Session Index

S10. Metaverse Photonics

Poster Session II
Saturday, Dec. 2, 2023  13:30-16:30
Room: Building of Electrical Engineering (電機系館) (B1)

Manuscript ID.  0693
Paper No.  2023-SAT-P1002-P001
Pei-Xuan Cai Ray-tracing for Ghosting Analysis in a Near-eye Display with Holographic Curved Waveguide
Pei-Xuan Cai, Wen-Kei Lin, National Changhua University of Education (Taiwan); Shao-Kui Zhou, National Changhua University of Education (Taiwan), National Yang Ming Chiao Tung University (Taiwan); Wei-Chia Su, National Changhua University of Education (Taiwan)

An AR display is integrated with a full holographic curved waveguide combiner. Ghosting phenomena can occur in curved waveguides due to the path of the incident light. To design efficiently and to accurately calculate the dimensions of Volume Holographic Optical Elements (VHOE) to avoid such ghosting, it is necessary to establish a mathematical model. This model uses the ABCD matrix to calculate the position of total internal reflection each time when the light travels through the waveguide.

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Manuscript ID.  0840
Paper No.  2023-SAT-P1002-P002
Wan-Jhen Wu Color Breaking Effect of VHOEs in MR Displays
Wan-Jhen Wu, Yu-Chien Wang, National Central University (Taiwan); Wen-Kai Lin, National Changhua University of Education (Taiwan); Tsung-Hsun Yang, Yeh-Wei Yu, National Central University (Taiwan); Wei-Chia Su, National Changhua University of Education (Taiwan); Ching-Cherng Sun, National Central University (Taiwan), National Yang Ming Chao Tung University (Taiwan)

As applying volume holographic optical elements (VHOEs) on the light guide in the see-through in the mixed-reality (MR) glasses, the color breaking in different view angles has somehow become a serious drawback in the practical applications and induces urgent need to be solved. In this work, we extensively explored with the color breaking effect according to the wavelength --dependent and angle--dependent diffraction efficiency spectrum of VHOEs for MR displays. As considering the color metamerism, a certain possible illumination light sources for reduction of the color breaking within FOVs can then be concluded. Some corresponding experiments are also performed for verification.

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Manuscript ID.  0900
Paper No.  2023-SAT-P1002-P003
Wei-Chi Wu Field-of-view Extension Array of Near Eye Display
Wei-Chi Wu, Cheng-Chuan Liu, Yeh-Wei Yu, Ching-Cherng Sun, Tsung-Hsun Yang, National Central University (Taiwan)

This study presents the development of a near-eye display with a field-of-view extension array system. It is composed of three volume holographic optical elements and light guides. Due to the volume holographic optical elements' ability to record interference fringes, we move the position of the microscopic lens to create interference fringes with varying focal lengths. This creates different focal lengths of the volume holographic optical elements, thus generating varying levels of field-of-view magnification.

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Manuscript ID.  1007
Paper No.  2023-SAT-P1002-P004
Wing-Sing Cheung Automated Identification Using Transformer-Based Deep Learning
Wing-Sing Cheung, National Cheng Kung University (Taiwan); Min-Hsuan You, Chi-Yeh Chen, National Cheng Kung University (Taiwan); Yu-Hsun Chou, National Cheng Kung University (Taiwan)

Two-dimensional transition metal dichalcogenides (2D TMDCs) have emerged as promising semiconductor materials due to their remarkable properties, including an ultrathin atomic layer structure and unique valley-spin characteristics. These materials have the potential to revolutionize semiconductor technology beyond silicon. However, their production methods, such as chemical vapor deposition (CVD) and mechanical exfoliation, have limitations. To address these challenges, this study explores the application of deep learning, specifically a Transformer model, for the automated identification of monolayer 2D TMDCs. The novel approach leverages semantic representations to enhance accuracy and adaptability, providing a promising solution for the mass production of high-quality 2D TMDCs.

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Manuscript ID.  0966
Paper No.  2023-SAT-P1002-P005
Tzu-Yuan Lin Wide-angle metalens with high angular resolution
Tzu-Yuan Lin, Po-Sheng Huang, Pin Chieh Wu, National Cheng Kung University (Taiwan)

A metalens is an ultrathin, flat lens made from metasurface that manipulates light and offers compactness and aberration correction. Though there has not been any studies on the metalens disclosure for optical distortion optimization. Here, we propose that it can be addressed by co-optimizing the distortion and the optical performance (MTF/focusing efficiency) in the wide-angle metalens(field of view 160°). A single-element wide-angle metalens with correction of f-theta distortion will be particularly optimized. It assists in enlarging the pixels per degree (PPD) at the image corner.

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Manuscript ID.  1006
Paper No.  2023-SAT-P1002-P006
Ting-Wei Huang A Polarization Switching Liquid Crystal Lens
Ting-Wei Huang, Wei-Hsiang Jen, Yu-Yang Wei, Xin-Cheng Lin, Yi-Hsin Lin, National Yang Ming Chiao Tung University (Taiwan)

In previous publication, we design a liquid crystal (LC) lens set consisting of three polarization switching LC lenses for varifocal images and vision corrections with fast response time. We demonstrate AR and VR systems by adopting the LC lens set to solve VAC as well as vision problem. However, there is some scattering at the edge of the lens due to the excessive thickness. It leads to a decrease in the effective aperture size and field of view. For increasing the aperture size, we conducted experiments to optimize the polarization-switching LC lens.

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Manuscript ID.  0951
Paper No.  2023-SAT-P1002-P007
Yu-Ting Yang Automatic detection of 2D material using deep-learning
Yu-Ting Yang, Yu-Hsun Chou, National Cheng Kung University (Taiwan)

Recent advances in deep learning have opened up exciting possibilities in exploring 2D materials for photonic devices, known for their unique light emission and modulation properties. However, the manual identification process using optical microscopy is time-consuming. To address this, we leveraged the power of deep learning with Mask-RCNN, a state-of-the-art AI model. Training on a curated dataset of 100 labeled 2D material images, we obtained encouraging results. This automated approach offers an efficient solution, eliminating labor-intensive inspections and accelerating the development of 2D materials in photonics applications.

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