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Title: Analysis of atomic force microscopy data for surface characterization using fuzzy logic

Abstract

In this paper we present a methodology to characterize surface nanostructures of thin films. The methodology identifies and isolates nanostructures using Atomic Force Microscopy (AFM) data and extracts quantitative information, such as their size and shape. The fuzzy logic based methodology relies on a Fuzzy Inference Engine (FIE) to classify the data points as being top, bottom, uphill, or downhill. The resulting data sets are then further processed to extract quantitative information about the nanostructures. In the present work we introduce a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and present an omni-directional search technique to improve the structural recognition accuracy. In order to demonstrate the effectiveness of our approach we present a case study which uses our approach to quantitatively identify particle sizes of two specimens each with a unique gold nanoparticle size distribution. - Research Highlights: {yields} A Fuzzy logic analysis technique capable of characterizing AFM images of thin films. {yields} The technique is applicable to different surfaces regardless of their densities. {yields} Fuzzy logic technique does not require manual adjustment of the algorithm parameters. {yields} The technique can quantitatively capture differences between surfaces. {yields} This technique yields more realistic structure boundariesmore » compared to other methods.« less

Authors:
 [1];  [1];  [2];  [1];  [1]
  1. Electron Devices Lab., Electrical Engineering Dept., Santa Clara University, Santa Clara (United States)
  2. University of California- Santa Cruz, Santa Cruz (United States)
Publication Date:
OSTI Identifier:
22066372
Resource Type:
Journal Article
Journal Name:
Materials Characterization
Additional Journal Information:
Journal Volume: 62; Journal Issue: 7; Other Information: Copyright (c) 2011 Elsevier Science B.V., Amsterdam, The Netherlands, All rights reserved.; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 1044-5803
Country of Publication:
United States
Language:
English
Subject:
77 NANOSCIENCE AND NANOTECHNOLOGY; ALGORITHMS; ATOMIC FORCE MICROSCOPY; DISTRIBUTED STRUCTURES; FUZZY LOGIC; GOLD; NANOSTRUCTURES; PARTICLE SIZE; SURFACES; THIN FILMS

Citation Formats

Al-Mousa, Amjed, E-mail: aalmousa@vt.edu, Niemann, Darrell L., Niemann, Devin J., Gunther, Norman G., E-mail: guntherng@msn.com, and Rahman, Mahmud, E-mail: mrahman@scu.edu. Analysis of atomic force microscopy data for surface characterization using fuzzy logic. United States: N. p., 2011. Web. doi:10.1016/J.MATCHAR.2011.04.001.
Al-Mousa, Amjed, E-mail: aalmousa@vt.edu, Niemann, Darrell L., Niemann, Devin J., Gunther, Norman G., E-mail: guntherng@msn.com, & Rahman, Mahmud, E-mail: mrahman@scu.edu. Analysis of atomic force microscopy data for surface characterization using fuzzy logic. United States. doi:10.1016/J.MATCHAR.2011.04.001.
Al-Mousa, Amjed, E-mail: aalmousa@vt.edu, Niemann, Darrell L., Niemann, Devin J., Gunther, Norman G., E-mail: guntherng@msn.com, and Rahman, Mahmud, E-mail: mrahman@scu.edu. Fri . "Analysis of atomic force microscopy data for surface characterization using fuzzy logic". United States. doi:10.1016/J.MATCHAR.2011.04.001.
@article{osti_22066372,
title = {Analysis of atomic force microscopy data for surface characterization using fuzzy logic},
author = {Al-Mousa, Amjed, E-mail: aalmousa@vt.edu and Niemann, Darrell L. and Niemann, Devin J. and Gunther, Norman G., E-mail: guntherng@msn.com and Rahman, Mahmud, E-mail: mrahman@scu.edu},
abstractNote = {In this paper we present a methodology to characterize surface nanostructures of thin films. The methodology identifies and isolates nanostructures using Atomic Force Microscopy (AFM) data and extracts quantitative information, such as their size and shape. The fuzzy logic based methodology relies on a Fuzzy Inference Engine (FIE) to classify the data points as being top, bottom, uphill, or downhill. The resulting data sets are then further processed to extract quantitative information about the nanostructures. In the present work we introduce a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and present an omni-directional search technique to improve the structural recognition accuracy. In order to demonstrate the effectiveness of our approach we present a case study which uses our approach to quantitatively identify particle sizes of two specimens each with a unique gold nanoparticle size distribution. - Research Highlights: {yields} A Fuzzy logic analysis technique capable of characterizing AFM images of thin films. {yields} The technique is applicable to different surfaces regardless of their densities. {yields} Fuzzy logic technique does not require manual adjustment of the algorithm parameters. {yields} The technique can quantitatively capture differences between surfaces. {yields} This technique yields more realistic structure boundaries compared to other methods.},
doi = {10.1016/J.MATCHAR.2011.04.001},
journal = {Materials Characterization},
issn = {1044-5803},
number = 7,
volume = 62,
place = {United States},
year = {2011},
month = {7}
}