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International Symposium on VLSI Technology, Systems and Applications

作者： Chandni Akbar Narasimhulu Thoti Yiming Li Paralllel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu Taiwan EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Paralllel and Scientific Computing Laboratory EECS International Graduate Program Institue of Communications Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Institue of Communications Engineering National Chiao Tung University Hsinchu Taiwan

We for the first time investigate the possibility to replace the device simulation for tunnel field-effect transistors (TFETs) with a machine learning (ML) algorithm. By incorporating the experimentally validated device simulation, a keyML technique named random forest regression (RFR) model is advanced and applied to predict characteristics of TFETs. The results of this work may benefit the design and fabrication of TFETs based on the well-trained RFR model. Very fast and accurate drain current (ID) prediction in terms of the engineering acceptable root-mean-square (RMSE) error inaugurates TFET technology with ML with a potential application to significantly reduce the computational cost.

关键词： Training TFETs Machine learning algorithms Computational modeling Vegetation Predictive models Data models

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Learning interface conditions in domain decomposition solvers 22

Learning interface conditions in domain decomposition solver...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Ali Taghibakhshi Nicolas Nytko Tareq Zaman Scott MacLachlan Luke Olson Matthew West Mechanical Science and Engineering University of Illinois Urbana-Champaign Urbana IL Computer Science University of Illinois Urbana-Champaign Urbana IL Scientific Computing Program Memorial University of Newfoundland and Labrador St. John's NL Canada Mathematics and Statistics Memorial University of Newfoundland and Labrador St. John's NL Canada

ISBN: (纸本)9781713871088

Domain decomposition methods are widely used and effective in the approximation of solutions to partial differential equations. Yet the optimal construction of these methods requires tedious analysis and is often available only in simplified, structured-grid settings, limiting their use for more complex problems. In this work, we generalize optimized Schwarz domain decomposition methods to unstructured-grid problems, using Graph Convolutional Neural Networks (GCNNs) and unsupervised learning to learn optimal modifications at subdomain interfaces. A key ingredient in our approach is an improved loss function, enabling effective training on relatively small problems, but robust performance on arbitrarily large problems, with computational cost linear in problem size. The performance of the learned linear solvers is compared with both classical and optimized domain decomposition algorithms, for both structured- and unstructured-grid problems.

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Accelerating GMRES with deep learning in real-time

arXiv

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arXiv 2021年

作者： Luna, Kevin Klymko, Katherine Blaschke, Johannes P. Program in Applied Mathematics The University of Arizona United States Computational Research Division Lawrence Berkeley National Laboratory United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory United States

GMRES is a powerful numerical solver used to find solutions to extremely large systems of linear equations. These systems of equations appear in many applications in science and engineering. Here we demonstrate a real-time machine learning algorithm that can be used to accelerate the time-to-solution for GMRES. Our framework is novel in that is integrates the deep learning algorithm in an in situ fashion: the AI-accelerator gradually learns how to optimizes the time to solution without requiring user input (such as a pre-trained data set). We describe how our algorithm collects data and optimizes GMRES. We demonstrate our algorithm by implementing an accelerated (MLGMRES) solver in Python. We then use MLGMRES to accelerate a solver for the Poisson equation - a class of linear problems that appears in may applications. Informed by the properties of formal solutions to the Poisson equation, we test the performance of different neural networks. Our key takeaway is that networks which are capable of learning non-local relationships perform well, without needing to be scaled with the input problem size, making them good candidates for the extremely large problems encountered in high-performance computing. For the inputs studied, our method provides a roughly 2× acceleration. Copyright © 2021, The Authors. All rights reserved.

关键词： Poisson equation

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Scaling Limitations of Line TFETs at Sub-8-nm Technology Node

Scaling Limitations of Line TFETs at Sub-8-nm Technology Nod...

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2020 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2020

作者： Thoti, Narasimhulu Li, Yiming Kola, Sekhar Reddy Parallel and Scientific Computing Laboratory Eecs International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering Center for MmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu300 Taiwan

ISBN: (纸本)9781728142326

The scope of the work is to investigate limitations in device scaling by identifying various parameters of short channel effects (SCEs) in current challenging geometries of the line tunnel field effect transistors (TFETs). Key factors of the considered device, such as the doping and the thickness (tn) of n-epitaxial region, and source-to-drain length (LSDeff) scaling cannot be tuned anymore to boost the device characteristics for the sub-8-nm technology node. The main results of this study indicates that the engineering acceptable performance are achieved at a low doping of 5 1018 cm-3, an optimal tn as low (about 0.5 nm), and a LSDeff greater than 12.5 nm. Hence, the line TFETs below sub-8-nm faces serious bottleneck of scaling and cannot be further scaled with the conventional scaling rule at all. © 2020 IEEE.

关键词： Tunnel field effect transistors

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Statistical Analysis of Intrinsic High-Frequency Characteristic Fluctuation of Emerging Silicon Gate-All-Around Nanosheet (NS) MOSFETs at Sub-3-nm Nodes

Statistical Analysis of Intrinsic High-Frequency Characteris...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Kazuhiko Endo Seiji Samukawa Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Institute of Communications Engineering Department of Electronics and Electrical Engineering Institute of Biomedical Engineering Center for mm Wave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Fluid Science Tohoku University 2-1-1 Katahira Aoba-ku Sendai Japan National Institute of Advanced Industrial Science and Technology Tsukuba Japan

In recent years, the GAA NS Si MOSFET has been explored as a leading technology. However, the intrinsic parameters of GAA NS Si MOSFETs are affected to varying degrees by various fluctuation sources, Statistically independent and identically distributed $(iid)$ assumptions on the aforementioned random variables overestimate the variability of high-frequency characteristics, compared with considering all fluctuation factors simultaneously. Notably, the random nanosized metal grains dominates the variations of voltage gain, cut-off frequency, and 3dB frequency because the random work functions strongly alter the channel surface potential.

