An artifical synapse based on graphene field-effect transistor with silver gel/polarized-aptamer gate

Han, Chuanyu; Li, Yijun; Zhang, Zhi Xing; Liu, Weihua; Li, Xin; Guo, Xiaoqing; Wan, Jun; Zhang, Guohe; Wang, Xiao Li; Lin, Qiao

doi:10.1016/j.orgel.2021.106118

Cited by 12 publications

(23 citation statements)

References 29 publications

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“…The direction of applied gate voltage determines the polarization of ΔPSC which shows potentiative or depressive plasticity. When the channel material is a heterojunction, an obvious hysteresis window or typical bipolar switching behavior will also be observed due to the existence of the interface and the migration of defects at the interface [5b,5d,5e] . From the angle of physics, a negative gate voltage applied to graphene results in the shifted downward of the Fermi level and the holes in the graphene channel dominate the charge transport, leading to an increased output current.…”

Section: Graphene‐based Artificial Synapsesmentioning

confidence: 99%

Section: Graphene‐based Artificial Synapsesmentioning

confidence: 99%

Section: Graphene‐based Artificial Synapsesmentioning

confidence: 99%

“…The transition between electron‐ and hole‐dominated conductions allows both behaviors of excitatory and inhibitory synapses to be realized in a single graphene transistor. [ 5 ] When acting as the memristive material or back electrode in synaptic memristors, graphene‐based memristive materials can perform memristive function through various mechanisms such as defect migration, vacancy migration, filament formation, and charge trapping, [ 6 ] while graphene‐based back electrodes can reduce the running current, increase the on/off ratio, and lower the energy consumption of memristors due to their high out‐off‐plane contact resistance. [ 7 ] Memristors possess the current–voltage characteristics with a closed hysteresis loop pinched in the origin, showing an intrinsic nonvolatile synaptic plasticity.…”

Section: Introductionmentioning

confidence: 99%

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Recent Advances of Graphene and Related Materials in Artificial Intelligence

Huang

Zhu

2022

Advanced Intelligent Systems

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Biological brains perform real‐time processing of unstructured data with ultralow energy consumption and represent the most efficient computing systems. Emulating the working principles of biological brains brings a revolutionary breakthrough in artificial intelligence (AI), generating two kinds of imitation approaches including machine learning (ML) and neuromorphic computing (NC) with artificial neurons/synapses. Herein this review, a general description of ML methods, the concept, and principle of artificial synapses (ASs) in NC derived from biological synapses, and their relationships, is provided. Graphene, one of the most representative 2D materials, arouses considerable attention due to its unique structure and properties. The application of ML in properties prediction (electronic properties, mechanical properties, thermal properties, cytotoxicity), structure recognition (atomic structure, microscopic dimensions/shapes), inverse design (composition, structure), and task recognition (chemical recognition, motion recognition, 3D imaging) of graphene and its derivatives and composites are summarized, and corresponding methods are discussed in the case studies. Recent progress in the development and application of graphene‐based ASs (synaptic transistors and memristors) is briefly introduced, where graphene‐based materials serve as the channel materials of synaptic transistors, the memristive materials, or back electrodes of synaptic memristors. Finally, the main challenges and prospects of graphene‐incorporated ML and ASs are presented.

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Section: Graphene‐based Artificial Synapsesmentioning

confidence: 99%

Section: Graphene‐based Artificial Synapsesmentioning

confidence: 99%

Section: Graphene‐based Artificial Synapsesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Recent Advances of Graphene and Related Materials in Artificial Intelligence

Huang

Zhu

2022

Advanced Intelligent Systems

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“…However, existing 2D-material based neuromorphic systems often employ elements that would induce toxicity when interacting with biological systems, e.g., involving elements like Li + ionic carriers 22,23 which directly impact the nervous system 24 . Other 2D carbon-based systems have non-ideal conductance responses that negatively impact performance in neuromorphic applications [25][26][27] . As a result, new device innovations are needed for future neuromorphic computing solutions that can directly integrate with biological tissue.…”

mentioning

confidence: 99%

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

et al. 2022

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CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

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Recent progress on two-dimensional neuromorphic devices and artificial neural network

Tian

Wei

et al. 2021

Current Applied Physics

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An artifical synapse based on graphene field-effect transistor with silver gel/polarized-aptamer gate

Cited by 12 publications

References 29 publications

Recent Advances of Graphene and Related Materials in Artificial Intelligence

Recent Advances of Graphene and Related Materials in Artificial Intelligence

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

Recent progress on two-dimensional neuromorphic devices and artificial neural network

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