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Neural network approximation of tip-abrasion effects in AFM-imaging.

Bakucz, P*; Yacoot, A; Dziomba, T*; Krüger-Sehm, R* (2008) Neural network approximation of tip-abrasion effects in AFM-imaging. Meas. Sci. Technol., 19 (6). 065101

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Abstract

In this paper, we describe the identification of tip-abrasion in a system of scanning probe microscope (AFM) measurements. We attempt to model the tip-abrasion process as a concatenation of a mapping from the measured AFM data to a regression vector and a nonlinear mapping from the regressor space to the output space. The mapping is formed as a basis function expansion. Feedforward neural networks are used to approximate this mapping. The one-hidden layer network gave good quality of fit for the training and test sets for the tip-abrasion system. We illustrate our method with AFM measurements of both fine periodic structures and randomly oriented sharp features and compare our neural network results with those obtained using other methods.

Item Type: Article
Keywords: scanning probe microscope, nanometrology, tip-abrasion, neural networks
Subjects: Engineering Measurements
Engineering Measurements > Dimensional
Last Modified: 02 Feb 2018 13:15
URI: http://eprintspublications.npl.co.uk/id/eprint/4122

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