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Title: Note: Artificial neural networks for the automated analysis of force map data in atomic force microscopy

Force curves recorded with the atomic force microscope on structured samples often show an irregular force versus indentation behavior. An analysis of such curves using standard contact models (e.g., the Sneddon model) would generate inaccurate Young's moduli. A critical inspection of the force curve shape is therefore necessary for estimating the reliability of the generated Young's modulus. We used a trained artificial neural network to automatically recognize curves of “good” and of “bad” quality. This is especially useful for improving the analysis of force maps that consist of a large number of force curves.
Authors:
;  [1]
  1. Institute of Applied Physics and LISA, University of Tübingen, Auf der Morgenstelle 10, 72076 Tübingen (Germany)
Publication Date:
OSTI Identifier:
22308868
Resource Type:
Journal Article
Resource Relation:
Journal Name: Review of Scientific Instruments; Journal Volume: 85; Journal Issue: 5; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; ATOMIC FORCE MICROSCOPY; DIAGRAMS; NEURAL NETWORKS; RELIABILITY; YOUNG MODULUS