Versatile NbOx‐Based Volatile Memristor for Artificial Intelligent Applications

Abstract To achieve cost‐effectiveness, researchers are exploring various memristors for their adaptation in neuromorphic computing. Recent studies have focused on developing versatile functioning singular memristors, such as those involved in on‐receptor computing, which integrates sensory functions into current neuromorphic computing paradigms. Additionally, adaptations like reservoir computing are being investigated for computing systems. In this study, a memristor composed of a stack of Ti/NbO x /Pt layers is fabricated to explore multifunctional behaviors within a single memristor. By applying bias toward the top Ti electrode, gradual current changes with volatile features are demonstrated, revealing an ion‐migration‐based nonfilamentary switching memristor. Leveraging this volatile functionality, an artificial nociceptor is first implemented, demonstrating key functions of biological nociceptors including thresholding, relaxation, no‐adaptation, and sensitization. Subsequently, synapse emulation akin to the biological brain is achieved through easy conductance potentiation and depression with diverse synapse functions, enabling the memristor to mimic learning activities with spike firing. Lastly, computational applications are explored by adapting edge computing and multi‐bit reservoir computing, expanding the memristor's applications across diverse fields with versatile behaviors.

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Publication Details

Journal
Advanced Functional Materials
Published
2024-09-02
DOI
https://doi.org/10.1002/adfm.202409436
Citations
39
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
8.14

Funders

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article

Versatile NbOx‐Based Volatile Memristor for Artificial Intelligent Applications

Sungjun Kim, Dongyeol Ju
39 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
8.14
article

Versatile NbOx‐Based Volatile Memristor for Artificial Intelligent Applications

Sungjun Kim, Dongyeol Ju
article en
39 citations

Abstract

Abstract To achieve cost‐effectiveness, researchers are exploring various memristors for their adaptation in neuromorphic computing. Recent studies have focused on developing versatile functioning singular memristors, such as those involved in on‐receptor computing, which integrates sensory functions into current neuromorphic computing paradigms. Additionally, adaptations like reservoir computing are being investigated for computing systems. In this study, a memristor composed of a stack of Ti/NbO x /Pt layers is fabricated to explore multifunctional behaviors within a single memristor. By applying bias toward the top Ti electrode, gradual current changes with volatile features are demonstrated, revealing an ion‐migration‐based nonfilamentary switching memristor. Leveraging this volatile functionality, an artificial nociceptor is first implemented, demonstrating key functions of biological nociceptors including thresholding, relaxation, no‐adaptation, and sensitization. Subsequently, synapse emulation akin to the biological brain is achieved through easy conductance potentiation and depression with diverse synapse functions, enabling the memristor to mimic learning activities with spike firing. Lastly, computational applications are explored by adapting edge computing and multi‐bit reservoir computing, expanding the memristor's applications across diverse fields with versatile behaviors.

Advanced Functional MaterialsVol. 34(49)
Dongguk University (KR)
National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Openalex Percentile: Top 2%
Advanced Memory and Neural Computing
8.14
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