About This Course:
The Control of Manufacturing Processes course teaches how statistical modeling and control techniques are used to improve quality and reduce process variability in modern manufacturing environments. The course begins with fundamental concepts such as design of experiments, response surface modeling, analysis of variation sources, and parameter optimization.

It then progresses to advanced process control methods, including statistical process control, run-by-run and adaptive control, and real-time feedback systems. These topics are contextualized through application areas such as semiconductor manufacturing, metal and polymer processing, and micro/nano fabrication.

This course aims to equip professionals working in manufacturing engineering, quality control, and system integration with the analytical perspective needed to manage processes more efficiently, reliably, and optimally.

Prerequisites:
A basic understanding of probability, statistics, and differential equations is recommended. Introductory knowledge of manufacturing systems will also be beneficial.

Instructors:
Prof. David Hardt and Prof. Duane Boning are leading academics at MIT in the fields of manufacturing process control and systems engineering. They are well known for their theoretical and applied contributions to semiconductor manufacturing, statistical process control, and optimization of manufacturing systems. Through industry-integrated research, their teaching offers both academic depth and practical value.