rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Machine learning-based modelling in atomic layer deposition processes
  name:
    name part: Adeleke, Oluwatobi
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Karimzadeh, Sina.
  name:
    name part: Jen, Tien-Chien.
  name:
    name part: Taylor & Francis
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: flu
    place:
      place term: Boca Raton, FL
    publisher: CRC Press
    date issued: 2024
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxii, 354 pages) : illustrations
  abstract: While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques, there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling, optimization, and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such, this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. Offers an in-depth overview of the fundamentals of thin film technology, state-of-the-art computational simulation approaches in ALD, ML techniques, algorithms, applications, and challenges. Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. Explores the application of key techniques in ML, such as predictive analysis, classification techniques, feature engineering, image processing capability, and microstructural analysis of deep learning algorithms and generative model benefits in ALD. Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers, this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes, which scale from academic to industrial applications.
  note: Oluwatobi Adeleke, Tien-Chien Jen, SIna Karimzadeh..
  note: Includes bibliographical references and index.
  note: Oluwatobi Adeleke is a researcher at the Department of Mechanical Engineering Science, University of Johannesburg. He joined the University of Johannesburg in 2019 as a PhD researcher and completed his PhD research in 2022. He also holds a Masters' and Bachelor's degree in Mechanical Engineering from University of Ibadan and Ladoke Akintola University of Technology, respectively. His research interest is in artificial intelligence, soft computing techniques and machine learning, renewable energy, bio-energy. solar cells, waste-to-energy, and systems modeling. He has also made extensive contribution to researches in atomic layer deposition, material science, corrosion inhibition, waste management modelling and optimization and life cycle assessment. He has published articles in reputable journals neural computing and applications, Energy reports, Fuel, renewable energy, biotechnology reports, journal of material research and technology, engineered science and several other journals. He has published a number of book chapters and conference papers in these fields. He is a reviewer for several reputable journals in the field of waste management, soft computing and renewable energy. He was awarded the best PhD researcher in the Department of Mechanical Science, under the faculty of Engineering and Built Environment (FEBE) award of academic excellence, 2021. He is a member of Council for Regulation of Engineering in Nigeria (COREN), Nigeria Society of Engineers (NSE), and American Society of Mechanical Engineers (ASME). Sina Karimzadeh is a PhD research candidate at the University of Johannesburg. He holds a MSc degree in Mechanical engineering from the University of Johannesburg in 2020. He has been selected as one of the prospective for the chancellors Medal for the most meritorious Masters study for 2020. His current research interest focuses on the development of Li-ion battery active components and interface engineering by using atomic layer deposition (ALD) techniq…
  subject:
    topic: Thin films
  subject:
    topic: Machine learning
  subject:
    topic: Thin films industry
    topic: Technological innovations
  subject:
    topic: TECHNOLOGY / Manufacturing
  subject:
    topic: Thin films
  classification: QC176.83 .A24 2024eb
  classification: 530.4/175
  related item:
    title info:
      title: Emerging materials and technologies
  identifier: 1003803113
  identifier: 9781003803119
  identifier: 9781003346234
  identifier: 1003346235
  identifier: 1003803334
  identifier: 9781003803331
  identifier: 9781003346234 Taylor & Francis
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 231104
    record change date: 20241108165944.0
    record identifier: in00005525214
    language of cataloging:
      language term: eng