BOOKS - Machine Learning-Based Modelling in Atomic Layer Deposition Processes (Emergi...
Machine Learning-Based Modelling in Atomic Layer Deposition Processes (Emerging Materials and Technologies) - Oluwatobi Adeleke December 15, 2023 PDF  BOOKS
ECO~20 kg CO²

2 TON

Views
977594

Telegram
 
Machine Learning-Based Modelling in Atomic Layer Deposition Processes (Emerging Materials and Technologies)
Author: Oluwatobi Adeleke
Year: December 15, 2023
Format: PDF
File size: PDF 41 MB
Language: English



Atomic layer deposition (ALD) is one such technology that has greatly benefited from the application of AI and ML techniques. However, there is still much to be learned from a full-scale exploration of these technologies in ALD. This book provides an in-depth look at the application of ML-based modeling techniques in ALD, offering a comprehensive understanding of the approaches, optimization, and prediction of the behavior and characteristics of ALD for improved process quality control and the discovery of new materials. The Need for Study and Understanding As we continue to advance in the field of technology, it is essential to study and understand the process of technology evolution. The need and possibility of developing a personal paradigm for perceiving the technological process of developing modern knowledge as the basis for the survival of humanity and the survival of the unification of people in a warring state cannot be overstated. The integration of AI and ML in ALD is a significant step towards achieving this goal.
Осаждение атомного слоя (ALD) является одной из таких технологий, которая получила большую выгоду от применения методов AI и ML. Тем не менее, еще многое предстоит узнать из полномасштабного изучения этих технологий в ALD. Эта книга содержит подробный обзор применения методов моделирования на основе ML в ALD, предлагая всестороннее понимание подходов, оптимизации и прогнозирования поведения и характеристик ALD для улучшения контроля качества процесса и открытия новых материалов., Потребность в изучении и понимании По мере того, как мы продолжаем продвигаться в области технологий, важно изучать и понимать процесс эволюции технологий. Необходимость и возможность выработки личностной парадигмы восприятия технологического процесса развития современного знания как основы выживания человечества и выживания объединения людей в воюющем государстве невозможно переоценить. Интеграция AI и ML в ALD является значительным шагом на пути к достижению этой цели.
Dépôt de couche atomique (ALD) est l'une de ces technologies qui a beaucoup bénéficié de l'application des techniques AI et ML. Cependant, il reste encore beaucoup à apprendre de l'étude approfondie de ces technologies dans ALD. Ce livre donne un aperçu détaillé de l'application des techniques de modélisation basées sur ML à ALD, offrant une compréhension complète des approches, de l'optimisation et de la prévision du comportement et des caractéristiques d'ALD pour améliorer le contrôle de la qualité du processus et la découverte de nouveaux matériaux., Besoin d'étude et de compréhension Alors que nous continuons à progresser dans le domaine de la technologie, il est important d'étudier et de comprendre le processus d'évolution de la technologie. La nécessité et la possibilité d'élaborer un paradigme personnel pour percevoir le processus technologique du développement de la connaissance moderne comme la base de la survie de l'humanité et de la survie de l'unification des gens dans un État en guerre ne peuvent être surestimées. L'intégration d'AI et de ML dans ALD est un pas important vers cet objectif.
