Emulation of Synaptic Plasticity in WO<sub>3</sub>‐Based Ion‐Gated Transistors

Abstract Neuromorphic systems, inspired by the human brain, promise significant advancements in computational efficiency and power consumption by integrating processing and memory functions, thereby addressing the von Neumann bottleneck. This paper explores the synaptic plasticity of a WO 3 ‐based ion‐gated transistor (IGT) in [EMIM][TFSI] and a 0.1 mol L −1 LiTFSI in [EMIM][TFSI] for neuromorphic computing applications. Cyclic voltammetry (CV), transistor characteristics, and atomic force microscopy (AFM) force–distance (FD) profiling analyses reveal that Li + brings about ion intercalation, together with higher mobility and conductance, and slower response time (τ). WO 3 IGTs exhibit spike amplitude‐dependent plasticity (SADP), spike number‐dependent plasticity (SNDP), spike duration‐dependent plasticity (SDDP), frequency‐dependent plasticity (FDP), and paired‐pulse facilitation (PPF), which are all crucial for mimicking biological synaptic functions and understanding how to achieve different types of plasticity in the same IGT. The findings underscore the importance of selecting the appropriate ionic medium to optimize the performance of synaptic transistors, enabling the development of neuromorphic systems capable of adaptive learning and real‐time processing, which are essential for applications in artificial intelligence (AI).

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Publication Details

Journal
Advanced Electronic Materials
Published
2025-03-20
DOI
https://doi.org/10.1002/aelm.202400807
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19

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article

Emulation of Synaptic Plasticity in WO3‐Based Ion‐Gated Transistors

W. Tsai, Lariel Chagas da Silva Neres, Pengyang Dang, Liam Collins et al.
3 citations
Advanced Electronic Materials
Advanced Memory and Neural Computing
2.19
article

Emulation of Synaptic Plasticity in WO3‐Based Ion‐Gated Transistors

W. Tsai, Lariel Chagas da Silva Neres, Pengyang Dang, Liam Collins, Clara Santato, Luan Pereira Camargo, Martin S. Barbosa, Ramin Karimi Azari, José Ramón Herrera Garza
article en
3 citations

Abstract

Abstract Neuromorphic systems, inspired by the human brain, promise significant advancements in computational efficiency and power consumption by integrating processing and memory functions, thereby addressing the von Neumann bottleneck. This paper explores the synaptic plasticity of a WO 3 ‐based ion‐gated transistor (IGT) in [EMIM][TFSI] and a 0.1 mol L −1 LiTFSI in [EMIM][TFSI] for neuromorphic computing applications. Cyclic voltammetry (CV), transistor characteristics, and atomic force microscopy (AFM) force–distance (FD) profiling analyses reveal that Li + brings about ion intercalation, together with higher mobility and conductance, and slower response time (τ). WO 3 IGTs exhibit spike amplitude‐dependent plasticity (SADP), spike number‐dependent plasticity (SNDP), spike duration‐dependent plasticity (SDDP), frequency‐dependent plasticity (FDP), and paired‐pulse facilitation (PPF), which are all crucial for mimicking biological synaptic functions and understanding how to achieve different types of plasticity in the same IGT. The findings underscore the importance of selecting the appropriate ionic medium to optimize the performance of synaptic transistors, enabling the development of neuromorphic systems capable of adaptive learning and real‐time processing, which are essential for applications in artificial intelligence (AI).

Advanced Electronic MaterialsVol. 11(8)
Universidade Estadual de Londrina (BR), Oak Ridge National Laboratory (US), Universidade de São Paulo (BR), Polytechnique Montréal (CA), Universidade Federal de Goiás (BR), Universidade Estadual Paulista (Unesp) (BR)
U.S. Department of Energy, Canada Research Chairs, Fundação de Amparo à Pesquisa do Estado de São Paulo, Conselho Nacional de Desenvolvimento Científico e Tecnológico, Office of Science, Natural Sciences and Engineering Research Council of Canada, Air Force Office of Scientific Research, Oak Ridge National Laboratory
Openalex Percentile: Top 13%
Advanced Memory and Neural Computing
2.19
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