A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing
Neuromorphic computing devices offer promising solutions for next‐generation computing hardware, addressing the high throughput data processing demands of artificial intelligence applications through brain‐mimicking non‐von Neumann architecture. Herein, PEDOT:Tos/PTHF‐based organic electrochemical t...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-01-01
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| Series: | Small Science |
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| Online Access: | https://doi.org/10.1002/smsc.202400415 |
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| author | Yuyang Yin Shaocong Wang Ruihong Weng Na Xiao Jianni Deng Qian Wang Zhongrui Wang Paddy Kwok Leung Chan |
| author_facet | Yuyang Yin Shaocong Wang Ruihong Weng Na Xiao Jianni Deng Qian Wang Zhongrui Wang Paddy Kwok Leung Chan |
| author_sort | Yuyang Yin |
| collection | DOAJ |
| description | Neuromorphic computing devices offer promising solutions for next‐generation computing hardware, addressing the high throughput data processing demands of artificial intelligence applications through brain‐mimicking non‐von Neumann architecture. Herein, PEDOT:Tos/PTHF‐based organic electrochemical transistors (OECTs) with dual‐modal memory functions—both short‐term and long‐term—are demonstrated. By characterizing memory levels and relaxation times, the device has been efficiently manipulated and switched between the two modes through coupled control of pulse voltage and duration. Both short‐term and long‐term memory functions are integrated within the same device, enabling its use as artificial neurons for the reservoir unit and synapses in the readout layer to build up a reservoir computing (RC) system. The performance of the dynamic neuron and synaptic weight update are benchmarked with classification tasks on hand‐written digit images, respectively, both attaining accuracies above 90%. Furthermore, by modulating the device as both reservoir mode and synaptic mode, a full‐OECT RC system capable of distinguishing electromyography signals of hand gestures is demonstrated. These results highlight the potential of simplified, homogeneous integration of dual‐modal OECTs to form brain‐like computing hardware systems for efficient biological signal processing across a broad range of applications. |
| format | Article |
| id | doaj-art-8a0b2062f99d4cb7a5e2b881f5a7234e |
| institution | DOAJ |
| issn | 2688-4046 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Small Science |
| spelling | doaj-art-8a0b2062f99d4cb7a5e2b881f5a7234e2025-08-20T02:51:16ZengWiley-VCHSmall Science2688-40462025-01-0151n/an/a10.1002/smsc.202400415A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir ComputingYuyang Yin0Shaocong Wang1Ruihong Weng2Na Xiao3Jianni Deng4Qian Wang5Zhongrui Wang6Paddy Kwok Leung Chan7Department of Mechanical Engineering The University of Hong Kong Hong Kong SAR ChinaDepartment of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR ChinaDepartment of Mechanical Engineering The University of Hong Kong Hong Kong SAR ChinaAdvanced Biomedical Instrumentation Centre Hong Kong SAR ChinaDepartment of Mechanical Engineering The University of Hong Kong Hong Kong SAR ChinaDepartment of Mechanical Engineering The University of Hong Kong Hong Kong SAR ChinaDepartment of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR ChinaDepartment of Mechanical Engineering The University of Hong Kong Hong Kong SAR ChinaNeuromorphic computing devices offer promising solutions for next‐generation computing hardware, addressing the high throughput data processing demands of artificial intelligence applications through brain‐mimicking non‐von Neumann architecture. Herein, PEDOT:Tos/PTHF‐based organic electrochemical transistors (OECTs) with dual‐modal memory functions—both short‐term and long‐term—are demonstrated. By characterizing memory levels and relaxation times, the device has been efficiently manipulated and switched between the two modes through coupled control of pulse voltage and duration. Both short‐term and long‐term memory functions are integrated within the same device, enabling its use as artificial neurons for the reservoir unit and synapses in the readout layer to build up a reservoir computing (RC) system. The performance of the dynamic neuron and synaptic weight update are benchmarked with classification tasks on hand‐written digit images, respectively, both attaining accuracies above 90%. Furthermore, by modulating the device as both reservoir mode and synaptic mode, a full‐OECT RC system capable of distinguishing electromyography signals of hand gestures is demonstrated. These results highlight the potential of simplified, homogeneous integration of dual‐modal OECTs to form brain‐like computing hardware systems for efficient biological signal processing across a broad range of applications.https://doi.org/10.1002/smsc.202400415long‐term memoryneuromorphic transistorsorganic electrochemical transistorsreservoir computingshort‐term memory |
| spellingShingle | Yuyang Yin Shaocong Wang Ruihong Weng Na Xiao Jianni Deng Qian Wang Zhongrui Wang Paddy Kwok Leung Chan A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing Small Science long‐term memory neuromorphic transistors organic electrochemical transistors reservoir computing short‐term memory |
| title | A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing |
| title_full | A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing |
| title_fullStr | A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing |
| title_full_unstemmed | A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing |
| title_short | A Dual‐Modal Memory Organic Electrochemical Transistor Implementation for Reservoir Computing |
| title_sort | dual modal memory organic electrochemical transistor implementation for reservoir computing |
| topic | long‐term memory neuromorphic transistors organic electrochemical transistors reservoir computing short‐term memory |
| url | https://doi.org/10.1002/smsc.202400415 |
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