关键词： MOSFET Fluctuations Statistical analysis Gallium arsenide Cutoff frequency Metals Logic gates

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Time-Dependent Orbital-Free Density Functional Theory: A New Development of the Dynamic Kinetic Energy Potential

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Journal of Chemical Theory and Computation 2025年第11期21卷 2025 May 20页

作者： Zhang, Xu Huang, Chen Department of Physics and Astronomy California State University Northridge 91330 CA United States Department of Scientific Computing Materials Science and Engineering Program National High Magnetic Field Laboratory Florida State University Tallahassee 32306 FL United States

Time-dependent orbital-free density functional theory (TD-OFDFT) is a promising method for investigating electronic dynamics in large metallic systems. One key component in TD-OFDFT is the dynamic kinetic energy potential (DKEP), which contains the memory effect missed in the adiabatic OFDFT. In this work, we developed a new DKEP based on a density-dependent kernel that is nonlocal in both space and time. The kernel is expanded in terms of the Laguerre polynomials multiplied by exponential decay functions. The parameters in the expansion are determined by fitting the TD-OFDFT dipole oscillations to those from time-dependent Kohn-Sham DFT (TD-KSDFT) simulations. This work also resolves a long-standing problem in TD-OFDFT: the lack of a well-defined total energy. The total energy of a system should be conserved when the external stimulating potential is switched off. Without a properly defined total energy, there is no guarantee that a long-time TD-OFDFT simulation will be stable. This problem is tackled by introducing an energy term for DKEP. The performance of this new TD-OFDFT formalism was examined by simulating several sodium clusters and a sodium nanorod. In most cases, the results are in good agreement with the TD-KSDFT calculations. © 2025 American Chemical Society.

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29. The competitiveness of bioenergy by-products in optimized livestock diet formulation

Animal - Science proceedings

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Animal - Science proceedings 2022年第4期13卷 543-544页

作者： J.G.O. Marques N.R.D. de Souza F. Senna V.H.M. Mourão P.C. Machado S.R. Medeiros L.G. Barioni D. Moran R. de Oliveira Silva Global Academy of Agriculture and Food Security University of Edinburgh Edinburgh UK Interinstitutional Graduate Program in Bioenergy (USP/UNICAMP/UNESP) University of Campinas Campinas Brazil Institute of Mathematics Statistics and Scientific Computing University of Campinas Campinas Brazil Embrapa Agricultural Informatics Campinas Brazil Xtra Factors Inc. Ephrata PA USA Embrapa Gado de Corte Campo Grande Mato Grosso do Sul Brazil

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Evaluation of logistic regression applied to respondent-driven samples: Simulated and real data

arXiv

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arXiv 2021年

作者： Sperandei, Sandro Bastos, Leonardo S. Ribeiro-Alves, Marcelo Reis, Arianne Bastos, Francisco I. Translational Health Research Institute Western Sydney University Australia Institute of Scientific and Technological Communication & Information in Health Oswaldo Cruz Foundation Brazil Scientific Computing Program Oswaldo Cruz Foundation Brazil National Institute of Infectious Diseases Evandro Chagas Oswaldo Cruz Foundation Brazil School of Health Sciences Western Sydney University Australia

Objective: To investigate the impact of different logistic regression estimators applied to RDS samples obtained by simulation and real data. Methods: Four simulated populations were created combining different connectivity models, levels of clusterization and infection processes. Each subject in the population received two attributes, only one of them related to the infection process. From each population, RDS samples with different sizes were obtained. Similarly, RDS samples were obtained from a real-world dataset. Three logistic regression estimators were applied to assess the association between the attributes and the infection status, and subsequently the observed coverage of each was measured. Results: The type of connectivity had more impact on estimators’ performance than the clusterization level. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the real dataset, the performance of weighted estimators presented some instabilities, making them a risky option. Conclusion: An unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with similar performance to random samples and, therefore, should be the preferred option. Copyright © 2021, The Authors. All rights reserved.

关键词： Sampling

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Analysis of Negative Capacitance MOSFETs Characteristic with Spacer

Analysis of Negative Capacitance MOSFETs Characteristic with...

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2020 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2020

作者： Lee, Yu-Chun Kola, Sekhar Reddy Chen, Chieh-Yang Chuang, Min-Hui Li, Yiming Parallel and Scientific Computing Laboratory Institute of Biomedical Engineering Institute of Communications Engineering Eecs International Graduate Program Department of Electrical and Computer Engineering Center for MmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu300 Taiwan

ISBN: (纸本)9781728142326

In this work, our study comprises of design and investigation on negative capacitance (NC), metal-oxide-semiconductor (MOS) field effects transistors (MOSFETs) with spacer and source/drain (S/D) overlap engineering. The scope of the work is to boost the performance and high-energy efficiency of the studied NC-MOSFETs by using the ferro electric material (FE). The NC-MOSFETs with the spacer technology can achieve the admirable Ion/Ioff ratio and subthreshold swing (SS), compared with planar MOSFETs. It makes device scaling possible by eliminating the short channel effect (SCE). We further estimated the effect of FE thickness and spacer, which are another critical parameter of obtaining better electrical characteristics and reducing SS. © 2020 IEEE.

关键词： Capacitance

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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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