Machine learning phase modulation of liquid crystal devices for three-dimensional display
Optics Express, ISSN: 1094-4087, Vol: 31, Issue: 12, Page: 19675-19685
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Article Description
A machine learning phase modulation scheme based on convolutional neural networks (CNN) and recurrent neural network (RNN) is proposed to carry out the regression task of liquid crystal (LC) device electric field prediction for the 2D/3D switchable display. The hybrid neural network is built and trained based on the illuminance distribution under three-dimensional (3D) display. Compared with manual phase modulation, the modulation method using a hybrid neural network can achieve higher optical efficiency and lower crosstalk in the 3D display. The validity of the proposed method is confirmed through simulations and optical experiments.
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