Toward grouped-reservoir computing: organic neuromorphic vertical transistor with distributed reservoir states for efficient recognition and prediction.
Changsong Gao: Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China.
Di Liu: Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China.
Chenhui Xu: Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China.
Weidong Xie: Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China.
Xianghong Zhang: Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China. ORCID
Junhua Bai: Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, 350207, Fuzhou, China.
Zhixian Lin: Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China.
Cheng Zhang: Department of Physics, Fuzhou University, 350108, Fuzhou, China.
Yuanyuan Hu: Changsha Semiconductor Technology and Application Innovation Research Institute, College of Semiconductors (College of Integrated Circuits), Hunan University, 410082, Changsha, China. ORCID
Tailiang Guo: Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China.
Huipeng Chen: Institute of Optoelectronic Display, National & Local United Engineering Lab of Flat Panel Display Technology, Fuzhou University, 350002, Fuzhou, China. hpchen@fzu.edu.cn. ORCID
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.
References
Nat Commun. 2022 Mar 31;13(1):1707
[PMID: 35361828]
