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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Temperature-Dependent Hydrogen Modulations of Ultra-Scaled a-IGZO Thin Film Transistor Under Gate Bias Stress

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2024年 5卷 9-16页

作者： Aslam, Muhammad Chang, Shu-Wei Chuang, Min-Hui Chen, Yi-Ho Lee, Yao-Jen Li, Yiming Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical Engineering National Cheng Kung University Tainan701401 Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Recently, a-IGZO has advanced toward the next-generation electronics system because of its compatibility with complementary metal oxide semiconductor (CMOS) and back-end-of-line (BOEL) based systems. A systematic electrical characterization of a-IGZO TFT related to reliability issues, such as positive bias temperature stress (PBTS) and negative bias temperature stress (NBTS), would entitle its integration into novel electronics systems. Unexpectedly, PBTS is characterized by the transition of positive Vth shift to negative Vth shift (ΔVth, the positive shift followed by the stress and temperature activated negative shift). This transition is attributed to charge trapping/trap-site generations and hydrogen migration to the active layer. The ΔVth shift mechanism depends on the temperature and voltage stress. On the other hand, a negative ΔVth shift has been observed during the NBTS operation and could be attributed to the hole trapping at the interface of GI/IGZO. An effective suppression of the gate leakage current has also been observed during reliability tests. Simulation results reveal a pronounced potential at the edges of source and drain regions, and considered the origin of hydrogen migration into the IGZO layer. Thermal image results also reveal the strong temperature/potential distribution at the edges of the source/drain regions, indorsing the simulation results. © 2020 IEEE.

关键词： Oxide semiconductors

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Privacy-Aware and AI Techniques for Healthcare Based on K-Anonymity Model in Internet of Things

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IEEE Transactions on engineering Management 2024年 71卷 12448-12462页

作者： Sangaiah, Arun Kumar Javadpour, Amir Ja'fari, Forough Pinto, Pedro Chuang, Huan-Ming National Yunlin University of Science and Technology International Graduate Institute of Artificial Intelligence Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon Harbin Institute of Technology Department of Computer Science and Technology Shenzhen150001 China Instituto Politécnico de Viana Do Castelo ADiT-Lab Electrotechnics and Telecommunications Department Porto4900-347 Portugal Sharif University of Technology Department of Computer Engineering Tehran11155-9161 Iran National Yunlin University of Science and Technology Department of Information Management Yunlin640 Taiwan

The government and industry have given the recent development of the Internet of Things in the healthcare sector significant respect. Health service providers retain data gathered from many sources and are useful for patient diagnostics and research for pivotal analysis. However, sensitive personal information about a person is contained in healthcare data, which must be protected. Individual privacy protection is a crucial concern for both people and organizations, particularly when those firms must send user data to data centers due to data mining. This article investigated two general states of increasing entropy by changing the entropy of the class set of characteristics based on artificial intelligence and the k-anonymity model in privacy in context, and also three different strategies have been investigated, i.e., the strategy of 'selecting the feature with the lowest number of distinct values,' 'selecting the feature with the lowest entropy,' and 'selecting the feature with the highest entropy.' For future tasks, we can find an optimal strategy that can help us to achieve optimal entropy in the least possible repetition. The results of our work have been compared by lightweight and MH-Internet of Things, FRUIT methods and shown that the proposed method has high efficiency in entropy criteria. © 1988-2012 IEEE.

