Machine Learning-Based Modelling in Atomic Layer Deposition Processes 1st Edition

Capture the forefront of thin-film technology with Machine Learning-Based Modelling in Atomic Layer Deposition Processes, 1st Edition by Oluwatobi Adeleke, Sina Karimzadeh, and Tien-Chien Jen. This authoritative volume brings together the latest in data-driven modeling and ALD (Atomic Layer Deposition) science, tailored for researchers, process engineers, and graduate students in semiconductor manufacturing and materials science.

Discover a clear, practical pathway from fundamentals to advanced applications: the authors explain how machine learning techniques can predict film growth, optimize process windows, and accelerate materials discovery. Richly-focused chapters cover data acquisition and preprocessing, feature engineering for ALD parameters, model selection and validation, and strategies to interpret predictive results—bridging theory with experimental insights relevant to R&D labs and fabs in Silicon Valley, South Korea, Taiwan, Japan, Europe, and beyond.

Imagine reducing development cycles and improving yield through predictive models that speak directly to real-world ALD challenges. Whether you are developing next-generation nanoscale films or integrating AI-driven control in production, this book provides actionable frameworks, comparative analyses, and best-practice recommendations to implement robust, reproducible models across industry and academia.

Ideal for practitioners in semiconductor manufacturing, thin-film research, and applied machine learning, this edition is written in an accessible yet technically rigorous style. Equip your team with the knowledge to translate data into high-performance ALD processes.

Enhance your library with this essential resource—order your copy today and lead the transition to smarter, faster, data-driven ALD innovation.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.