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Neural networks for HREM image analysis. (English) Zbl 0981.68745

Summary: We present a new neural network-based method of image processing for determining the local composition and thickness of III–V semiconductors in high resolution electron microscope images. This is of great practical interest as these parameters influence the electrical properties of the semiconductor. Neural networks suppress correlated noise from amorphous object covering and distinguish between variations of sample thickness and semiconductor composition.

MSC:

68U99 Computing methodologies and applications
68U10 Computing methodologies for image processing

Software:

EMS
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Full Text: DOI

References:

[1] Kleber, W.; Bautsch, H.; Bohm, J., Einführung in die Kristallographie (1990), Verlag Technik: Verlag Technik Berlin
[2] Ourmazd, A.; Baumann, F. H.; Bode, M.; Kim, Y., Quantitative chemical lattice imaging: theory and practice, Ultramicroscopy, 34, 237-255 (1990)
[3] Stenkamp, D.; Jäger, W., Compositional and structural characterization of \(Si_xGe_{1−x}\) alloys and heterostructures by high-resolution transmission electron microscopy, Ultramicroscopy, 50, 321-354 (1993)
[4] Kisielowski, C.; Schwander, P.; Baumann, F. H.; Seibt, M.; Kim, Y.; Ourmazd, A., An approach to quantitative high resolution transmission electron microscopy of crystalline materials, Ultramicroscopy, 58, 131-155 (1995)
[5] Hillebrand, R., Fuzzy logic approaches to the analysis of HREM images of III-V compounds, Journal of Microscopy, 190, 61-72 (1998)
[6] R. Hillebrand, P.P. Wang, U. Gösele. Fuzzy logic applied to physics of III-V compounds, in: Proceedings of the Workshop on Breakthrough Opportunities for Fuzzy Logic, Tokyo, 1996, pp. 77-78; R. Hillebrand, P.P. Wang, U. Gösele. Fuzzy logic applied to physics of III-V compounds, in: Proceedings of the Workshop on Breakthrough Opportunities for Fuzzy Logic, Tokyo, 1996, pp. 77-78
[7] Hillebrand, R.; Wang, P. P.; Gösele, U., A fuzzy logic approach to edge detection in HREM images of III-V crystals, Information Sciences - Applications, 93, 321-338 (1996)
[8] R. Hillebrand, P.P. Wang, U. Gösele, Fuzzy logic image processing applied to electron micrographs of semiconductors, in: P. Wang (ed.), Proceedings of the Third Joint Conference on Information Sciences‘97, Duke University, Durham, I, 1997, pp. 55-57; R. Hillebrand, P.P. Wang, U. Gösele, Fuzzy logic image processing applied to electron micrographs of semiconductors, in: P. Wang (ed.), Proceedings of the Third Joint Conference on Information Sciences‘97, Duke University, Durham, I, 1997, pp. 55-57
[9] H. Kirschner, R. Hillebrand, Neuronale Netze zur Kompositionsbestimmung von III-V Heterostrukturen in HREM Abbildungen, Optik (Suppl.) 1997, 74; H. Kirschner, R. Hillebrand, Neuronale Netze zur Kompositionsbestimmung von III-V Heterostrukturen in HREM Abbildungen, Optik (Suppl.) 1997, 74
[10] H. Kirschner, HREM-Bildanalyse von III-V-Halbleiter-Schichtstrukturen durch quantitativen Bildvergleich experiment – simulation, Master Thesis, Martin-Luther-Universität Halle-Wittenberg, January 2000; H. Kirschner, HREM-Bildanalyse von III-V-Halbleiter-Schichtstrukturen durch quantitativen Bildvergleich experiment – simulation, Master Thesis, Martin-Luther-Universität Halle-Wittenberg, January 2000
[11] Stadelmann, P. A., EMS - a software package for electron diffraction analysis and HREM image simulation in materials science, Ultramicroscopy, 21, 131-146 (1987)
[12] P.A. Stadelmann, Image calculation techniques, Technical report, EPFL Lausanne, 1995; P.A. Stadelmann, Image calculation techniques, Technical report, EPFL Lausanne, 1995
[13] Reimer, L., Transmission Electron Microscopy (1989), Springer: Springer Berlin
[14] M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, in: H. Ruspini (Ed.), Proceedings of the IEEE International Conference on Neural Networks, San Francisco, 1993, pp. 586-591; M. Riedmiller, H. Braun, A direct adaptive method for faster backpropagation learning: the RPROP algorithm, in: H. Ruspini (Ed.), Proceedings of the IEEE International Conference on Neural Networks, San Francisco, 1993, pp. 586-591
[15] H. Kirschner, Architekturabhängiges Lern- und Anpassungsverhalten bei Neuronalen Mehrschichtnetzen, Master Thesis, Institut für angewandte Physik der Universität Regensburg, 1997; H. Kirschner, Architekturabhängiges Lern- und Anpassungsverhalten bei Neuronalen Mehrschichtnetzen, Master Thesis, Institut für angewandte Physik der Universität Regensburg, 1997
[16] Jollife, I. T., Principal Component Analysis (1986), Springer: Springer New York
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