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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Tseng, Ming Lun Semmlinger, Michael Zhang, Ming Arndt, Catherine Huang, Tzu-Ting Yang, Jian Kuo, Hsin Yu Su, Vin-Cent Chen, Mu Ku Chu, Cheng Hung Cerjan, Benjamin Tsai, Din Ping Nordlander, Peter Halas, Naomi J. Institute of Electronics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Research Center for Applied Sciences Academia Sinica Taipei115 Taiwan Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Laboratory for Nanophotonics Rice University HoustonTX77005 United States Applied Physics Graduate Program Rice University HoustonTX77005 United States Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Physics National Taiwan University Taipei10617 Taiwan Department of Electrical Engineering National United University Miaoli36003 Taiwan Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong Department of Chemistry Rice University HoustonTX77005 United States

Vacuum ultraviolet (VUV) light is of great importance to many applications such as photochemistry and nanolithography. In this talk, a nonlinear metalens composed of ZnO nanoantennas for VUV generation and focusing wi...

ISBN: (纸本)9798350350012

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Vacuum Ultraviolet Light-Generating Metalens

Vacuum Ultraviolet Light-Generating Metalens

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2022 International Conference on Optics-Photonics Design and Fabrication, ODF 2022

关键词： Nanoclay

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Intrinsic Parameter Fluctuation and Process Variation Effect of Vertically Stacked Silicon Nanosheet Complementary Field-Effect Transistors

Intrinsic Parameter Fluctuation and Process Variation Effect...

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IEEE International Symposium on Quality Electronic Design

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Parallel and Scientific Computing Laboratory Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program Hsinchu Taiwan Institute of Communications Engineering Hsinchu Taiwan Institute of Biomedical Engineering Hsinchu Taiwan Institute of Pioneer Semiconductor Innovation Hsinchu Taiwan Institute of Artificial Intelligence Innovation Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan

We study the variability of vertically stacked gate-all-around silicon nanosheet (GAA Si NS) complementary field-effect transistors (CFETs). The process variation effect (PVE), the work function fluctuation (WKF), and the random dopant fluctuation (RDF) of CFETs are statistically estimated using an experimentally validated device simulation technique. Among five factors of PVE, the channel thickness (T Nch /T Pch ), the channel width (W ch ), and the gate length (L G ) are significant. Owing to superior GAA channel control and increased effective gate area, both WKF and RDF are suppressed. Notably, the PVE on both N-/P-FETs of GAA Si CFET induce the largest off-state current fluctuations of 80% and 278%, respectively, because the device characteristic is very sensitive to the layer thickness and width of channel.

关键词： Fluctuations Gallium arsenide Logic gates Electric variables Silicon Resource description framework Semiconductor process modeling

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Multimodal deep learning framework for image popularity prediction on social media

arXiv

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arXiv 2021年

作者： Abousaleh, Fatma S. Cheng, Wen-Huang Yu, Neng-Hao Tsao, Yu Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Science Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan Department of Computer Science National Chengchi University Taipei11605 Taiwan The Institute of Electronics Department of Electronics Engineering National Chiao Tung University Hsinchu30010 Taiwan The College of Design Department of Design National Taiwan University of Science and Technology Taipei10607 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan

Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by several factors, such as visual content, aesthetic quality, user, post metadata, and time. Thus, considering all these factors is essential for accurately predicting image popularity. In addition, the efficiency of the predictive model also plays a crucial role. In this study, motivated by multimodal learning, which uses information from various modalities, and the current success of convolutional neural networks (CNNs) in various fields, we propose a deep learning model, called visual-social convolutional neural network (VSCNN), which predicts the popularity of a posted image by incorporating various types of visual and social features into a unified network model. VSCNN first learns to extract high-level representations from the input visual and social features by utilizing two individual CNNs. The outputs of these two networks are then fused into a joint network to estimate the popularity score in the output layer. We assess the performance of the proposed method by conducting extensive experiments on a dataset of approximately 432K images posted on Flickr. The simulation results demonstrate that the proposed VSCNN model significantly outperforms state-of-the-art models, with a relative improvement of greater than 2.33%, 7.59%, and 14.16% in terms of Spearman’s Rho, mean absolute error, and mean squared error, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Social networking (online)

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Performance comparison of InGaN-based 40–80 μm micro-LEDs fabricated with and without plasma etching

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Materials Today Advances 2024年 22卷

作者： Lo, Yu-Yun Chen, Yi-Ho Hsu, Yun-Cheng Lee, Tzu-Yi Hung, Yu-Ying Kao, Yu-Cheng Zan, Hsiao-Wen Wuu, Dong- Sing Kuo, Hao-Chung Samukawa, Seiji Horng, Ray-Hua Institute of Electronics National Yang Ming Chiao Tung University 1001 University Road Hsinchu30010 Taiwan Department of Photonics College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Materials Science and Engineering National Chung Hsing University Taichung40227 Taiwan Department of Applied Materials and Optoelectronic Engineering National Chi Nan University Nantou54561 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

