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

Abstract

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.

High-Throughput Electrochemistry to Study Materials Degradation in Extreme Environments

“…In the analysis of postcorrosion samples by scanning electron microscopy (SEM) or transmission electron microscopy (TEM), computer vision has been demonstrated to quantify material porosity 30 with some success, as well as to identify and classify material defects. 31 Another major strategy for data reduction from SEM/TEM images is to use convolutional neural networks (CNN) to identify, classify, and quantify such features. 32 In determining the phasefractions of combinatorially processed alloy samples by HTP X-ray diffraction, the implementation of CNN either on its own 33−38 or coupled to heuristic MATLAB scripts 39,40 has proliferated in recent years.…”

“…In the analysis of postcorrosion samples by scanning electron microscopy (SEM) or transmission electron microscopy (TEM), computer vision has been demonstrated to quantify material porosity with some success, as well as to identify and classify material defects . Another major strategy for data reduction from SEM/TEM images is to use convolutional neural networks (CNN) to identify, classify, and quantify such features .…”