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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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CompressedMediQ: Hybrid Quantum Machine Learning Pipeline for High-Dimensional Neuroimaging Data

CompressedMediQ: Hybrid Quantum Machine Learning Pipeline fo...

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Kuan-Cheng Chen Yi-Tien Li Tai-Yu Li Chen-Yu Liu Po-Heng Henry Lee Cheng-Yu Chen Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Research Center for Neuroscience Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan National Center for High-Performance Computing NARlabs Hsinchu Taiwan Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Department of Medical Imaging Taipei Medical University Hospital Taipei Taiwan Department of Radiology National Defense Medical Center Taipei Taiwan Department of Radiology School of Medicine College of Medicine Taipei Taiwan

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

This paper introduces CompressedMediQ, a novel hybrid quantum-classical machine learning pipeline specifically developed to address the computational challenges associated with high-dimensional multi-class neuroimaging data analysis. Standard neuroimaging datasets, such as large-scale MRI data from the Alzheimer’s Disease Neuroimaging Initiative and Neuroimaging in Frontotemporal Dementia, present significant hurdles due to their vast size and complexity. CompressedMediQ integrates classical HPC nodes for advanced MRI pre-processing and Convolutional Neural Network (CNN)-PCA-based feature extraction and reduction, addressing the limited-qubit availability for quantum data encoding in the NISQ era. This is followed by Quantum Support Vector Machine (QSVM) classification. By utilizing quantum kernel methods, the pipeline optimizes feature mapping and classification, enhancing data separability and outperforming traditional neuroimaging analysis techniques. Experimental results highlight the pipeline’s superior accuracy in dementia staging, validating the practical use of quantum machine learning in clinical diagnostics. Despite the limitations of NISQ devices, this proof-of-concept demonstrates the transformative potential of quantum-enhanced learning, paving the way for scalable and precise diagnostic tools in healthcare and signal processing.

关键词： Neuroimaging Support vector machines Quantum computing Accuracy Magnetic resonance imaging Pipelines Machine learning Medical services Signal processing Standards

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Towards to Characterization of Network Management Traffic in OpenStack-Based Clouds

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SN Computer Science 2023年第5期4卷 450页

作者： Donatti, Adnei W. Miers, Charles C. Koslovski, Guilherme P. Pillon, Maurício A. Carvalho, Tereza C. M. B. Graduate Program in Electrical Engineering Escola Politécnica da Universidade de São Paulo São Paulo 05508-010 Brazil Graduate Program in Applied Computing Santa Catarina State University Joinville 89219-710 Brazil

OpenStack is versatile and popular, allowing full customization for creating private or public IaaS clouds. This work addresses a network traffic analysis and characterization for the management domain inside OpenStack clouds. We conduct an induced lifecycle to execute virtual machine (VM)-related tasks, measure the traffic, and identify the services behind the traffic generated by these operations. Also, we analyze the impact of different images of operating systems (OSs) and VM’s flavor in the measured traffic. Moreover, we observe that predicting the volume of network traffic from operations, such as creation and shelving instances of VMs, helps estimate bandwidth boundaries, avoiding bottlenecks, for example. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Characterization Network traffic OpenStack Virtual machine VM

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

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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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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Statistical Device Simulation and Machine Learning of Process Variation Effects of Vertically Stacked Gate-All-Around Si Nanosheet CFETs

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IEEE Transactions on Nanotechnology 2024年 23卷 386-392页

作者： Kola, Sekhar Reddy Li, Yiming Butola, Rajat National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Department of Electronics and Electrical Engineering Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Institute of Pioneer Semiconductor Innovation Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

In this study, we report the process variation effect (PVE) including the work function fluctuation (WKF) on the DC/AC characteristic fluctuation of stacked gate-all-around silicon complementary field-effect transistors (CFETs). The PVE affects characteristic fluctuation significantly;in particular, for the variability of off-state current. Owing to the bottom channel of a fin-type, the P-FET suffers from the worst off-state current fluctuation (more than 200% variation) compared to the N-FET. The device variability induced by the WKF is marginal because of amorphous-type metal grains. As input features to an artificial neural network (ANN) model, low and high work function values, as well as parameters of PVE that have prevalent effects on CEFT transfer characteristics are further considered and modeled. The estimated values of R2-score prove that the ANN model properly grasps information from the dataset successfully;thus, it can be used to model emerging CFETs for circuit simulation. © 2002-2012 IEEE.

关键词： Neural networks

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Artificial neural network-based method to classify sedimentary rocks 12

Artificial neural network-based method to classify sedimenta...

