Semiconductor Manufacturing: AI-Driven Data Collection and Preprocessing
In this course, learners will explore how data can significantly enhance AI applications in semiconductor manufacturing. Learners will delve into advanced data collection techniques, such as using IoT sensors and edge computing, to optimize data gathering and monitoring. They will also learn key data preprocessing methods, including cleaning, transformation, and feature engineering, which are crucial for boosting AI model performance. Through detailed case studies, the course will enable learners to analyze real-world scenarios to gain practical insights. Finally, best practices will be applied to optimize AI-driven outcomes, improve data quality, and tackle challenges in semiconductor manufacturing.
What you will learn:
- How to investigate advanced data collection methods in semiconductor manufacturing and assess their impact on AI applications
- How to utilize data preprocessing techniques to enhance data quality and deploy effective data quality assurance measures
- Gain the ability to evaluate real-world case studies to extract practical insights and best practices for data utilization
This course is part of the following Course Program:
Mastering AI Integration in Semiconductor Manufacturing
Courses included in this program:
Who Should Attend: AI Engineers, Edge Computing Engineers, Graduates and Undergraduates in Computer Science, Engineering, and Nanotechnology-related fields, Materials Scientists, Nanotechnologists, Nanotechnology Research Scientists, Professors and Lecturers in Computer Science and Engineering, Research and Technology Policy Professionals, Researchers in Nanomaterials and Nanoelectronics, Sustainability and Conservation Specialists, Semiconductor Industry Professionals, Semiconductor Product Development Managers, System Architects
Instructor
Dr. Santhosh Sivasubramani
Dr. Santhosh Sivasubramani is the working group chair of IEC/IEEE P62659™, the International Standard for Nanomanufacturing in Large Scale Manufacturing for Nanoelectronics. Sivasubramani is a Senior Member of IEEE as well as a member of the IEEE Nanotechnology Council Technical Committee 12 on Nanomagnetics and the IEEE Task Force on Rebooting Computing. Sivasubramani won the IEEE Member Geographic Activities Young Professional Achievement Award in 2023. He is a contributor to the IEEE International Roadmap of Devices and Systems: Beyond CMOS Working Group Report, in addition to serving as the IEEE NTC Standards Committee Secretary. Dr. Sivasubramani gained post-Ph.D. academic and industrial experience in the Advanced Embedded Systems and IC Design Laboratory and the Nanomagnetics Research Lab, demonstrating a versatile blend of expertise. He also contributes as a Track/Session Chair for IEEE international conferences and Founder of RSL Quantum. Dr. Sivasubramani graduated with a Ph.D. in Rebooting Computing from the Indian Institute of Technology Hyderabad (IITH).
Publication Year: 2025
ISBN: 978-1-7281-7884-4