Rapid sensing of hidden objects and defects using a single-pixel diffractive terahertz sensor

Jingxi Li, Xurong Li, Nezih T. Yardimci, Jingtian Hu, Yuhang Li, Junjie Chen, Yi-Chun Hung, Mona Jarrahi and Aydogan Ozcan ()
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Jingxi Li: University of California
Xurong Li: University of California
Nezih T. Yardimci: University of California
Jingtian Hu: University of California
Yuhang Li: University of California
Junjie Chen: University of California
Yi-Chun Hung: University of California
Mona Jarrahi: University of California
Aydogan Ozcan: University of California

Nature Communications, 2023, vol. 14, issue 1, 1-14

Abstract: Abstract Terahertz waves offer advantages for nondestructive detection of hidden objects/defects in materials, as they can penetrate most optically-opaque materials. However, existing terahertz inspection systems face throughput and accuracy restrictions due to their limited imaging speed and resolution. Furthermore, machine-vision-based systems using large-pixel-count imaging encounter bottlenecks due to their data storage, transmission and processing requirements. Here, we report a diffractive sensor that rapidly detects hidden defects/objects within a 3D sample using a single-pixel terahertz detector, eliminating sample scanning or image formation/processing. Leveraging deep-learning-optimized diffractive layers, this diffractive sensor can all-optically probe the 3D structural information of samples by outputting a spectrum, directly indicating the presence/absence of hidden structures or defects. We experimentally validated this framework using a single-pixel terahertz time-domain spectroscopy set-up and 3D-printed diffractive layers, successfully detecting unknown hidden defects inside silicon samples. This technique is valuable for applications including security screening, biomedical sensing and industrial quality control.

Date: 2023
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DOI: 10.1038/s41467-023-42554-2

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