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HREM Image Analysis of III–V Heterostructures Based on Neural Networks
Author(s) -
Hillebrand R.,
Kirschner H.,
Werner P.,
Gösele U.
Publication year - 2000
Publication title -
physica status solidi (b)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.51
H-Index - 109
eISSN - 1521-3951
pISSN - 0370-1972
DOI - 10.1002/1521-3951(200011)222:1<185::aid-pssb185>3.0.co;2-m
Subject(s) - micrograph , heterojunction , amorphous solid , materials science , artificial neural network , electron micrographs , interpretation (philosophy) , chemistry , optoelectronics , physics , optics , crystallography , electron microscope , scanning electron microscope , computer science , artificial intelligence , composite material , programming language
Abstract The quantitative interpretation of high resolution electron micrographs requires image processing. We present a new neural network (NN) based method of determining the local composition and thickness in micrographs of III–V semiconductors. The NNs are trained with simulated HREM patterns of AlGaAs including an appropriate amorphous layer modelling. By comparing simulated and experimental images, we can estimate the amorphization depth of the samples. Neural networks suppress the correlated noise from amorphous surface layers and distinguish between variations of the sample thickness and the semiconductor composition.