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Nanocrystal-based optical diffusers for white LED lighting: inverse design achieved by machine learning technologies
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- Author / Creator
- Li, Gangyi
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Illumination receives a great deal of attention as white lighting-emitting diodes (WLEDs) become energy-efficient light sources in households and commercial buildings, on streets and highways, and at stadiums and construction sites. In general, lenses and mirrors are used to control the spatial distribution of WLED light. In this thesis, we propose to use optical diffuser, the key optical device in scattering optics, to achieve the desired figures of merit for WLEDs. Optical diffusers are typically used to create soft light (similar brightness from any angle of view), however, here we can alter the concentration of nanocrystals in the nanocomposite film (optical diffuser) to control its optical property. Machine learning is employed to achieve the inverse design of the optical diffuser pattern on WLEDs, and this design task is beyond human capacities which are generally carried out using the brute force approach (solving a problem through exhaustion). In this thesis work, we have constructed the neural network architectures for machine learning and used them to achieve the inverse design of a symmetric pattern of optical diffusers (two-dimensional design) on WLED modules. Furthermore, we focus on achieving the inverse design of non-symmetric patterns of optical diffusers (three-dimensional design), and several pre-defined patterns of WLED light intensity are demonstrated for showing the success of our efforts.
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- Subjects / Keywords
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- Graduation date
- Spring 2023
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.