La deposición de la capa atómica (ALD) es una de estas tecnologías que se ha beneficiado mucho de la aplicación de técnicas de IA y ML. n embargo, aún queda mucho por aprender del estudio a gran escala de estas tecnologías en ALD. Este libro ofrece una visión general detallada de la aplicación de técnicas de modelado basadas en ML en ALD, ofreciendo una comprensión integral de los enfoques, optimización y predicción del comportamiento y las características de ALD para mejorar el control de calidad del proceso y el descubrimiento de nuevos materiales., La necesidad de aprender y entender A medida que continuamos avanzando en el campo de la tecnología, es importante aprender y entender el proceso de evolución de la tecnología. No se puede exagerar la necesidad y la posibilidad de desarrollar un paradigma personal para percibir el proceso tecnológico del desarrollo del conocimiento moderno como base para la supervivencia de la humanidad y la supervivencia de la unión de los seres humanos en un Estado en guerra. La integración de AI y ML en ALD es un paso significativo hacia este objetivo.
O depósito da camada atômica (ALD) é uma dessas tecnologias que se beneficiou muito da aplicação dos métodos AI e ML. No entanto, ainda há muito a aprender com o estudo completo dessas tecnologias na ALD. Este livro traz uma visão detalhada da aplicação de técnicas de modelagem baseadas em ML na ALD, oferecendo uma compreensão completa das abordagens, otimização e previsão do comportamento e características da ALD para melhorar o controle da qualidade do processo e a descoberta de novos materiais. A necessidade e a possibilidade de criar um paradigma pessoal para a percepção do processo tecnológico de desenvolvimento do conhecimento moderno como base para a sobrevivência da humanidade e para a sobrevivência da união das pessoas num Estado em guerra não podem ser sobrevalorizadas. A integração entre AI e ML no ALD é um passo significativo para alcançar este objetivo.
Die Atomic Layer Deposition (ALD) ist eine dieser Technologien, die stark von der Anwendung der Methoden AI und ML profitiert hat. Es gibt jedoch noch viel zu lernen von der umfassenden Erforschung dieser Technologien bei ALD. Dieses Buch bietet einen detaillierten Überblick über die Anwendung von ML-basierten Modellierungsmethoden in ALD und bietet ein umfassendes Verständnis von Ansätzen, Optimierungen und Vorhersagen von ALD-Verhalten und -Eigenschaften, um die Prozessqualitätskontrolle zu verbessern und neue Materialien zu entdecken. Die Notwendigkeit und Möglichkeit, ein persönliches Paradigma für die Wahrnehmung des technologischen Prozesses der Entwicklung des modernen Wissens als Grundlage für das Überleben der Menschheit und das Überleben der Vereinigung der Menschen in einem kriegführenden Staat zu entwickeln, kann nicht überschätzt werden. Die Integration von KI und ML in ALD ist ein wichtiger Schritt zur Erreichung dieses Ziels.
''
Atomik katman biriktirme (ALD), AI ve ML yöntemlerinin uygulanmasından büyük ölçüde yararlanan böyle bir teknolojidir. Bununla birlikte, ALD'deki bu teknolojilerin tam ölçekli çalışmasından öğrenilecek çok şey var. Bu kitap, ALD'de ML tabanlı modelleme tekniklerinin uygulanmasına ayrıntılı bir genel bakış sunmaktadır. Süreç kalite kontrolünü ve yeni malzemelerin keşfini geliştirmek için ALD davranış ve özelliklerinin yaklaşımları, optimizasyonu ve tahmini hakkında kapsamlı bir anlayış sunmak., Çalışma ve anlayış ihtiyacı Teknolojide ilerlemeye devam ederken, Teknoloji evrimi sürecini incelemek ve anlamak önemlidir. Modern bilginin gelişiminin teknolojik sürecinin algılanması için kişisel bir paradigma geliştirmenin gerekliliği ve olasılığı, insanlığın hayatta kalması ve insanların savaşan bir durumda birleşmesinin hayatta kalması için temel olarak kabul edilemez. AI ve ML'nin ALD'ye entegrasyonu, bu hedefe ulaşmak için önemli bir adımdır.
ترسب الطبقة الذرية (ALD) هي واحدة من هذه التكنولوجيا التي استفادت بشكل كبير من تطبيق أساليب الذكاء الاصطناعي و ML. ومع ذلك، لا يزال هناك الكثير لنتعلمه من الدراسة الشاملة لهذه التقنيات في ALD. يقدم هذا الكتاب لمحة عامة مفصلة عن تطبيق تقنيات النمذجة القائمة على ML في ALD، تقديم فهم شامل للنهج والتحسين والتنبؤ بسلوك وخصائص ALD لتحسين مراقبة جودة العملية واكتشاف مواد جديدة.، الحاجة إلى الدراسة والفهم بينما نواصل التقدم في التكنولوجيا، ومن المهم دراسة وفهم عملية التطور التكنولوجي. ولا يمكن المبالغة في تقدير ضرورة وإمكانية وضع نموذج شخصي لتصور العملية التكنولوجية لتطور المعرفة الحديثة كأساس لبقاء البشرية وبقاء توحيد الشعوب في دولة متحاربة. ويمثل دمج الذكاء الاصطناعي و ML في ALD خطوة هامة نحو تحقيق هذا الهدف.

