83‐3: Inverse Design of Liquid Crystal Phase Modulators for 2D/3D Switchable Display Based on Deep Learning

The fringe field effect in liquid crystal (LC) devices can cause pixel crosstalk issues. Therefore, LC devices with multiple electrode structures require multiple calibrations to achieve an approximate distribution of the target phase modulation. At the same time, optical inverse design based on dat...

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Vydáno v:SID International Symposium Digest of technical papers Ročník 55; číslo 1; s. 1159 - 1162
Hlavní autoři: Chen, Qian, Tang, Mingyuan, Sun, Changli, Lu, Jiangang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Campbell Wiley Subscription Services, Inc 01.06.2024
Témata:
Algorithms
Crosstalk
Deep learning
Display devices
fringe field effect
Inverse design
inverse phase modulation
liquid crystal device
Liquid crystals
Machine learning
Phase modulation
ISSN:0097-966X, 2168-0159
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Shrnutí:The fringe field effect in liquid crystal (LC) devices can cause pixel crosstalk issues. Therefore, LC devices with multiple electrode structures require multiple calibrations to achieve an approximate distribution of the target phase modulation. At the same time, optical inverse design based on data‐driven algorithms is also a highly popular topic. This article introduces a deep‐learning approach to realize inverse phase modulation in LC devices. By adopting the deep learning method for inverse phase modulation in LC devices, can effectively alleviate the problems caused by fringe field effects and achieve more precise and accurate phase modulation distribution.
Bibliografie:ObjectType-Article-1
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ISSN:0097-966X
2168-0159
DOI:10.1002/sdtp.17745