Machine vision systems that capture images for visual inspection and identification tasks have to be able to perceive a scene under a range of illumination conditions. To achieve this, current systems use circuitry and algorithms that compromise efficiency and increase complexity. Here we report bioinspired vision sensors that are based on molybdenum disulfide phototransistors and exhibit time-varying activation and inhibition characteristics. Charge trap states are intentionally introduced into the surface of molybdenum disulfide, enabling the dynamic modulation of the photosensitivity of the devices under different lighting conditions. The light-intensity-dependent characteristics of the sensors match Weber’s law in which the perceived change in stimuli is proportional to the light stimuli. The approach offers visual adaptation with highly localized and dynamic modulation of photosensitivity under different lighting conditions at the pixel level, creating an effective perception range of up to 199 dB. The phototransistor arrays exhibit image contrast enhancement for both scotopic and photopic adaptation.
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Source data are provided with this paper. The data that support the plots within these paper and other findings of this study are available from the corresponding authors upon reasonable request.
The codes used for simulation and data plotting are available from the corresponding authors upon reasonable request.
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This work was supported by China Postdoctoral Science Foundation (2021M692221); Research Grant Council of Hong Kong (15205619); Science, Technology and Innovation Commission of Shenzhen (JCYJ20180507183424383 and SGDX2020110309540000); and the Hong Kong Polytechnic University (1-ZE1T and 1-ZVGH). J.-H.A. acknowledges support from the National Research Foundation of Korea (NRF-2015R1A3A2066337).
The authors declare no competing interests.
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Liao, F., Zhou, Z., Kim, B.J. et al. Bioinspired in-sensor visual adaptation for accurate perception. Nat Electron 5, 84–91 (2022). https://doi.org/10.1038/s41928-022-00713-1
Nature Electronics (2022)
Nature Electronics (2022)