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Natural van der Waals canalization lens for non-destructive nanoelectronic circuit imaging and inspection

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posted on 2025-02-15, 17:00 authored by Qingdong Ou, Shuwen Xue, Weiliang Ma, Jiong Yang, Guangyuan Si, Lu Liu, Gang Zhong, Jingying Liu, Zongyuan Xie, Ying Xiao, Kourosh Kalantar-Zadeh, Xiang Qi, Peining Li, Zhigao Dai, Huanyang Chen, Qiaoliang Bao
Optical inspection has long served as a cornerstone non-destructive method in semiconductor wafer manufacturing, particularly for surface and defect analysis. However, conventional techniques such as bright-field and dark-field scattering optics face significant limitations, including insufficient resolution and the inability to penetrate and detect buried structures. Atomic force microscopy (AFM), while offering higher resolution and precise surface characterization, is constrained by slow speed, limited to surface-level imaging, and incapable of resolving subsurface features. Here, we propose an approach that integrates the strengths of dark-field scattering optics and AFM by leveraging a van der Waals (vdW) canalization lens based on natural biaxial {\alpha}-MoO3 crystals. This method enables ultrahigh-resolution subwavelength imaging with the ability to visualize both surface and buried structures, achieving a spatial resolution of 15 nm and grating pitch detection down to 100 nm. The underlying mechanism relies on the unique anisotropic properties of {\alpha}-MoO3, where its atomic-scale unit cells and biaxial symmetry facilitate the diffraction-free propagation of both evanescent and propagating waves via a flat-band canalization regime. Unlike metamaterial-based superlenses and hyperlenses, which suffer from high plasmonic losses, fabrication imperfections, and uniaxial constraints, {\alpha}-MoO3 provides robust and aberration-free imaging in multiple directions. We successfully applied this approach to high-resolution inspection of buried nanoscale electronic circuits, offering unprecedented capabilities essential for next-generation semiconductor manufacturing.

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