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135 results on '"Seoane, Natalia"'

9. An accurate machine learning model to study the impact of realistic metal grain granularity on Nanosheet FETs

Author

Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información, Universidade de Santiago de Compostela. Departamento de Electrónica e Computación, Fernandez, Julian G., Seoane, Natalia, Comesaña, Enrique, Pichel, Juan C., Garcia-Loureiro, Antonio, Universidade de Santiago de Compostela. Centro de Investigación en Tecnoloxías da Información, Universidade de Santiago de Compostela. Departamento de Electrónica e Computación, Fernandez, Julian G., Seoane, Natalia, Comesaña, Enrique, Pichel, Juan C., and Garcia-Loureiro, Antonio

Abstract

In this work, we present a machine learning neural network model to predict the impact of realistic metal grain granularity (MGG) variability on the threshold voltage V Th and on the ID -VG characteristics of a silicon-based 12 nm gate length nanosheet FET. This model is based on the multi-layer perceptron (MLP) machine learning architecture. As realistic MGG maps consist of the distribution of grains on the gate with different work-function values, it is relevant to apply algorithms such as the principal component analysis to reduce these features to the most representative ones. Once the realistic MGG features are correctly reduced without losing information, we train two different neural networks with the neurons in the output layer as the only difference, to predict the VTh and the ID - VG characteristics, respectively. The comparison between TCAD results and the model, shows excellent agreement for the mean and standard deviation of VTh distributions for different average grain sizes values (from 3 nm to 10 nm) demonstrating the accuracy of the machine learning model. Also, we study the amount of data needed to accurately train the MLPs, leading to results that allow us to drastically reduce the computational time required to perform variability studies for state-of-art nano FET devices

Published
2023

13. A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint.

Author

García-Loureiro, Antonio, Seoane, Natalia, Fernández, Julián G., Comesaña, Enrique, and Pichel, Juan C.

Subjects
*ECOLOGICAL impact, *MACHINE learning, *NANOWIRES, *SEMICONDUCTOR devices
Abstract

The performance and reliability of semiconductor devices scaled down to the sub-nanometer regime are being seriously affected by process-induced variability. To properly assess the impact of the different sources of fluctuations, such as line edge roughness (LER), statistical analyses involving large samples of device configurations are needed. The computational cost of such studies can be very high if 3D advanced simulation tools (TCAD) that include quantum effects are used. In this work, we present a machine learning approach to model the impact of LER on two gate-all-around nanowire FETs that is able to dramatically decrease the computational effort, thus reducing the carbon footprint of the study, while obtaining great accuracy. Finally, we demonstrate that transfer learning techniques can decrease the computing cost even further, being the carbon footprint of the study just 0.18 g of CO2 (whereas a single device TCAD study can produce up to 2.6 kg of CO2), while obtaining coefficient of determination values larger than 0.985 when using only a 10% of the input samples. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Analysis of Parallel Numerical Libraries to Solve the 3D Electron Continuity Equation

Author

Seoane, Natalia, García-Loureiro, A. J., Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Bubak, Marian, editor, van Albada, Geert Dick, editor, Sloot, Peter M. A., editor, and Dongarra, Jack, editor

Published
2004
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23. Current variability in Si nanowire MOSFETs due to random dopants in the source/drain regions: a fully 3-D NEGF simulation study

Author

Seoane, Natalia, Martinez, Antonio, Brown, Andrew R., Barker, John R., and Asenov, Asen

Subjects
Metal oxide semiconductor field effect transistors -- Evaluation, Potential theory (Mathematics) -- Usage, Semiconductor doping -- Analysis, Silicon -- Electric properties, Business, Electronics, Electronics and electrical industries
Abstract

The impact of random discrete dopants in the source/drain (S/D) leads on the current variability of a gate-all-around Si nanowire transistor is analyzed. A full 3-dimensional (3-D) real-space nonequilibrium Green's function (NEGF) formalism is used for estimating the variability in the [I.sub.D]-[V.sub.G] characteristics of a future-generation Si nanowire transistor induced by random dopants (RDs) in the source/drain (S/D) extensions.

Published
2009

25. Benchmarking of scaled InGaAs implant-free nanoMOSFETs

Author

Kalna, Karol, Seoane, Natalia, Garcia-Loureiro, Antonio J., Thayne, Iain G., and Asenov, Asen

Subjects
Finite element method -- Usage, Gallium arsenide -- Electric properties, Indium -- Electric properties, Metal oxide semiconductor field effect transistors -- Evaluation, Business, Electronics, Electronics and electrical industries
Abstract

The performance of n-type implant-free (IF) III-V nanoMOSFETs with an [In.sub.0.75][Ga.sub.0.25]As channel is analyzed by using finite-element heterostructure Monte Carlo (MC) and parallel 3-D drift-diffusion (D-D) simulations. The benchmarking study is based on the calibration of the MC simulator against the transport data obtained from relevant [delta]-doped heterostructures with a high-k gate dielectric.

Published
2008

33. Ultra‐efficient intrinsic‐vertical‐tunnel‐junction structures for next‐generation concentrator solar cells.

Author

Seoane, Natalia, Fernández, Eduardo F., Almonacid, Florencia, and García‐Loureiro, Antonio

Subjects
SOLAR concentrators, SOLAR cells, SILICON solar cells, SOLAR cell efficiency, LIGHT intensity
Abstract

The efficiency of solar cells can be enhanced by increasing the light intensity and/or the number of bandgaps of the structure. However, current solar cells cannot fully exploit these two factors because of various critical drawbacks. Here, we show a novel microscale, that is, side ≈ 0.5 mm, vertical solar cell structure that does not suffer the series resistance and bandgap limitations issues of current devices. The preliminary structures investigated show extreme efficiencies, >40%, at ultrahigh concentration factors of 15 000 suns. In addition, future designs with a better bandgap configuration are expected to deliver cells with efficiencies far above 50% at extreme light intensities. This early design offers a fast and reliable route to push the efficiency towards the maximum solar conversion limit and represents a promising way to develop new‐generation ultraefficient and low‐cost concentrator photovoltaic systems. [ABSTRACT FROM AUTHOR]

Published
2021
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44. Vertical-Tunnel-Junction (VTJ) Solar Cell for Ultra-High Light Concentrations (>2000 Suns).

Author

Fernandez, Eduardo F., Seoane, Natalia, Almonacid, Florencia, and Garcia-Loureiro, Antonio J.

Subjects
TUNNEL junctions (Materials science), SOLAR cells, BAND gaps
Abstract

A novel architecture of cell structure tailored to ultra-high (>2000 suns) concentration ratios is proposed. The basic solar cell consists of two p-n junctions connected in series by a highly doped tunnel diode with the metallic contacts located laterally. The tunneling connection allows using direct band-gap semiconductor compounds aiming to optimize the absorption of the spectrum. The performance of the novel architecture is investigated up to ultra-high concentration using TCAD software. Simulations show its viability for developing a new generation of solar cells to increase the potential in terms of efficiency and cost reduction of ultra-high concentrator systems. The solar cell does not show any degradation with concentration and efficiency as high as 28.4% at 15000 suns has been obtained for a preliminary design. [ABSTRACT FROM AUTHOR]

Published
2019
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