MXenes, an emerging class of 2D transition metal carbides and nitrides with the general formula M X T (n = 1-4), have potential for application as floating gates in memory devices because of their intrinsic properties of a 2D structure, high density-of-states, and high work function. In this study, a series of MXene-TiO core-shell nanosheets are synthesized by deterministic control of the surface oxidation of MXene. The floating gate (multilayer MXene) and tunneling layer (TiO ) in a nano-floating-gate transistor memory (NFGTM) device are prepared simultaneously by a facile, low-cost, and water-based process. The memory performance is optimized via adjustment of the thickness of the oxidation layer formed on the MXene surface. The fabricated MXene NFGTMs exhibit excellent nonvolatile memory characteristics, including a large memory window (>35.2 V), high programming/erasing current ratio (≈10 ), low off-current (<1 pA), long retention (>10 s), and cyclic endurance (300 cycles). Furthermore, synaptic functions, including the excitatory postsynaptic current/inhibitory postsynaptic current, paired-pulse facilitation, and synaptic plasticity (long-term potentiation/depression), are successfully emulated using the MXene NFGTMs. The successful control of MXene oxidation and its application to NFGTMs are expected to inspire the application of MXene as a data-storage medium in future memory devices.
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http://dx.doi.org/10.1002/adma.201907633 | DOI Listing |
BMC Neurol
December 2024
Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, 02115, USA.
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December 2024
College of Sciences, National University of Defense Technology, 410073, Changsha, China.
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December 2024
Department of Communications and Electronics, Delta University for Science and Technology, Mansoura, Egypt.
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December 2024
Laboratory of Microsystems LMIS1, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland.
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View Article and Find Full Text PDFChem Rev
December 2024
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.
Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention.
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