Resistive Switching of Spinel Li 4 Ti 5 O 12 Lithium-Ion Battery Material for Neuromorphic Computing
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Le résumé fourni par la source
The rapid rise of AI has exposed significant limitations in conventional Von Neumann computing architecture, particularly in regard to speed and energy efficiency. To address these challenges, researchers are exploring a brain-inspired neuromorphic architecture that mimics biological neural networks, enabling massive parallel processing with reduced power consumption for complex AI computational demands. Recent interest has focused on utilizing battery electrodes and solid electrolyte materials for their resistive switching properties in developing a neuromorphic architecture. These properties are precisely tuned through local- and bulk-level chemical composition modifications via voltage bias stimuli. In this study, we demonstrate fabricating a three-terminal lithium-ion electrochemical transistor based on lithium titanium oxide (Li 4 Ti 5 O 12 ), a popular lithium-ion battery anode material. We deposited and characterized LTO thin films using RF sputtering, demonstrating a 6 orders of magnitude increase in electronic conductivity upon lithiation, with conductivity plateauing after 20% lithiation. Density functional theory calculations revealed transformation from the insulating to conducting state, supported by experimental characterization through X-Ray Photoelectron Spectroscopy (XPS) and Direct Current (DC) polarization analyses. The fabricated transistor consisted of LTO as the channel layer, gold as source/drain terminals, lithium phosphorus oxynitride (LiPON) as the lithium-ion conductor, and copper as the gate terminal. The device exhibited clear hysteresis in transfer characteristics due to lithium insertion/extraction processes. Long-term potentiation (LTP) and long-term depression (LTD) measurements showed an asymmetric ratio of 1.425 and maximum/minimum conductance ratio of 7.83. When implemented in a deep neural network (DNN) for MNIST handwritten digit recognition, the device achieved 92.03% accuracy over 20 training epochs. Detailed transport mechanism analysis revealed the crucial role of oxygen vacancies and interface effects in device operation. Our preliminary findings establish LTO-based lithium-ion electrochemical transistors as promising candidates for energy-efficient neuromorphic computing applications, offering potential solutions to traditional Von Neumann architecture limitations.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé, mais le titre doit être comparé manuellement.
- Titre Crossref
- Resistive Switching of Spinel Li <sub>4</sub> Ti <sub>5</sub> O <sub>12</sub> Lithium-Ion Battery Material for Neuromorphic Computing
- Date Crossref
- 21/07/2025
- Éditeur
- American Chemical Society (ACS)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of California San Diego pays non établi dans la noticeUniversité ou école supérieure
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National Taiwan University of Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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University of Chicago pays non établi dans la noticeUniversité ou école supérieure
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Aiiso Yufeng Li Family Department of Chemical and Nano Engineering pays non établi dans la noticeInstitution
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Department of Electrical and Computer Engineering pays non établi dans la noticeInstitution
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Graduate Institute of Applied Science and Technology pays non établi dans la noticeStructure de recherche
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Pritzker School of Molecular Engineering pays non établi dans la noticeUniversité ou école supérieure
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Center for Memory and Recording Research pays non établi dans la noticeInstitution
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Department of Physics pays non établi dans la noticeInstitution
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Shu Chien-Gene Lay Department of Bioengineering pays non établi dans la noticeInstitution
University of California San Diego, National Taiwan University of Science and Technology et University of Chicago, avec 7 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.