@article{Ma2025, 
author = {Shihong Ma and Xiankai Lin and Junli Du and Chunlei Zhang and Wenbo Li and Guitian Qiu and Lingan Kong and Ziling Chen and Pei Lin and Qijie Liang},
title = {Light-adaptable and polarization-sensitive bionic vision by contact engineering for multi-dimensional imaging recognition},
year = {2025},
journal = {Nano Research},
volume = {18},
number = {8},
pages = {94907546},
keywords = {polarization-sensitive, contact engineering, PdSe2 transistor, dim and bright adaptation, bionic vision},
url = {https://www.sciopen.com/article/10.26599/NR.2025.94907546},
doi = {10.26599/NR.2025.94907546},
abstract = {Simulating ambient light adaptability and polarization sensitivity of biological vision is paramount for developing intelligent optoelectronic devices with multi-dimensional perception capabilities. However, achieving both functionalities in semiconductor devices has historically necessitated complex architectures and high-voltage operation, posing significant challenges for bionic vision systems. Here, we present a light-adaptable and polarization-sensitive bionic vision utilizing a simple yet effective strategy of semiconductor-metal contact engineering in PdSe2 transistors. By exploiting the differential coupling strengths at diverse metal–semiconductor interfaces to modulate the dynamics of photogenerated carriers, the device achieves energy-efficient visual adaptive perception across a broad range of lighting conditions, from dim to bright, without the need for additional gate voltage. Furthermore, this transistor enables multi-dimensional perception of visual information through dynamic polarization angle changes and light intensity (dim/bright) detection, providing rich input features for intelligent recognition in complex scenarios. Capitalizing on the intrinsic anisotropy of PdSe2 and contact engineering, we have constructed a bionic light-adaptive visual neural network capable of perceiving and recognizing images in complex lighting environments. When enhanced by a residual-generating adversarial network, the system achieves remarkable recognition accuracies of 98% and 97% under dim and bright adaptation conditions, respectively. This research offers a streamlined, versatile, and scalable approach for developing energy-efficient, highly integrated, and multi-dimensional imaging recognition capabilities in light-adaptive and polarization-sensitive bionic vision devices.}
}