MODELING ELECTRON TRANSPORT IN MOSFET DEVICES: EVOLUTION AND STATE OF THE ART

The progress of silicon technologies we have witnessed in the last twenty years has traced the path to the unprecedented revolution of information technologies, which has changed almost everybody's lifestyles. Apparently, this has happened with a little big help from TCAD tools. Big, because few major advancements have been achieved through the clever exploitation of non-conventional simulation tools, and because everyday device optimization deeply relies on TCAD tools. Little, because the qualitative feeling is that the technology would have progressed anyway, through the work of many highly-skilled technology experts, even without simulation guidelines. The purpose of this paper is to review the state-of-the-art of the field of the transport modeling of electron devices, trying to grasp the essence of the most relevant simulation models proposed so far, whence to contribute to spur the activity on the fundamental modeling of carrier transport.

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Framework for TCAD augmented machine learning on multi- I–V characteristics using convolutional neural network and multiprocessing

Journal of Semiconductors ◽

10.1088/1674-4926/42/12/124101 ◽

2021 ◽

Vol 42 (12) ◽

pp. 124101

Author(s):

Thomas Hirtz ◽

Steyn Huurman ◽

He Tian ◽

Yi Yang ◽

Tian-Ling Ren

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Information Technologies ◽

Deep Neural Networks ◽

State Of The Art ◽

Data Driven ◽

Sufficient Data ◽

Learning Models ◽

Simulation Tools ◽

New Information

Abstract In a world where data is increasingly important for making breakthroughs, microelectronics is a field where data is sparse and hard to acquire. Only a few entities have the infrastructure that is required to automate the fabrication and testing of semiconductor devices. This infrastructure is crucial for generating sufficient data for the use of new information technologies. This situation generates a cleavage between most of the researchers and the industry. To address this issue, this paper will introduce a widely applicable approach for creating custom datasets using simulation tools and parallel computing. The multi-I–V curves that we obtained were processed simultaneously using convolutional neural networks, which gave us the ability to predict a full set of device characteristics with a single inference. We prove the potential of this approach through two concrete examples of useful deep learning models that were trained using the generated data. We believe that this work can act as a bridge between the state-of-the-art of data-driven methods and more classical semiconductor research, such as device engineering, yield engineering or process monitoring. Moreover, this research gives the opportunity to anybody to start experimenting with deep neural networks and machine learning in the field of microelectronics, without the need for expensive experimentation infrastructure.

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A long way to the electrode: how do Geobacter cells transport their electrons?

Biochemical Society Transactions ◽

10.1042/bst20120046 ◽

2012 ◽

Vol 40 (6) ◽

pp. 1274-1279 ◽

Cited By ~ 24

Author(s):

Pablo Sebastián Bonanni ◽

Germán David Schrott ◽

Juan Pablo Busalmen

Keyword(s):

Electron Transport ◽

State Of The Art ◽

The State ◽

Geobacter Sulfurreducens ◽

Specific Role ◽

Genetic Studies ◽

Current Production

The mechanism of electron transport in Geobacter sulfurreducens biofilms is a topic under intense study and debate. Although some proteins were found to be essential for current production, the specific role that each one plays in electron transport to the electrode remains to be elucidated and a consensus on the mechanism of electron transport has not been reached. In the present paper, to understand the state of the art in the topic, electron transport from inside of the cell to the electrode in Geobacter sulfurreducens biofilms is analysed, reviewing genetic studies, biofilm conductivity assays and electrochemical and spectro-electrochemical experiments. Furthermore, crucial data still required to achieve a deeper understanding are highlighted.

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Trends in integrated land use/transport modeling: An evaluation of the state of the art

Journal of Transport and Land Use ◽

10.5198/jtlu.2018.1205 ◽

2018 ◽

Vol 11 (1) ◽

Cited By ~ 9

Author(s):

Rolf Moeckel ◽

Carlos Llorca Garcia ◽

Ana Tsui Moreno Chou ◽

Matthew Bediako Okrah

Keyword(s):

Land Use ◽

State Of The Art ◽

The State ◽

Transport Modeling

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Constructing n/n+ homojunction in monolithic perovskite film for boosting charge collection in inverted perovskite photovoltaics

Energy & Environmental Science ◽

10.1039/d1ee00918d ◽

2021 ◽

Author(s):

Yan-Na Lu ◽

Jun-Xing Zhong ◽

Yinye Yu ◽

Xi Chen ◽

Chan-Ying Yao ◽

...

Keyword(s):

Solar Cells ◽

Electron Transport ◽

Performance Enhancement ◽

State Of The Art ◽

Perovskite Solar Cells ◽

Carrier Dynamics ◽

The State ◽

Transport Layer ◽

Electron Transport Layer ◽

Charge Collection

The suboptimal carrier dynamics at perovskite/electron transport layer has largely limited the further performance enhancement of the state-of-the-art inverted p-i-n structured perovskite solar cells. Herein, we discovered that a simple...

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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