Enhanced absorption range and sensing efficiency of ZnO-based photodetectors via matchhead yttria-stabilized zirconia (YSZ) co-sputtering incorporating artificial intelligence sensing waveform recognition
Deng-Yi Wang
,Yue-Lin Wang
,Cheng-Yu Chang
,Ming-Xiang Xue
,Yew-Chung Wu
,Ming-Ling Lee
,Hsiang Chen
, Yung-Hui LiZinc oxide (ZnO) nanorods (NRs) are promising building blocks for photodetectors due to their wide bandgap and high surface-to-volume ratio; however, their photoresponse is mainly limited to the ultraviolet region. In this work, yttria-stabilized zirconia (YSZ) was deposited onto ZnO NRs by RF magnetron co-sputtering to form YSZ-coated ZnO nanorods (YSZ-coated ZnO NRs). The YSZ coating effectively modifies the surface and interfacial properties of ZnO NRs, leading to a significantly enhanced photoresponse and an extended detection range from the ultraviolet to the visible and near-infrared regions. Device characterization reveals that the visible light sensitivity of the YSZ-modified ZnO photodetector is greatly enhanced compared to pristine ZnO. More importantly, while pristine ZnO shows no detectable response under red (625 nm) and near-infrared (850 nm) illumination, the matchhead YSZ-coated ZnO NRs exhibit clear and stable photoresponses in these spectral regions. The enhanced performance is attributed to YSZ-induced interfacial effects and optimized carrier transport, with an optimal YSZ thickness of approximately 60 nm. Additionally, through the incorporation of AI waveform machine learning-based recognition, UV, visible, and IR light with various wavelengths can be accurately identified by the waveform characteristics. These results demonstrate that YSZ surface engineering effectively mitigates the intrinsic wide-bandgap limitation of ZnO and provides a viable strategy for developing high-sensitivity and broadband ZnO nanostructure-based photodetectors.