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Research Article | Online first | Published: 14 March 2024

Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing

Show Author's Information Xuemeng Hu1,  Jialin Meng1( ),  Tianyang Feng1,  Tianyu Wang1,  Hao Zhu1,  Qingqing Sun1,  David Wei Zhang1,  Lin Chen1,2( )
School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, China
National Integrated Circuit Innovation Center, Shanghai 201203, China
Keywords:
neuromorphic computing, organic heterojunction, synapse behaviors, optical modulation, Modified National Institute of Standards and Technology (MNIST) pattern recognition
Topics:
HeterojunctionsNanoelectronics Neuromorphic ComputingOrganic field effect transistorsPhotoelectric devices
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Hu X, Meng J, Feng T, et al. Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing. Nano Research, 2024, https://doi.org/10.1007/s12274-024-6532-6
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Abstract Full text Electronic supplementary material About this article

Traditional computing structures are blocked by the von Neumann bottleneck, and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more attention. Here, a flexible organic device with 2,7-dioctyl[1] benzothieno [3,2-b][1] benzothiophene (C8-BTBT) and 2,9-didecyldinaphtho [2,3-b:2′,3′-f] thieno [3,2-b] thiophene (C10-DNTT) heterostructural channel having excellent synaptic behaviors was fabricated on muscovite (MICA) substrate, which has a memory window greater than 20 V. This device shows better electrical characteristics than organic field effect transistors with single organic semiconductor channel. Furthermore, the device simulates organism synaptic behaviors successfully, such as paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD) process, and transition from short-term memory (STM) to long-term memory (LTM) by optical and electrical modulations. Importantly, the neuromorphic computing function was verified using the Modified National Institute of Standards and Technology (MNIST) pattern recognition, with a recognition rate nearly 100% without noise. This research proposes a flexible organic heterojunction with the ultra-high recognition rate in MNIST pattern recognition and provides the possibility for future flexible wearable neuromorphic computing devices.


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Electronic supplementary material
About this article

Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing

Show Author's information Xuemeng Hu1, Jialin Meng1( ), Tianyang Feng1, Tianyu Wang1, Hao Zhu1, Qingqing Sun1, David Wei Zhang1, Lin Chen1,2( )
School of Microelectronics, State Key Laboratory of Integrated Chips and Systems, Fudan University, Shanghai 200433, China
National Integrated Circuit Innovation Center, Shanghai 201203, China

Abstract

Traditional computing structures are blocked by the von Neumann bottleneck, and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more attention. Here, a flexible organic device with 2,7-dioctyl[1] benzothieno [3,2-b][1] benzothiophene (C8-BTBT) and 2,9-didecyldinaphtho [2,3-b:2′,3′-f] thieno [3,2-b] thiophene (C10-DNTT) heterostructural channel having excellent synaptic behaviors was fabricated on muscovite (MICA) substrate, which has a memory window greater than 20 V. This device shows better electrical characteristics than organic field effect transistors with single organic semiconductor channel. Furthermore, the device simulates organism synaptic behaviors successfully, such as paired-pulse facilitation (PPF), long-term potentiation/depression (LTP/LTD) process, and transition from short-term memory (STM) to long-term memory (LTM) by optical and electrical modulations. Importantly, the neuromorphic computing function was verified using the Modified National Institute of Standards and Technology (MNIST) pattern recognition, with a recognition rate nearly 100% without noise. This research proposes a flexible organic heterojunction with the ultra-high recognition rate in MNIST pattern recognition and provides the possibility for future flexible wearable neuromorphic computing devices.

Keywords: neuromorphic computing, organic heterojunction, synapse behaviors, optical modulation, Modified National Institute of Standards and Technology (MNIST) pattern recognition

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Publication history
Copyright
Acknowledgements

Publication history

Received: 15 December 2023
Revised: 28 January 2024
Accepted: 30 January 2024
Published: 14 March 2024

Copyright

© Tsinghua University Press 2024

Acknowledgements

Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2021YFA1202600), the National Natural Science Foundation of China (Nos. 92064009 and 22175042), the Science and Technology Commission of Shanghai Municipality (No. 22501100900), the China Postdoctoral Science Foundation (Nos. 2022TQ0068 and 2023M740644), the Shanghai Sailing Program (Nos. 23YF1402200 and 23YF1402400), and Jiashan Fudan Institute.

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