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Adaptive and Robust Statistical Methods for Processing Near-Field Scanning Microwave Microscopy Images

Published

Author(s)

Kevin J. Coakley, Samuel Berweger, Atif A. Imtiaz, Thomas M. Wallis, Joel C. Weber, Pavel Kabos

Abstract

Near-field scanning microwave microscopy offers great potential to facilitate characterization, development and modeling of materials. By acquiring microwave images at multiple frequencies and amplitudes (along with the other modalities) one can study material and device\physics at different lateral and depth scales. Images are typically noisy and contaminated by artifacts that can vary from scan line to scan line and planar-like trends due to sample tilt errors. Here, we level images based on an estimate of a smooth 2-d trend determined with a robust implementation of a local regression method. In this robust approach, features and outliers which are not due to the trend are automatically downweighted. We denoise images with the Adaptive Weights Smoothing method. This method smooths out additive noise while preserving edge-like features in images. We demonstrate the feasibility of our methods on topography images and microwave S11 images. For one challenging test case, we demonstrate that our method outperforms alternative methods from the scanning probe microscopy data analysis software package Gwyddion. Our methods should be useful for massive image data sets where manual selection of landmarks or image subsets by a user is impractical.
Citation
Ultramicroscopy
Volume
150

Keywords

Adaptive Weights Smoothing, Atomic force microscopy, Denoising, Ferrite materials, Gwyddion, GaN nanowire, Leveling, Local regression and likelihood, micro-capacitance calibration image, Near-field scanning probe microwave microscopy, Robust statistical methods, Scan artifacts, Statistical image processing
Created November 23, 2014, Updated February 19, 2017