfoliated hBN over HfO2.
- X-ray diffraction (XRD) for strain analysis.
- Raman spectroscopy to characterize hBN.
- Electrical characterization (current-voltage measurements) to observe conductance.
Main Results:
- Quantized conductance plateaus near integer and half-integer multiples of G0 (2e²/h) were observed.
- Evidence of quantum point contact (QPC) formation due to topographical inhomogeneities.
- Raman spectroscopy indicated strain in hBN stacks.
- Hysteretic current-voltage behavior consistent with filament evolution.
Conclusions:
- Scattered hBN flakes induce nanoscale topography, enhancing oxygen vacancy concentration and forming QPCs.
- A unified morphological-ionic model explains the observed phenomena.
- Van-der-Waals/oxide heterostructures are promising for atomic-scale memristive devices and stochastic hardware.
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