A thousand-state optoelectronic memory for high-precision spatiotemporal encoding

quickly combine with holes from amorphous carbon, effectively reducing deexcitation recombination with charged vacancies and associated noise. The resulting device achieves 1,024 distinguishable optoelectronic memory states without any denoising process, enabling high-precision spatiotemporal information encoding. The thousands of states in optoelectronic memory allows for the emulation raptor vision to perception fast-moving objects.

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Publication Details

Journal
Nature Communications
Published
2026-04-13
DOI
https://doi.org/10.1038/s41467-026-71504-x
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

A thousand-state optoelectronic memory for high-precision spatiotemporal encoding

Yang Chai, Guangdong Zhou, Xuesen Xie, Jin Ye et al.
Nature Communications
Neural Networks and Reservoir Computing
article

A thousand-state optoelectronic memory for high-precision spatiotemporal encoding

Yang Chai, Guangdong Zhou, Xuesen Xie, Jin Ye, Yu Xu, Chaoyi Zhu
article en

Abstract

quickly combine with holes from amorphous carbon, effectively reducing deexcitation recombination with charged vacancies and associated noise. The resulting device achieves 1,024 distinguishable optoelectronic memory states without any denoising process, enabling high-precision spatiotemporal information encoding. The thousands of states in optoelectronic memory allows for the emulation raptor vision to perception fast-moving objects.

Nature Communications
Southwest University (CN), Hong Kong Polytechnic University (HK)
National Natural Science Foundation of China, Hong Kong Polytechnic University
Affordable and clean energy
Openalex Percentile: Top 18%
Neural Networks and Reservoir Computing
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