关键词： Data mining

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Experimental Evaluation of Covariates Effects on Periocular Biometrics: A Robust Security Assessment Framework

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Journal of Data and Information Quality 2023年第2期15卷 1-25页

作者： Kumar, Gautam Bakshi, Sambit Sangaiah, Arun Kumar Sa, Pankaj Kumar National Institute of Technology Rourkela Sector II Odisha Rourkela769 008 India International Graduate Institute of Ai National Yunlin University of Science and Technology Taiwan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

The growing integration of technology into our lives has resulted in unprecedented amounts of data that are being exchanged among devices in an Internet of Things (IoT) environment. Authentication, identification, and device heterogeneities are major security and privacy concerns in IoT. One of the most effective solutions to avoid unauthorized access to sensitive information is biometrics. Deep learning-based biometric systems have been proven to outperform traditional image processing and machine learning techniques. However, the image quality covariates associated with blur, resolution, illumination, and noise predominantly affect recognition performance. Therefore, assessing the robustness of the developed solution is another important concern that still needs to be investigated. This article proposes a periocular region-based biometric system and explores the effect of image quality covariates (artifacts) on the performance of periocular recognition. To simulate the real-time scenarios and understand the consequences of blur, resolution, and bit-depth of images on the recognition accuracy of periocular biometrics, we modeled out-of-focus blur, camera shake blur, low-resolution, and low bit-depth image acquisition using Gaussian function, linear motion, interpolation, and bit plan slicing, respectively. All the images of the UBIRIS.v1 database are degraded by varying strength of image quality covariates to obtain degraded versions of the database. Afterward, deep models are trained with each degraded version of the database. The performance of the model is evaluated by measuring statistical parameters calculated from a confusion matrix. Experimental results show that among all types of covariates, camera shake blur has less effect on the recognition performance, while out-of-focus blur significantly impacts it. Irrespective of image quality, the convolutional neural network produces excellent results, which proves the robustness of the developed model. © 2023 Co

关键词： Image quality

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Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

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computer Modeling in engineering & sciences 2024年第7期140卷 409-430页

作者： Zhuoqun Xia Hangyu Hu Wenjing Li Qisheng Jiang Lan Pu Yicong Shu Arun Kumar Sangaiah School of Computer and Communication Engineering Changsha University of Science and TechnologyChangshaChina Hunan Province People’s Hospital(The First Affiliated Hospital of Hunan Normal University) ChangshaChina The First Affiliated Hospital of Guangxi Medical University NanningChina International Graduate Institute of AI National Yunlin University of Science and TechnologyYunlinChina Department of Electrical and Computer Engineering Lebanese American UniversityBayrutLebanon

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible *** research has failed to fully explore effective DR lesion information in fundus ***,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion ***,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis ***,the LLM generates a global attention map based on ***,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion *** paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

关键词： DDR dataset diabetic retinopathy lesion localization multi-level patch attention mechanism

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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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2023 international Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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Heart Disease Prediction Using Novel Ensemble and Blending Based Cardiovascular Disease Detection Networks: EnsCVDD-Net and BlCVDD-Net

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IEEE Access 2024年 12卷 109230-109254页

作者： Khan, Hira Javaid, Nadeem Bashir, Tariq Akbar, Mariam Alrajeh, Nabil Aslam, Sheraz Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh11633 Saudi Arabia Cyprus University of Technology Limassol3036 Cyprus

Cardiovascular Diseases (CVDs) have emerged as a significant physiological condition, being a primary contributor to mortality. Timely and precise diagnosis of heart disease is crucial to safeguard patients from additional harm. Recent studies show that the usage of data driven approaches, such as Deep Learning (DL) and Machine Learning (ML) techniques, in the field of medical science is highly useful in accurately diagnosing heart disease in less time. However, statistical learning and traditional ML approaches require feature engineering to generate robust and effective features from data, which are then used in the prediction models. In the case of large complex data, both processes pose many challenges. Whereas, DL techniques are capable of learning features automatically from the data and are effective at handling large and intricate datasets while outperforming the ML models. This study focuses on the accurate prediction of CVDs, considering the patient’s health and socio-economic conditions while mitigating the challenges presented by imbalanced data. The Adaptive Synthetic Sampling Technique is used for data balancing, while the Point Biserial Correlation Coefficient is used as a feature selection technique. In this study, two DL models, Ensemble based Cardiovascular Disease Detection Network (EnsCVDD-Net) and Blending based Cardiovascular Disease Detection Network (BlCVDD-Net), are proposed for accurate prediction and classification of CVDs. EnsCVDD-Net is made by applying an ensemble technique to LeNet and Gated Recurrent Unit (GRU), and BlCVDD-Net is made by blending LeNet, GRU and Multilayer Perceptron. SHapley Additive exPlanations is used to provide a clear understanding of the influence different factors have on CVD diagnosis. The network’s performance is evaluated on the basis of various performance metrics. The results indicate that the EnsCVDD-Net outperforms all base models with 88% accuracy, 88% F1-score, 91% precision, 85% recall, and 777s execu

