Sze Ming Fu
National Chiao Tung University
17 Papers
85 Citations
Sze Ming Fu is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Metamaterial & Metamaterial absorber. The author has an hindex of 7, co-authored 17 publications.
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Papers
Fully Planarized Perfect Metamaterial Absorbers With No Photonic Nanostructures
TL;DR: In this paper, the authors proposed a planarized design with ultrathin metallic films for broadband metamaterial absorbers, which can be used with different moderate-extinction metals such as tungsten, titanium, tantalum, and nickel.
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A fully functionalized metamaterial perfect absorber with simple design and implementation.
TL;DR: In this work, one-dimensional (1D) planar stacking structure is designed to achieve the ultimate goal of a functionalized absorber with a fully tailorable spectral absorption, andBand-rejected, high-pass, low-pass and band-pass structure are constructed successfully.
14
An ultra-compact blackbody using electrophoretic deposited carbon nanotube films
Albert Lin,Chien Chih Yang,Parag Parashar,Chien Yung Lin,Ding Rung Jian,Wei Ming Huang,Yi-Wen Huang,Sze Ming Fu,Yan Kai Zhong,Tseung-Yuen Tseng +9 more
TL;DR: In this paper, an electrophoretic deposited (EPD) CNT resonant cavity structure on tantalum (Ta) was proposed to enhance optical absorption, and the proposed EPD-CNT film was used for sensing, antenna, and thermophotovoltaics (TPV) applications.
Lithographically fabricable, optimized three-dimensional solar cell random structure
TL;DR: In this article, a binary random grating is proposed which can be easily fabricated using common lithographic techniques, and the solar cell structure with 4 4 quasi-random binary grating can provide 23% higher integrated absorbance than its periodic grating counterpart.
9
Analytics-statistics mixed training and its fitness to semisupervised manufacturing.
Parag Parashar,Chun Han Chen,Chandni Akbar,Sze Ming Fu,Tejender Singh Rawat,Sparsh Pratik,Rajat Butola,Shih Han Chen,Albert Lin +8 more
TL;DR: It is shown that the mean square error (MSE) can be effectively decreased when the analytics-statistics mixed training (ASMT) scheme is used instead of the classic neural network (NN) used in the baseline study.