Vertically integrated spiking cone photoreceptor arrays for color perception

Xiangjing Wang, Chunsheng Chen, Li Zhu, Kailu Shi, Baocheng Peng, Yixin Zhu, Huiwu Mao, Haotian Long, Shuo Ke, Chuanyu Fu, Ying Zhu, Changjin Wan () and Qing Wan ()
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Xiangjing Wang: Nanjing University
Chunsheng Chen: Nanjing University
Li Zhu: Nanjing University of Posts and Telecommunications
Kailu Shi: Nanjing University
Baocheng Peng: Nanjing University
Yixin Zhu: Nanjing University
Huiwu Mao: Nanjing University
Haotian Long: Nanjing University
Shuo Ke: Nanjing University
Chuanyu Fu: Nanjing University
Ying Zhu: Nanjing University
Changjin Wan: Nanjing University
Qing Wan: Nanjing University

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract The cone photoreceptors in our eyes selectively transduce the natural light into spiking representations, which endows the brain with high energy-efficiency color vision. However, the cone-like device with color-selectivity and spike-encoding capability remains challenging. Here, we propose a metal oxide-based vertically integrated spiking cone photoreceptor array, which can directly transduce persistent lights into spike trains at a certain rate according to the input wavelengths. Such spiking cone photoreceptors have an ultralow power consumption of less than 400 picowatts per spike in visible light, which is very close to biological cones. In this work, lights with three wavelengths were exploited as pseudo-three-primary colors to form ‘colorful’ images for recognition tasks, and the device with the ability to discriminate mixed colors shows better accuracy. Our results would enable hardware spiking neural networks with biologically plausible visual perception and provide great potential for the development of dynamic vision sensors.

Date: 2023
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DOI: 10.1038/s41467-023-39143-8

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