You may also be interested in:

Machine Learning-Based Modelling in Atomic Layer Deposition Processes
Machine Learning-Based Modelling in Atomic Layer Deposition Processes (Emerging Materials and Technologies)
Energy Efficiency and Robustness of Advanced Machine Learning Architectures A Cross-Layer Approach
Energy Efficiency and Robustness of Advanced Machine Learning Architectures A Cross-Layer Approach
Default Loan Prediction Based On Customer Behavior Using Machine Learning And Deep Learning With Python, Second Edition
Model-Based Machine Learning
Model-Based Machine Learning
Model-Based Machine Learning
Machine Learning A Constraint-Based Approach
ReRAM-based Machine Learning (Computing and Networks)
Pragmatic AI An Introduction to Cloud-Based Machine Learning
Simple Machine Learning for Programmers Beginner|s Intro to Using Machine Learning, Deep Learning, and Artificial Intelligence for Practical Applications
Machine Learning for Beginners A Complete and Phased Beginner’s Guide to Learning and Understanding Machine Learning and Artificial Intelligence Algoritms
Computational and Analytic Methods in Biological Sciences Bioinformatics with Machine Learning and Mathematical Modelling
Computational and Analytic Methods in Biological Sciences Bioinformatics with Machine Learning and Mathematical Modelling
Machine Learning-based Design and Optimization of High-Speed Circuits
Machine Learning for Kids A Project-Based Introduction to Artificial Intelligence
Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithms
Machine Vision Inspection Systems Machine Learning-Based Approaches (Machine Vision Inspection Systems, Volume 2)
Python Machine Learning The Ultimate Guide for Beginners to Machine Learning with Python, Programming and Deep Learning, Artificial Intelligence, Neural Networks, and Data Science
Debugging Machine Learning Models with Python: Develop high-performance, low-bias, and explainable machine learning and deep learning models
Machine Learning Adoption in Blockchain-Based Intelligent Manufacturing Theoretical Basics, Applications, and Challenges
Content-Based Image Classification Efficient Machine Learning Using Robust Feature Extraction Techniques
Design and Deploy Microsoft Defender for IoT Leveraging Cloud-based Analytics and Machine Learning Capabilities
Design and Deploy Microsoft Defender for IoT: Leveraging Cloud-based Analytics and Machine Learning Capabilities
Design and Deploy Microsoft Defender for IoT Leveraging Cloud-based Analytics and Machine Learning Capabilities
Blueprints for Text Analytics Using Python Machine Learning-Based Solutions for Common Real World (NLP) Applications
Machine Learning in Production: Master the art of delivering robust Machine Learning solutions with MLOps (English Edition)
Machine Learning for Business The Ultimate Artificial Intelligence & Machine Learning for Managers, Team Leaders and Entrepreneurs
Building Machine Learning Systems Using Python Practice to Train Predictive Models and Analyze Machine Learning Results
Machine Learning for Beginners An Introductory Guide to Learn and Understand Artificial Intelligence, Neural Networks and Machine Learning
Serverless Machine Learning with Amazon Redshift ML: Create, train, and deploy machine learning models using familiar SQL commands
Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices
Machine Learning for Data Streams with Practical Examples in MOA (Adaptive Computation and Machine Learning series)
Online Machine Learning: A Practical Guide with Examples in Python (Machine Learning: Foundations, Methodologies, and Applications)
Machine Learning Master Machine Learning Fundamentals for Beginners, Business Leaders and Aspiring Data Scientists
Machine Learning The Ultimate Guide to Understand AI Big Data Analytics and the Machine Learning’s Building Block Application in Modern Life
Machine Learning for Beginners Build and deploy Machine Learning systems using Python, 2nd Edition
Robust Machine Learning: Distributed Methods for Safe AI (Machine Learning: Foundations, Methodologies, and Applications)
Machine Learning A Guide to PyTorch, TensorFlow, and Scikit-Learn Mastering Machine Learning With Python