The fabrication of InGaN-based blue 4✕4 array micro-LEDs (μLEDs) with 40 μm ✕40 μm chip size and 2✕2 array μLEDs with 80 μm ✕80 μm chip size etching by the inductive coupled plasma reactive ion etching (ICPRIE) and defect-free neutral beam etching (NBE) processes was studied in this work. In μLEDs of this size, the influence of defects formation in the sidewalls on EQE was evaluated. There was almost no difference in EQE between μLEDs array etched by the NBE process no matter 40 μm ✕40 μm and 80 μm ✕80 μm, but the dependence was observed in the ICPRIE. Even with this size, it was found that the size effect of EQE is smaller than that in case of using ICPRIE for defect-free neutral beam etching. This impact is substantial since μLED predominantly operated at low current density, around 1–5 A/cm2. Consequently, the reduction of defect density, encompassing both internal and sidewall defects, becomes imperative even in 40–80 μm InGaN-based μLEDs. This not only improves the overall efficiency of μLEDs but also fortifies the brightness stability of μLED displays if process etching by NBE. It was also found that the etching shape had an influence on EQE. It could be attributed to fact that the etching profile angle of NBE was more vertical than that of ICPRIE. Because the different angles of the mesa resulted in different light intensity. The μLEDs emitting with a wavelength of 450 nm, the light extraction efficiency and intensity at a mesa angle 58° of NBE etching μLEDs was about 8% lower than those of an angle (38°) of ICPRIE etching μLEDs by simulation. © The Authors

关键词： Light emitting diodes

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Chromosome Classification for Metaphase Selection

Chromosome Classification for Metaphase Selection

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International Symposium on Communications and Information Technologies

作者： Ravi Uttamatanin Peerapol Yuvapoositanon Apichart Intarapanich Saowaluck Kaewkamnerd Sissades Tongsima The Electrical Engineering Graduate Program Faculty of Engineering Mahanakorn University of Technology Bangkok Thailand Electronic Engineering Department Mahanakorn University of Technology Bangkok Thailand National Electronics and Computer Technology Center Pathumtani Thailand National Center for Genetic Engineering and Biotechnology Pathumtani Thailand

ISBN: (纸本)9781467355797

Identification of good metaphase spread is an important step in chromosome analysis for genetic disorder detection. In this paper, we propose a rule for chromosome classification to identify good metaphase spreads. The chromosome shapes were classified into four main classes. The first and the second classes refer to individual chromosomes with straight and bended shapes, respectively. The third class is characterized as those chromosomes with overlapping bodies and the forth class is for the non-chromosomal artifacts. Good metaphase spreads should largely contain the first and the second classes while the number of the third class should be kept minimal. Several image parameters were examined and used for creating rule-based classification. The threshold value for each parameter is determined using statistical model. We observed that the empirical probability density function of the parameters can be represented by Gaussian model and, hence, the threshold value can be easily determined. The proposed rules can efficiently and accurately classify the individual chromosome with > 90% accuracy.

关键词： Chromosome Metaphase selection Metaphase spread Rule base classification Gaussian model

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Effects of Hydrogen on Endurance Characteristics in NAND Flash Memories

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ECS Advances 2022年第4期1卷 042001-042001页

作者： Chiu, Yung-Yueh Liu, Wen-Chien Chen, Yu-Jung Kumaria, Roshni Takeshita, Toshiaki Yano, Masaru Shirota, Riichiro Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan College of Semiconductor Research National Tsing Hua University Hsinchu 300 Taiwan EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Winbond Electronics Corporation Kanagawa 222-0033 Japan

This study comprehensively investigates the effect of hydrogen content in forming gas annealing (FGA) on the endurance of NAND flash memories by statistically analyzing the transconductance (G_m,max) characteristics. The G_m,max degradation (ΔG_m,max) worsened with higher cycling temperature, delayed time period from erasing to programming (t_EP) operation, and higher word-line bias (V_A) during t_EP. Moreover, these effects become more pronounced as the hydrogen content in FGA increases. Using the measured G_m,max distributions and Monte -Carlo TCAD technology, the activation energy (E_A) of oxide damage creation can be extracted. The extracted E_A values were 50 meV and 160 meV for the diluted (4%) and pure (100%) hydrogen samples, respectively. This suggests that a higher hydrogen concentration results in more ionized hydrogen atoms remaining in the oxide layer. During t_EP, these hydrogen ions can drift near the Si/SiO₂ surface, where they may react to form trap states. E_A is revealed to have a linear relationship with respect to V_A, where the slope is independent of the hydrogen content in FGA. © 2022 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited.

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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

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European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, computer Retrospective Study

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Coherently Enhanced Wireless Power Transfer

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Physical Review Letters 2018年第14期120卷 143901-143901页

作者： Alex Krasnok Denis G. Baranov Andrey Generalov Sergey Li Andrea Alù Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 USA Department of Physics Chalmers University of Technology 412 96 Gothenburg Sweden Moscow Institute of Physics and Technology Dolgoprudny 141700 Russia Department of Electronics and Nanoengineering Aalto University 02150 Espoo Finland ITMO University St. Petersburg 197101 Russia Photonics Initiative Advanced Science Research Center City University of New York New York 10031 USA Physics Program Graduate Center City University of New York New York 10016 USA Department of Electrical Engineering City College of The City University of New York New York 10031 USA

Extraction of electromagnetic energy by an antenna from impinging external radiation is at the basis of wireless communications and wireless power transfer (WPT). The maximum of transferred energy is ensured when the antenna is conjugately matched, i.e., when it is resonant and it has an equal coupling with free space and its load. This condition, however, can be easily affected by changes in the environment, preventing optimal operation of a WPT system. Here, we introduce the concept of coherently enhanced WPT that allows us to bypass this difficulty and achieve dynamic control of power transfer. The approach relies on coherent excitation of the waveguide connected to the antenna load with a backward propagating signal of specific amplitude and phase. This signal creates a suitable interference pattern at the load resulting in a modification of the local wave impedance, which in turn enables conjugate matching and a largely increased amount of extracted energy. We develop a simple theoretical model describing this concept, demonstrate it with full-wave numerical simulations for the canonical example of a dipole antenna, and verify experimentally in both near-field and far-field regimes.

关键词： Electromagnetism engineering Light propagation, transmission & absorption Photonics

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