Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction

Changsong Gao, Di Liu, Chenhui Xu, Weidong Xie, Xianghong Zhang, Junhua Bai, Zhixian Lin, Cheng Zhang, Yuanyuan Hu, Tailiang Guo and Huipeng Chen ()
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Changsong Gao: Fuzhou University
Di Liu: Fuzhou University
Chenhui Xu: Fuzhou University
Weidong Xie: Fuzhou University
Xianghong Zhang: Fuzhou University
Junhua Bai: Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City
Zhixian Lin: Fuzhou University
Cheng Zhang: Fuzhou University
Yuanyuan Hu: Hunan University
Tailiang Guo: Fuzhou University
Huipeng Chen: Fuzhou University

Nature Communications, 2024, vol. 15, issue 1, 1-13

Abstract: Abstract Reservoir computing has attracted considerable attention due to its low training cost. However, existing neuromorphic hardware, focusing mainly on shallow-reservoir computing, faces challenges in providing adequate spatial and temporal scales characteristic for effective computing. Here, we report an ultra-short channel organic neuromorphic vertical transistor with distributed reservoir states. The carrier dynamics used to map signals are enriched by coupled multivariate physics mechanisms, while the vertical architecture employed greatly increases the feedback intensity of the device. Consequently, the device as a reservoir, effectively mapping sequential signals into distributed reservoir state space with 1152 reservoir states, and the range ratio of temporal and spatial characteristics can simultaneously reach 2640 and 650, respectively. The grouped-reservoir computing based on the device can simultaneously adapt to different spatiotemporal task, achieving recognition accuracy over 94% and prediction correlation over 95%. This work proposes a new strategy for developing high-performance reservoir computing networks.

Date: 2024
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DOI: 10.1038/s41467-024-44942-8

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