The potential of memristive devices for applications in nonvolatile memory and neuromorphic computing has sparked considerable interest, particularly in exploring memristive effects in two-dimensional (2D) magnetic materials. However, the progress in developing nonvolatile, magnetic field-free memristive devices using 2D magnets has been limited. In this work, we report an electrostatic-gating-induced nonvolatile memristive effect in CrI-based tunnel junctions. The few-layer CrI-based tunnel junction manifests notable hysteresis in its tunneling resistance as a function of gate voltage. We further engineered a nonvolatile memristor using the CrI tunneling junction with low writing power and at zero magnetic field. We show that the hysteretic transport observed is not a result of trivial effects or inherent magnetic properties of CrI. We propose a potential association between the memristive effect and the newly predicted ferroelectricity in CrI via gating-induced Jahn-Teller distortion. Our work illuminates the potential of 2D magnets in developing next-generation advanced computing technologies.
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http://dx.doi.org/10.1021/acs.nanolett.3c03926 | DOI Listing |
J Phys Chem Lett
December 2024
School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, China.
Advancing the development of novel materials or architectures for random access memories, coupled with an in-depth understanding of their intrinsic conduction mechanisms, holds the potential to transcend the conventional von Neumann bottleneck. In this work, a novel memristor based on the Sb(S,Se) material with an alloy of S and Se was fabricated. A systematic investigation of the correlation between the Se/(S + Se) ratio and memristive performance revealed that Ag/Sb(S,Se)/FTO memristive behavior is uniquely associated with the formation and disruption of anion vacancies and silver filaments.
View Article and Find Full Text PDFAdv Mater
November 2024
Department of Nano Science and Technology and Department of Nanoengineering, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 16419, Republic of Korea.
Adv Mater
November 2024
Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany.
Non-volatile memristors dynamically switch between high (HRS) and low resistance states (LRS) in response to electrical stimuli, essential for electronic memories, neuromorphic computing, and artificial intelligence. High-entropy Prussian blue analogs (HE-PBAs) are promising insertion-type battery materials due to their diverse composition, high structural integrity, and favorable ionic conductivity. This work proposes a non-volatile, bipolar memristor based on HE-PBA.
View Article and Find Full Text PDFCogn Neurodyn
August 2024
College of Artificial Intelligence, Southwest University, Chongqing, 400715 China.
Brain-inspired neuromorphic computing has emerged as a promising solution to overcome the energy and speed limitations of conventional von Neumann architectures. In this context, in-memory computing utilizing memristors has gained attention as a key technology, harnessing their non-volatile characteristics to replicate synaptic behavior akin to the human brain. However, challenges arise from non-linearities, asymmetries, and device variations in memristive devices during synaptic weight updates, leading to inaccurate weight adjustments and diminished recognition accuracy.
View Article and Find Full Text PDFNanoscale
November 2024
National Research Center Kurchatov Institute, Moscow 123182, Russia.
From the very beginning, the emulation of biological principles has been the primary avenue for the development of energy-efficient artificial intelligence systems. Reservoir computing, which has a solid biological basis, is particularly appealing due to its simplicity and efficiency. So-called memristors, resistive switching elements with complex dynamics, have proved beneficial for replicating both principal parts of a reservoir computing system.
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