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First-Principles and Machine Learning Modeling for Design and Operation of Area-Selective Atomic Layer Deposition
First-Principles and Machine Learning Modeling for Design and Operation of Area-Selective Atomic Layer Deposition
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151348
- ISBN
- 9798382332710
- DDC
- 660
- 서명/저자
- First-Principles and Machine Learning Modeling for Design and Operation of Area-Selective Atomic Layer Deposition
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 169 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
- 주기사항
- Advisor: Christofides, Panagiotis D.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Semiconductor manufacturing comprises nearly 500 processing steps, where products rely on stringent design criteria to have high-performance characteristics. One of these processing steps includes the fabrication of high-κ oxide films on the surfaces of transistors to minimize current and heat losses, and short-channel effects, which are detrimental to semiconductor longevity. These films demand thicknesses in the nanoscale that are constructed using sequential cycles of atomic layer deposition (ALD) and atomic layer etching (ALE), where precise monolayers of substrate film are deposited and exhibit self-limiting behavior. However, notable challenges in industrial practice include maintaining the accuracy of the deposition and etching processes and the uniformity of the films that are produced, identifying the operating conditions that contribute to optimal product conformation, and developing reactors that maximize the productivity of these atomic layer processes. Additionally, there is insufficient and available data for these processes in industry, which makes their characterization and optimization an obstacle for researchers. Thus, in silico modeling has paved the way for producing data that is reflective of data observed in industrial practice. This simulated data is produced through a multiscale computational fluid dynamics framework that combined microscopic, mesoscopic, and macroscopic phases throughout various time and length scales. This work encompasses several disciplines from reaction characterization through ab initio molecular dynamics simulations, rudimentary chemical kinetics laws, and kinetic Monte Carlo methods, reactor optimization and design through computational fluid dynamics, and feedback-based run-to-run and online process control with an application to machine learning for a plethora of atomic layer processes.
- 일반주제명
- Chemical engineering
- 일반주제명
- Materials science
- 일반주제명
- Fluid mechanics
- 일반주제명
- Computational physics
- 키워드
- Process control
- 기타저자
- University of California, Los Angeles Chemical Engineering 0294
- 기본자료저록
- Dissertations Abstracts International. 85-11B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211151348
■006m o d
■007cr#unu||||||||
■020 ▼a9798382332710
■035 ▼a(MiAaPQ)AAI31242820
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a660
■1001 ▼aTom, Matthew Cheuk-Woh.
■24510▼aFirst-Principles and Machine Learning Modeling for Design and Operation of Area-Selective Atomic Layer Deposition
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a169 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-11, Section: B.
■500 ▼aAdvisor: Christofides, Panagiotis D.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aSemiconductor manufacturing comprises nearly 500 processing steps, where products rely on stringent design criteria to have high-performance characteristics. One of these processing steps includes the fabrication of high-κ oxide films on the surfaces of transistors to minimize current and heat losses, and short-channel effects, which are detrimental to semiconductor longevity. These films demand thicknesses in the nanoscale that are constructed using sequential cycles of atomic layer deposition (ALD) and atomic layer etching (ALE), where precise monolayers of substrate film are deposited and exhibit self-limiting behavior. However, notable challenges in industrial practice include maintaining the accuracy of the deposition and etching processes and the uniformity of the films that are produced, identifying the operating conditions that contribute to optimal product conformation, and developing reactors that maximize the productivity of these atomic layer processes. Additionally, there is insufficient and available data for these processes in industry, which makes their characterization and optimization an obstacle for researchers. Thus, in silico modeling has paved the way for producing data that is reflective of data observed in industrial practice. This simulated data is produced through a multiscale computational fluid dynamics framework that combined microscopic, mesoscopic, and macroscopic phases throughout various time and length scales. This work encompasses several disciplines from reaction characterization through ab initio molecular dynamics simulations, rudimentary chemical kinetics laws, and kinetic Monte Carlo methods, reactor optimization and design through computational fluid dynamics, and feedback-based run-to-run and online process control with an application to machine learning for a plethora of atomic layer processes.
■590 ▼aSchool code: 0031.
■650 4▼aChemical engineering
■650 4▼aMaterials science
■650 4▼aFluid mechanics
■650 4▼aComputational physics
■653 ▼aMultiscale modeling
■653 ▼aProcess control
■653 ▼aSemiconductor manufacturing
■653 ▼aAtomic layer etching
■653 ▼aAtomic layer deposition
■690 ▼a0542
■690 ▼a0794
■690 ▼a0204
■690 ▼a0216
■71020▼aUniversity of California, Los Angeles▼bChemical Engineering 0294.
■7730 ▼tDissertations Abstracts International▼g85-11B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161374▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