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12th International Conference on Sensing Technology, ICST 2018

作者： Figueiredo, Rodrigo Marques Veronez, Mauricio Roberto Wohnrath, Francisco Manoel Da Silva, Marcio Rosa Gonzaga, Luiz Kupssinsku, Lucas Silveira Bordin, Fabiane Brum, Diego Cazarin, Caroline Lessio Graduate Program in Geology Unisinos University Saõ Leopoldo Brazil Advanced Visualization and Geoinformatics Laboratory Unisinos University Saõ Leopoldo Brazil Graduate Program in Electrical Engineering Unisinos University Saõ Leopoldo Brazil Graduate Program in Applied Computing Unisinos University Saõ Leopoldo Brazil Rio de Janeiro Brazil

ISBN: (纸本)9781538651476

In a geological study, an important step is to determine the type of sedimentary rock or its grain size. Such a determination requires accurate analysis in the field or in a laboratory. As the size of the study area grows, this activity can be time consuming and error prone because the number of specialists working under rigid criteria also increases. This paper proposes a novel methodology to classify grain size using unique wavelength reflectance data and artificial neural networks. The results indicate that the proposed method can be reliably used in the field. © 2018 IEEE.

关键词： Neural networks

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Improving the vocabulary learning by personalized proficiency 12

Improving the vocabulary learning by personalized proficienc...

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12th International Conference on Ubi-Media computing, Ubi-Media 2019

作者： Lin, Yi-Zhen Chen, Kai-Hsiang Huang, Jen-Wei Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Academia Sinica Taiwan

ISBN: (纸本)9781728128207

To improve the vocabulary ability is very important in language learning. Thus, if we can learn and remember a word very effectively, then we will be able to master a language more quickly. Therefore, many scholars began to propose related research. Due to the learning mechanism of human brain, sometimes when people learn a new knowledge they may forgot at a short time. In order to make the consideration more complete, after analyzing Hermann Ebbinghaus's forgetting curve experiment, we added two variables, one is the acceptance of each word by the same person, and the other is the ability of different people to remember vocabulary. With the above two parameters, we want to design a system to help user to review the vocabulary which may be forgetting before. The forgetting curve can be personalized, and it is more accurate to calculate the best time for each user to review the vocabulary. © 2019 IEEE.

关键词： Game design

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Ontology based modelling of operator training simulator scenarios from human error reports 5

Ontology based modelling of operator training simulator scen...

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5th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2015

作者： Filho, Flávio Torres Aguiar, Yuska Paola Costa De Fátima Queiroz Vieira, Maria Post-Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Department of Computing Federal University of Paraiba Rio Tinto Brazil Department of Electrical Engineering Federal University of Campina Grande Campina Grande Brazil

ISBN: (纸本)9789897581205

In industrial systems' simulated environments the assimilation of technical procedures by the operators under training is enhanced by reproducing similar to real situations experienced in the workplace. The experience and learning acquired in simulators is directly related to the quality and realism of the proposed training scenarios. On the other hand, the experience acquired is even more conducive to good working practices when it involves situations known to lead into errors. Often training scenarios are dependent on the tutor's experience and the knowledge of operator difficulties in the work environment. This paper proposes a systematic approach for building training scenarios to be simulated, based on the analysis and reproduction of situations described in the working environment error reports. This approach is based on the instantiation of ontologies built for both domains, covering the knowledge on both the error situations and operator training scenarios. This study is focused in the domain of electric power systems operation.

关键词： Errors

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Data Classification combining Self-Organizing Maps and Informative Nearest Neighbor

Data Classification combining Self-Organizing Maps and Infor...

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International Joint Conference on Neural Networks

作者： Leandro J. Moreira Leandro A. Silva Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo Brazil Graduate Program in Electrical Engineering & Computing Mackenzie Presbyterian University Sao Paulo Brazil

ISBN: (纸本)9781509006212

The task of classifying data has been addressed in various works, and has been utilized in various areas of application, such as medicine, industry, marketing, financial market and many others. This work will present a data classifier proposal that combines the SOM (Self-Organizing Map) neural network with INN (Informative Nearest Neighbors). The combination of these two algorithms will be called in this work as SOM-INN. The classification will be done in a two steps task: in the first the SOM has a functionality to reduce the dataset through a process that utilizes the winning neuron concept and, in the second step, the dataset objects selected will be utilized as reference for the INN algorithm to decide about the classification utilizing the most informative object of the reduced dataset. Experiments using 14 public datasets will be made comparing classic classification algorithms from the indicators of accuracy and time consumed in the classification process. The results obtained show that the proposed SOM-INN algorithm, when compared with the other classifiers, presents better accuracy rates in databases where the border region does not have the object classes well distributed. However, its main differential is in the classification time, which is extremely important for real applications.

关键词： Classification algorithms Training Neurons Proposals Prototypes Measurement Self-organizing feature maps self-organising feature maps Sorting algorithm Nearest neighbor Neurons PROTOTYPE Functional training Dataset Pharmaceutical Industry pubs data classification GRHL3 gene

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