Mechanics-Based Approach for Detection and Measurement of Particle Contamination in Proximity Nanofabrication Processes

Singhal, Shrawan; Grigas, Michelle; Sreenivasan, Sameet

doi:10.1115/1.4033742

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…6(b). Because the template is fairly rigid, the resulting exclusion zone caused by a particle can have diameter of the order of one thousand times that of the particle itself [27], so the particle that caused this specific void was probably of the order of 3 lm in size. As seen in Fig.…”

Section: Resultsmentioning

confidence: 99%

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

Gawlik

Barr

Mallavarapu

et al. 2021

Journal of Micro and Nano-Manufacturing

Self Cite

View full text Add to dashboard Cite

Far-field spectral imaging, coupled with computer vision methods, is demonstrated as an effective inspection method for detection, classification, and root-cause analysis of manufacturing defects in large area Si nanopillar arrays. Si nanopillar arrays exhibit a variety of nanophotonic effects, causing them to produce colors and spectral signatures which are highly sensitive to defects, on both the macro- and nanoscales, which can be detected in far-field imaging. Compared with traditional nanometrology approaches like scanning electron microscopy (SEM), atomic force microscopy (AFM), and optical scatterometry, spectral imaging offers much higher throughput due to its large field of view (FOV), high spatial resolution, and ability to be performed in-line and non-destructively. Thus, spectral imaging is an excellent choice for high speed defect detection/classification in Si nanopillar arrays and potentially other types of large-area nanostructure arrays (LNAs) fabricated on Si wafers, glass sheets, and roll-to-roll webs. The origins of different types of nanoimprint patterning defects - including particle voids, etch delay, and non-filling - and the unique ways in which they manifest as optical changes in the completed nanostructure arrays are discussed. With this understanding in mind, computer vision methods are applied to spectral image data to detect and classify various defects in a sample containing wine glass shaped Si resonator arrays.

show abstract

Section: Resultsmentioning

confidence: 99%

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

Gawlik

Barr

Mallavarapu

et al. 2021

Journal of Micro and Nano-Manufacturing

Self Cite

View full text Add to dashboard Cite

show abstract

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

Gawlik¹,

Barrera²,

Yu³

et al. 2020

Opt. Express

View full text Add to dashboard Cite

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.

show abstract

Mechanics-Based Approach for Detection and Measurement of Particle Contamination in Proximity Nanofabrication Processes

Cited by 2 publications

References 11 publications

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

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

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

Contact Info

Product

Resources

About