关键词： Heart

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Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism

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Light(science & Applications) 2023年第7期12卷 1401-1414页

作者： Cheng Xu Zhihao Ren Hong Zhou Jingkai Zhou Chong Pei Ho Nan Wang Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Center for Inteligent Sensors and MEMS(CISM) National University of SingaporeSingapore 117608Singapore institute of Microelectronics(IME) Agency for ScienceTechnology and Research(A*STAR)Singapore 138634Singapore NUS Graduate School for Integrative Science and Engineering Program(ISEP) National University of SingaporeSingapore 117456Singapore

Circular dichroism(CD)spectroscopy has been widely demonstrated for detecting chiral ***,the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging *** solve this problem,we report an enhanced vibrational circular dichroism(VCD)sensing platform based on plasmonic chiral metamaterials,which presents a 6-magnitude signal enhancement with a selectivity of chiral *** by coupled-mode theory,we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process,which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss,resulting in improved chiral light-matter interaction and enhanced molecular VCD ***,we demonstrate the thin-film sensing process of BSA andβ-lactoglobulin proteins,which contain secondary structures a-helix andβ-sheet and achieve a limit of detection down to zeptomole ***,we also,for the first time,explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures,where the mixing ratio can be successfully differentiated with our proposed chiral *** findings improve the sensing signal of molecules and expand the extractable information,paving the way toward label-free,compact,small-volume chiral molecule detection for stereochemical and clinical diagnosisapplications.

关键词： chiral vibrational circular

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Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

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international Journal of Fuzzy Systems 2024年第1期26卷 368-389页

作者： Zavieh, Hadi Javadpour, Amir Ja’fari, Forough Sangaiah, Arun Kumar Slowik, Adam School of Economics and Statistics Guangzhou University Guangzhou510006 China Harbin Institute of Technology Shenzhen China Department of Computer Engineering Sharif University of Technology Tehran Iran International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900–347 Portugal Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan. © The Author(s) 2023.

关键词： Markov processes

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Multi-agent Architecture for Passive Rootkit Detection with Data Enrichment

Multi-agent Architecture for Passive Rootkit Detection with...

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international Conference on computer science, Electronics, and Industrial engineering, CSEI 2022

作者： Trinks, Maickel Gondim, João Albuquerque, Robson Professional Post-Graduate Program in Electrical Engineering - PPEE - Department of Electrical Engineering University of Brasília DF Brasília Brazil Department of Computer Science Universidade de Brasília DF Brasília Brazil

ISBN: (纸本)9783031305917

The added value of the information transmitted in a cybernetic environment has resulted in a sophisticated malicious actions scenario aimed at data exfiltration. In situations with advanced actors, like APTs, such actions use obfuscation techniques of harmful activities as persistence assurance on strategic targets. The MADEX and NERD architectures proposed flow analysis solutions to detect rootkits that hide network traffic;however, it presents some operational cost, either in traffic volume or due to lack of aggregated information. In that regard, this work changes and improves user flow analysis techniques to eliminate impacts on network traffic, with data enrichment on local and remote bases, detection of domains consulted by rootkits and aggregation of information to generate threat intelligence, while maintaining high performance. The results show the possibility of aggregating information to data flows used by rootkits in order to have effective cyber defense actions against cybernetic threats without major impacts on the existing network infrastructure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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