Shao He, Wang Weijun, Zhang Yuxuan, Gao Boxiang, Jiang Chunsheng, Li Yezhan, Xie Pengshan, Yan Yan, Shen Yi, Wu Zenghui, Wang Ruiheng, Ji Yu, Ling Haifeng, Huang Wei, Ho Johnny C
Department of Materials Science and Engineering, City University of Hong Kong, Hong Kong SAR, 999077, China.
State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications (NJUPT), Nanjing, 210023, China.
Adv Mater. 2025 Feb;37(6):e2414261. doi: 10.1002/adma.202414261. Epub 2024 Dec 10.
Traditional imaging systems struggle in weak or complex lighting environments due to their fixed spectral responses, resulting in spectral mismatches and degraded image quality. To address these challenges, a bioinspired adaptive broadband image sensor is developed. This innovative sensor leverages a meticulously designed type-I heterojunction alignment of 0D perovskite quantum dots (PQDs) and 2D black phosphorus (BP). This configuration enables efficient carrier injection control and advanced computing capabilities within an integrated phototransistor array. The sensor's unique responses to both visible and infrared (IR) light facilitate selective enhancement and precise feature extraction under varying lighting conditions. Furthermore, it supports real-time convolution and image restoration within a convolutional autoencoder (CAE) network, effectively countering image degradation by capturing spectral features. Remarkably, the hardware responsivity weights perform comparably to software-trained weights, achieving an image restoration accuracy of over 85%. This approach offers a robust and versatile solution for machine vision applications that demand precise and adaptive imaging in dynamic lighting environments.
传统成像系统由于其固定的光谱响应，在弱光或复杂光照环境中表现不佳，导致光谱不匹配和图像质量下降。为应对这些挑战，研发了一种受生物启发的自适应宽带图像传感器。这种创新型传感器利用精心设计的零维钙钛矿量子点（PQD）和二维黑磷（BP）的I型异质结排列。这种配置能够在集成光电晶体管阵列中实现高效的载流子注入控制和先进的计算能力。该传感器对可见光和红外（IR）光的独特响应有助于在不同光照条件下进行选择性增强和精确特征提取。此外，它支持在卷积自动编码器（CAE）网络内进行实时卷积和图像恢复，通过捕捉光谱特征有效对抗图像退化。值得注意的是，硬件响应权重与软件训练的权重表现相当，图像恢复准确率超过85%。这种方法为在动态光照环境中需要精确和自适应成像的机器视觉应用提供了一种强大且通用的解决方案。
