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Abstract Recent studies of resistive switching devices with hexagonal boron nitride (h-BN) as the switching layer have shown the potential of two-dimensional (2D) materials for memory and neuromorphic computing applications. The use of 2D materials allows scaling the resistive switching layer thickness to sub-nanometer dimensions enabling devices to operate with low switching voltages and high programming speeds, offering large improvements in efficiency and performance as well as ultra-dense integration. These characteristics are of interest for the implementation of neuromorphic computing and machine learning hardware based on memristor crossbars. However, existing demonstrations of h-BN memristors focus on single isolated device switching properties and lack attention to fundamental machine learning functions. This paper demonstrates the hardware implementation of dot product operations, a basic analog function ubiquitous in machine learning, using h-BN memristor arrays. Moreover, we demonstrate the hardware implementation of a linear regression algorithm on h-BN memristor arrays.
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A Study on h‐BN Resistive Switching Temporal Response
Abstract Previous work that studied hexagonal boron nitride (h‐BN) memristor DC resistive‐switching characteristics is extended to include an experimental understanding of their dynamic behavior upon programming or synaptic weight update. The focus is on the temporal resistive switching response to driving stimulus (programming voltage pulses) effecting conductance updates during training in neural network crossbar implementations. Test arrays are fabricated at the wafer level, enabled by the transfer of CVD‐grown few‐layer (8 layer) or multi‐layer (18 layer) h‐BN films. A comprehensive study of their temporal response under various conditions–voltage pulse amplitude, edge rate (pulse rise/fall times), and temperature–provides new insights into the resistive switching process toward optimized devices and improvements in their implementation of artificial neural networks. The h‐BN memristors can achieve multi‐state operation through ultrafast pulsed switching (< 25 ns) with high energy efficiency (≈10 pJ pulse−1).
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- Award ID(s):
- 2052527
- PAR ID:
- 10574886
- Publisher / Repository:
- Advanced Electronic Materials
- Date Published:
- Journal Name:
- Advanced Electronic Materials
- Volume:
- 10
- Issue:
- 9
- ISSN:
- 2199-160X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/aelm.202400022
