Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning

Xiaosong Wu, Shaocong Wang, Wei Huang, Yu Dong, Zhongrui Wang () and Weiguo Huang ()
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Xiaosong Wu: Chinese Academy of Sciences
Shaocong Wang: University of Hong Kong
Wei Huang: Chinese Academy of Sciences
Yu Dong: Chinese Academy of Sciences
Zhongrui Wang: University of Hong Kong
Weiguo Huang: Chinese Academy of Sciences

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a ‘readout function’ on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.

Date: 2023
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DOI: 10.1038/s41467-023-36205-9

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