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Learning With MOOCS (LWMOOCS)

作者： Felix Garcia Loro Rosario Gil Elio Sancristobal Pedro Plaza Merino Blanca Quintana Sergio Martin Oscar Casanova-Carvajal Inma Miralles Dimitar Tokmakov Slava Malenkova Tzanova Slavka Tzanova Russ Meier Manuel Castro Electrical and Computer Engineering Department Universidad Nacional de Educacion a Distancia Madrid Spain ANCCP & Centro de Tecnologí Biomédica (CTB) Campus Montegancedo Universidad Politécnica de Madrid (UPM) Spain ANCCP Barcelona Spain Plovdiv University Plovdiv Bulgaria Department of Microelectronics Technical University of Sofia Sofia Bulgaria Milwaukee School of Engineering Milwaukee USA Electrical and Computer Engineering Dept UNED Madrid Spain

Professional on-line courses are the way to increase skills, competences and knowledge inside our marketplace workers and might be one of the key actions to promote an increase and engagement on the people interest in a new field, like microelectronics, allowing them to adapt some more sustainable vision, greener electronics technology, and a more diverse, equity and much more better integration people in this workforce. Recent European Union actions like the Chip Act, [1], as a coordinated answer to the shortage and dependence of the European Union on the foreign technology and microelectronics fabrication aggravated by the Ukrainian war put their outcomes from this project as a reference for future actions on technical VET inside the Europe Union. Early pilot results show that at this moment the students that are doing part of the pilot have less time that should be necessary to follow the curricula as well as probably they are not interested in all the activities and tasks of the pilot courses offered.

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Multi-Task Deep Learning Model for Autonomous Driving: Object Detection, Semantic Segmentation, and depth Estimation

Multi-Task Deep Learning Model for Autonomous Driving: Objec...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Yih-Chen Wang Jhe-Li Lin Yen-Lin Chen Chieh-Sheng Huang Ming-Liang Lai Dept. of Computer Science & Information Engineering National Taipei University of Technology Taipei Taiwan Artificial Intelligence Research and Development Department ELAN Microelectronics Corporation Hsinchu Taiwan Graduate Institute of Intellectual Property National Taipei University of Technology Taipei Taiwan

In the field of autonomous driving, many models based on deep learning methods have been constructed to solve computer vision tasks related to this domain, such as object detection, semantic segmentation, and depth estimation. Each model can provide different information about the surrounding environment of a vehicle to assist in driving. However, for applying in the real world, more detailed information about the surrounding environment is essentially required. In this study, we propose a model based on the concept of multi-task learning. This model is an encoder-decoder architecture mainly consisting of an encoder using the hard-parameter sharing technique and three decoders for individual tasks. Therefore, this model can handle object detection, semantic segmentation, and depth estimation at the same time. Our proposed multi-task model has been verified to perform well on the public dataset Cityscapes and has higher generalizability than other models.

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RF Performance of FOWLP to PCB Board Transition

RF Performance of FOWLP to PCB Board Transition

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Electronics Packaging Technology Conference (EPTC)

作者： K. B. Zheng Sun Mei Sharon Lim Pei Siang Jong Ming Chinq Lau Boon Long Zhou Lin Lim Teck Guan IME attached student from Dept. of Electrical and Computer Engineering National University of Singapore (NUS) Innovis Tower Singapore Institute of Microelectronics (IME) A*STAR (Agency for Science Technology and Research)

ISBN: (纸本)9798350398861

Fan-Out Wafer-Level-Packaging (FOWLP) to PCB transition enables the scaling of an antenna element into an array at a PCB level. This paper presents key design steps ensuring the FOWLP package-to-board transition structure's performance. The back-to-back package-to-board transition structure has a maximum simulated insertion loss of 0.88 dB and a minimum return loss of 11.73 dB over 20 to 35 GHz. A prototype was also fabricated for further characterization.

关键词： Radio frequency 5G mobile communication Wireless networks System performance Web and internet services Prototypes Insertion loss

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INCL: An Effective Deep Learning Model to Identify Road Potholes by Using Image Based Neural Classification Logic

