Hyperspectral imaging for high-throughput, spatially resolved spectroscopic scatterometry of silicon nanopillar arrays

Abstract

Modern high-throughput nanopatterning techniques, such as nanoimprint lithography, make it possible to fabricate arrays of nanostructures (features with dimensions of 10’s to 100’s of nm) over large area substrates (cm2 to m2 scale) such as Si wafers, glass sheets, and flexible roll-to-roll webs. The ability to make such large-area nanostructure arrays (LNAs) has created an extensive design space, enabling a wide array of applications including optical devices, such as wire-grid polarizers, transparent conductors, color filters, and anti-reflection surfaces, and building blocks for electronic components, such as ultracapacitors, sensors, and memory storage architectures. However, existing metrology methods will have trouble scaling alongside fabrication methods. Scanning electron microscopy (SEM) and atomic force microscopy (AFM), for instance, have micron scale fields of view (FOV) that preclude comprehensive characterization of LNAs, which may be manufactured at m2 per minute rates. Scatterometry approaches have larger FOVs (typically 100’s of µm to a few mm), but traditional scatterometry systems measure samples one point at a time, which also makes them too slow for large-scale LNA manufacturing. In this work, we demonstrate parallelization of the traditional spectroscopic scatterometry approach using hyperspectral imaging, increasing the throughput of the technique by a factor of 106-107. We demonstrate this approach by using hyperspectral imaging and inverse modeling of reflectance spectra to derive 3-dimensional geometric data for Si nanopillar array structures over both mm and cm-scale with µm-scale spatial resolution. This work suggests that geometric measurements for a variety of LNAs can be performed with the potential for high speed over large areas which may be critical for future LNA manufacturing.

Spectral Imaging and Computer Vision for High-Throughput Defect Detection and Root-Cause Analysis of Silicon Nanopillar Arrays

“…While certain applications require the full acquired spectra [23], oftentimes it is convenient to convert the hyperspectral data to an RGB image. RGB images are much more compact, which significantly reduces compute cost.…”

Semiconductor device metrology for detecting defective chip due to high-aspect ratio-based structures using hyperspectral imaging and deep learning

“…2 A similar study confirmed the possibility of identifying the size of the nanometer structure constituting the semiconductor as a large-area image. 3 Hyperspectral imaging, which acquires signals with both spatial and spectroscopic resolution in a large area as shown in Figure 1b, acquires high spectroscopic resolution spectrum in a wide wavelength area in all pixels of the image. In case of our system, ISR builds hyperspectral cube data at a high speed through spectral scanning using a monochromator, and can measure whole wafer individually in unit blocks within about 2 hours.…”