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Machine learning aided phase retrieval algorithm for beam splitting with an LCoS-SLM

Mikhaylov, Dmitriy; Zhou, Baifan; Kiedrowski, Thomas; Mikut, Ralf; Lasagni, Andrés Fabián

Abstract (englisch):
Liquid crystal on silicon phase-only spatial light modulators are widely used for the generation of multi-spot patterns. The phase distribution in the modulator plane, corresponding to the target multi-spot intensity distribution in the focal plane, is calculated by means of the so-called phase retrieval algorithms. Due to deviations of the real optical setup from the ideal model, these algorithms often do not achieve the desired power distribution accuracy within the multi-spot patterns. In this study, we present a novel method for generating high quality multi-spot patterns even in the presence of optical system disturbances. The standard Iterative Fourier Transform Algorithm is extended by means of machine learning methods combined with an open camera feedback loop. The machine learning algorithm is used to predict the mapping function between the desired and the measured multi-spot beam profiles. The problem of generation of multispot patterns is divided into three complexity levels. Due to distinct parameter structures, each of the complexity levels requires differing solution approaches, particularly differing machine learning ... mehr



Originalveröffentlichung
DOI: 10.1117/12.2508673
Seitenaufrufe: 3
seit 31.03.2019
Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Proceedingsbeitrag
Jahr 2019
Sprache Englisch
Identifikator ISBN: 978-1-5106-2451-1
KITopen-ID: 1000092792
HGF-Programm 47.01.02 (POF III, LK 01)
Erschienen in Laser Resonators, Microresonators, and Beam Control XXI. Ed.: A.V. Kudryashov
Verlag SPIE, Bellingsham, WA
Seiten 10 S.
Serie Proceedings of SPIE ; 10904
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