INCL: An Effective Deep Learning Model to Identify Road Poth...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Y Shasikala M.Rajitha Kantha M. Dinesh K. Nagamani Bonde A AfanehSuha Departmentof Computer Applications AdityaUniversity Surampalem Department of S&H QIS College of Engineering and Technology Pondur Road Vengamukkapalem Ongole Prakasam India Dept of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Technical Sciences (SIMATS) Chennai Department of MBA QIS College of Engineering and Technology Pondur Road Vengamukkapalem Ongole Prakasam India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan Universiry of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This paper presents a deep learning-based approach for detecting road potholes using image-based neural classification logic. The methodology involves collecting road images from publicly available datasets, particularly from Kaggle, and augmenting the dataset to improve model performance. Preprocessing steps such as resizing, standardizing, and annotating the images were employed to ensure consistency. A pre-trained Faster R-CNN model was fine-tuned for pothole detection, using convolutional neural networks (CNNs) to extract features and classify the road images into pothole and non-pothole categories. The model was trained using transfer learning and tested under varying road and environmental conditions, including day, night, dry, and wet scenarios. The model achieved an accuracy of 96.5% in dry daylight conditions, with a slightly lower performance of 85.4% during wet nighttime conditions. Additionally, precision and recall were evaluated, with the model achieving a precision of 95.3% and a recall of 97.6% for daytime conditions. The system was implemented in a real-time environment, allowing for real-time pothole detection and reporting through a mobile or web application. Future work will aim to improve performance under challenging conditions and enhance real-time deployment efficiency.

关键词： Deep learning Roads Computational modeling Transfer learning Feature extraction Real-time systems Maintenance Logic Convolutional neural networks Image classification

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Hot Carrier Injection Reliability of Fabricated N- and P-Type Multi FinFETs with Different TiN Stacks

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ECS Journal of Solid State Science and Technology 2023年第3期12卷 035007-035007页

作者： Chen, Yu-Lin Yeh, Wen-Kuan Hsu, Heng-Tung Chen, Ke-Horng Lin, Wen-Chin Yu, Tien-Han Chou, Hung-Ting Godwin Raj, D. Godfrey, D. International College of Semiconductor Technology National Yang Ming Chiao Tung University Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan National Applied Research Laboratories Taiwan Institute of Microelectronics National Cheng Kung University Taiwan National Chung-Shan Institute of Science and Technology Taoyuan Taiwan Dept of ECE Amal Jyothi College of Engineering Kerala India Dept of Electronics and Communication Engineering Dayananda Sagar University Bengaluru India

Device degradation due to hot carrier injection (HCI) in multi-fin 20 nm and 10 nm N- and P-type FinFET devices are thoroughly analyzed. To further understand the HCI reliability of the four FinFET devices, the device is fabricated with a standard Vt base and low Vt base gate stacks with different work functions. It is evident that: (i) The standard Vt device sustains lower effective stress bias due to the difference in threshold voltage, resulting in a more stable threshold voltage than the low Vt base device, and (ii) the transconductance of the single N- and P-type FinFET is more severely degraded than the multi-fin N- and P-type FinFET, mainly because multi N- and P-type Finfet has coupe effect, which effectively reduces the impact of HCI. © 2023 The Electrochemical Society ("ECS"). Published on behalf of ECS by IOP Publishing Limited

关键词： FinFET

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Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning

arXiv

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arXiv 2023年

作者： Zhai, Shengfang Dong, Yinpeng Shen, Qingni Pu, Shi Fang, Yuejian Su, Hang School of Software and Microelectronics Peking University China Department of Computer Science & Technology Tsinghua University China ShengShu Beijing China School of Software and Microelectronics National Engineering Research Center for Software Engineering PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing100871 China Dept. of Comp. Sci. and Tech. Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Beijing100084 China

With the help of conditioning mechanisms, the state-of-the-art diffusion models have achieved tremendous success in guided image generation, particularly in text-to-image synthesis. To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image diffusion models and propose BadT2I, a general multimodal backdoor attack framework that tampers with image synthesis in diverse semantic levels. Specifically, we perform backdoor attacks on three levels of the vision semantics: Pixel-Backdoor, Object-Backdoor and Style-Backdoor. By utilizing a regularization loss, our methods efficiently inject backdoors into a large-scale text-to-image diffusion model while preserving its utility with benign inputs. We conduct empirical experiments on Stable Diffusion, the widely-used text-to-image diffusion model, demonstrating that the large-scale diffusion model can be easily backdoored within a few fine-tuning steps. We conduct additional experiments to explore the impact of different types of textual triggers, as well as the backdoor persistence during further training, providing insights for the development of backdoor defense methods. Besides, our investigation may contribute to the copyright protection of text-to-image models in the future. Our Code: https://***/sf-zhai/BadT2I. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Photonic Crystal Back Reflectors for Enhanced Absorption in Amorphous Silicon Solar Cells

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MRS Online Proceedings Library 2021年第1期1245卷 1-6页

作者： Benjamin Curtin Rana Biswas Vikram Dalal Microelectronics Research Center Dept. of Electrical and Computer Engineering Iowa State University Ames USA Physics & Astronomy Ames Lab Iowa State University Ames USA

Photonic crystal back reflectors offer enhanced optical absorption in thin-film solar cells, without undesirable losses. Rigorous simulations of photonic crystal back reflectors predicted maximized light absorption in amorphous silicon solar cells for a pitch of 700-800 nm. Simulations also predict that for typical 250 nm i-layer cells, the periodic photonic crystal back reflector can improve absorption over the ideal randomly roughened back reflector (or the ‘4n2 classical limit’) at wavelengths near the band edge. The PC back reflector provides even higher enhancement than roughened back reflectors for cells with even thinner i-layers. Using these simulated designs, we fabricated metallic photonic crystal back reflectors with different etch depths and i-layer thicknesses. The photonic crystals had a pitch of 760 nm and triangular lattice symmetry. The average light absorption increased with the PC back reflectors, but the greatest improvement (7-8%) in short circuit current was found for thinner i-layers. We have studied the dependence of cell performance on the etch depth of the photonic crystal. The photonic crystal back reflector strongly diffracts light and increases optical path lengths of solar photons.

