東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Multiscale Modeling and Machine Lear...

Reveil, Mardochee.

FindBook

Google Book

Amazon

博客來

Multiscale Modeling and Machine Learning Studies of the Diffusion of Silicon and Intrinsic Defects in III-V Semiconductors.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multiscale Modeling and Machine Learning Studies of the Diffusion of Silicon and Intrinsic Defects in III-V Semiconductors./
作者:	Reveil, Mardochee.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	198 p.
附註:	Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Contained By:	Dissertations Abstracts International80-03B.
標題:	Computational chemistry. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10822218
ISBN:	9780438341708

Multiscale Modeling and Machine Learning Studies of the Diffusion of Silicon and Intrinsic Defects in III-V Semiconductors.
Reveil, Mardochee.

Multiscale Modeling and Machine Learning Studies of the Diffusion of Silicon and Intrinsic Defects in III-V Semiconductors. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 198 p.

Source: Dissertations Abstracts International, Volume: 80-03, Section: B.

Thesis (Ph.D.)--Cornell University, 2018.

This item is not available from ProQuest Dissertations & Theses.

Integrating III-V semiconductors into next-generation silicon-based processing is a promising alternative being considered as a route to faster and more energyefficient electronic devices. These III-V materials will be doped, typically with Si as a dopant. However, dopant activation remains an issue, compounded by the fact that there is still insufficient knowledge of the ease and preferred mechanistic pathways by which dopants, like Si, become activated within the III-V matrix. Using Density Functional Theory (DFT) and Nudge Elastic Band (NEB) calculations, we have determined many of these critically important properties, namely, the energy barriers associated with the diffusion of both intrinsic point defects and silicon impurities in prototypical III-V materials, here Zinc Blende GaAs, InAs and the CuAuI-ordered ternary In 0.5Ga0.5As. Refuting assumptions in the current literature that the enhanced diffusion can be attributed primarily to an increase in vacancies, vacancy-assisted diffusion of isolated Si atoms was found to be an unfavorable mechanism for this group of semiconductor alloys. Our results show that new and highly mobile species that are created at high dopant concentration are instead responsible for the enhanced diffusion observed. Those new species include Si complexes such as Si-Si pairs and Si split interstitials which can move more easily within the crystal lattice. We use these DFT results to inform the development of a continuum model that addresses limitations in current models and shows great agreement with experimental results. We also develop a new method whereby machine learning in lieu of DFT, is used to predict forces during NEB simulations. This new method allows to compute transition pathways at a fraction of the cost while maintaining reasonable accuracy compared to the traditional DFT approach.

ISBN: 9780438341708Subjects--Topical Terms:

3350019
Computational chemistry.

Multiscale Modeling and Machine Learning Studies of the Diffusion of Silicon and Intrinsic Defects in III-V Semiconductors.
LDR:03082nmm a2200349 4500 001 2207935
005 20190929184014.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438341708
035 $a (MiAaPQ)AAI10822218
035 $a (MiAaPQ)cornellgrad:10886
035 $a AAI10822218
040 $a MiAaPQ $c MiAaPQ
100 1 $a Reveil, Mardochee. $3 3434935
245 1 0 $a Multiscale Modeling and Machine Learning Studies of the Diffusion of Silicon and Intrinsic Defects in III-V Semiconductors.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 198 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Clancy, Paulette.
502 $a Thesis (Ph.D.)--Cornell University, 2018.
506 $a This item is not available from ProQuest Dissertations & Theses.
506 $a This item must not be sold to any third party vendors.
520 $a Integrating III-V semiconductors into next-generation silicon-based processing is a promising alternative being considered as a route to faster and more energyefficient electronic devices. These III-V materials will be doped, typically with Si as a dopant. However, dopant activation remains an issue, compounded by the fact that there is still insufficient knowledge of the ease and preferred mechanistic pathways by which dopants, like Si, become activated within the III-V matrix. Using Density Functional Theory (DFT) and Nudge Elastic Band (NEB) calculations, we have determined many of these critically important properties, namely, the energy barriers associated with the diffusion of both intrinsic point defects and silicon impurities in prototypical III-V materials, here Zinc Blende GaAs, InAs and the CuAuI-ordered ternary In 0.5Ga0.5As. Refuting assumptions in the current literature that the enhanced diffusion can be attributed primarily to an increase in vacancies, vacancy-assisted diffusion of isolated Si atoms was found to be an unfavorable mechanism for this group of semiconductor alloys. Our results show that new and highly mobile species that are created at high dopant concentration are instead responsible for the enhanced diffusion observed. Those new species include Si complexes such as Si-Si pairs and Si split interstitials which can move more easily within the crystal lattice. We use these DFT results to inform the development of a continuum model that addresses limitations in current models and shows great agreement with experimental results. We also develop a new method whereby machine learning in lieu of DFT, is used to predict forces during NEB simulations. This new method allows to compute transition pathways at a fraction of the cost while maintaining reasonable accuracy compared to the traditional DFT approach.
590 $a School code: 0058.
650 4 $a Computational chemistry. $3 3350019
650 4 $a Chemical engineering. $3 560457
650 4 $a Materials science. $3 543314
690 $a 0219
690 $a 0542
690 $a 0794
710 2 $a Cornell University. $b Chemical Engineering. $3 3284480
773 0 $t Dissertations Abstracts International $g 80-03B.
790 $a 0058
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10822218