Nanoimprinted ZnO Nanograss Grating Structures on Polyethylene Terephthalate Flexible Substrates for Enhanced Broadband Photosensing and Intelligent Light Source Recognition

Jyun-Jie Chen, Yi-Sheng Chen, Ming-Xiang Xue, Chia-Chi Chang, Jeih-Weih Hung, Ming-Hsien Li, Ming-Yu Kuo, Chun-Hung Lin, Yung-Hui Li, Hsiang Chen, Jung Han

Abstract: Flexible electronic devices are rapidly advancing, bringing innovative applications to optoelectronic technology. However, traditional optoelectronic devices are limited by rigid substrates, making them unsuitable for flexible applications. Due to its excellent optoelectronic properties and low processing temperature, zinc oxide (ZnO) has become an ideal material for flexible photodetectors (PDs). This study aims to integrate grating-patterned ZnO nanograss onto a flexible polyethylene terephthalate (PET) substrate using nanoimprint lithography (NIL) to enhance the broadband photosensing capabilities of ZnO-based PDs. Experimental results show that NIL technology successfully fabricates uniform and well-structured ZnO nanograss gratings on PET substrates, with a linewidth of approximately 600 nm and a pitch of about 1080 nm. The material analyses indicate that the NIL process, assisted by oxygen plasma etching, increases surface oxygen adsorption on ZnO nanorod surfaces, thereby improving device sensitivity. Notably, photosensing measurements demonstrate that, compared with conventional nonpatterned ZnO nanograss PDs, grating-patterned ZnO nanograss PDs exhibit significantly enhanced photoresponse in the visible light region. Absorption spectrum analysis further confirms that the grating structure effectively improves light absorption in the visible wavelength range. This enhanced photoresponse is attributed to the grating structure, which extends the optical path and increases light absorption by ZnO nanorods. In addition, this study employs artificial intelligence (AI) deep learning techniques to analyze photosensing data, successfully identifying the corresponding light sources. This work demonstrates the potential of NIL technology in improving the broadband photosensing performance of ZnO-nanograssbased PDs and, in combination with AI deep learning, creates a broadband PDs capable of distinguishing different light sources without requiring optical filters. This research paves the way for future development of flexible optoelectronic devices, with ZnO nanograss PDs showing great potential for advanced light sensing applications.

External IDs:doi:10.1109/jsen.2025.3607908