关键词： amorphous Si absorption

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Dynamic Vth in p-GaN Gate Power HEMTs and Its Impacts upon Power Switching Circuits 15

Dynamic Vth in p-GaN Gate Power HEMTs and Its Impacts upon P...

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15th IEEE International Conference on Solid-State and Integrated Circuit Technology, ICSICT 2020

作者： Wei, Jin Xu, Han Xie, Ruiliang Chen, Kevin J. Institute of Microelectronics Peking University Beijing100871 China Hong Kong University of Science and Technology Dept. of Electronic and Computer Engineering Hong Kong Hong Kong

ISBN: (纸本)9781728162355

The p-GaN gate power HEMT exhibits a dynamic threshold voltage (V-{\mathrm{th}}) during device operation, i.e. its V-{\mathrm{th}} is a mathematical function of the terminal bias. The dynamic V-{\mathrm{th}} phenomenon can be accurately predicted by a charge storage mechanism. The floating p-GaN layer stores negative charges during switching operating, and requires an additional gate bias to counteract these charges. The dynamic V-{\mathrm{th}} has profound impacts upon power switching circuits. It affects the dynamic on-state characteristics and reverse conduction characteristics, presents restriction on the design of gate driving circuit design, and influences the switching waveforms of the circuits. Detailed analysis of these effects will be presented. © 2020 IEEE.

关键词： Timing circuits

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Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network

arXiv

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arXiv 2022年

作者： Wang, Xiang Yang, Yimin Pang, Qixiang Lu, Xiao Liu, Yu Du, Shan Dept. of Computer Science Lakehead University Thunder BayON Canada Dept. of Electrical and Computer Engineering Western University LondonON Canada Okanagan College KelownaBC Canada College of Engineering and Design Hunan Normal University Hunan China School of Microelectronics Tianjin University Tianjin China Dept. of Comp. Sci. Math Physics & Statistics The University of British Columbia OkanaganBC Canada

Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned tiny LR face image to its HR counterpart with multiple ultra-upscaling factors (e.g., 4× and 8×). The proposed network is composed of a novel semantic encoder that has the ability to capture the embedded semantics to guide adversarial learning and a novel generator that uses a hierarchical architecture named Residual in Internal Dense Block (RIDB). Moreover, we propose a joint discriminator which discriminates both image data and embedded semantics. The joint discriminator learns the joint probability distribution of the image space and latent space. We also use a Relativistic average Least Squares loss (RaLS) as the adversarial loss to alleviate the gradient vanishing problem and enhance the stability of the training procedure. Extensive experiments on large face datasets have proved that the proposed method can achieve superior super-resolution results and significantly outperform other state-of-the-art methods in both qualitative and quantitative comparisons. © 2022, CC BY.

关键词： Semantics

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Nanocrystalline Si Films and Devices Produced Using Chemical Annealing with Helium

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MRS Online Proceedings Library 2020年第1期862卷 2061-2066页

作者： Nanlin Wang Vikram L. Dalal Dept. of Electrical and Computer Engineering and Microelectronics Research Center Iowa State University Ames USA

We report on growth of nanocrystalline Si:H films and devices using a layer-by-layer growth technique, where the growth of a thin amorphous layer by PECVD is followed by chemical annealing in a Helium plasma. The films and devices were grown using a remote, low pressure ECR plasma process. It was found that the structure of the films grown using the layer-by-layer technique depended critically upon whether the annealing was done with hydrogen or helium, and the time taken to do the annealing. When the annealing was done in a hydrogen plasma, the films remained amorphous; in contrast, when the annealing was done in helium, and the annealing time was increased to 20 seconds from 10 seconds, the films became crystalline. The crystallinity of the films was confirmed using Raman spectroscopy and x-ray diffraction. The result obtained here shows that it is not necessary to have a high hydrogen dilution to obtain nanocrystalline films. Rather, the amount of hydrogen already present in an amorphous film is enough to cause crystallization, provided that enough ion flux and perhaps energy are available for converting the amorphous structure to a crystalline structure. Proof - of - concept p+nn+ junction devices were fabricated in these chemically annealed materials, and they showed classical nanocrystalline Si solar cell type behavior.

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