skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

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

Journal Article · · Review of Scientific Instruments
DOI:https://doi.org/10.1063/1.4876485· OSTI ID:22308868

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.

OSTI ID:
22308868
Journal Information:
Review of Scientific Instruments, Vol. 85, Issue 5; Other Information: (c) 2014 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); ISSN 0034-6748
Country of Publication:
United States
Language:
English