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Combining gradient ascent search and support vector machines for effective autofocus of a field emission–scanning electron microscope
Author(s) -
DEMBÉLÉ S.,
LEHMANN O.,
MEDJAHER K.,
MARTURI N.,
PIAT N.
Publication year - 2016
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12419
Subject(s) - autofocus , focus (optics) , magnification , benchmark (surveying) , support vector machine , computer science , artificial intelligence , optics , vector field , algorithm , computer vision , physics , mathematics , geometry , geology , geodesy
Summary Autofocus is an important issue in electron microscopy, particularly at high magnification. It consists in searching for sharp image of a specimen, that is corresponding to the peak of focus. The paper presents a machine learning solution to this issue. From seven focus measures, support vector machines fitting is used to compute the peak with an initial guess obtained from a gradient ascent search, that is search in the direction of higher gradient of focus. The solution is implemented on a Carl Zeiss Auriga FE‐SEM with a three benchmark specimen and magnification ranging from x300 to x160 000. Based on regularized nonlinear least squares optimization, the solution overtakes the literature nonregularized search and Fibonacci search methods: accuracy improvement ranges from 1.25 to 8 times, fidelity improvement ranges from 1.6 to 28 times, and speed improvement ranges from 1.5 to 4 times. Moreover, the solution is practical by requiring only an off‐line easy automatic train with cross‐validation of the support vector machines.