As a silicon wafer winds its way through a semiconductor manufacturing plant\u2014from deposition and lithography to etching and dicing\u2014each stop at another machine loaded with sensors also generates information.<\/p>\n\n\n\n
And all those numbers, with advanced analysis, could be harnessed to further optimize the production process.<\/p>\n\n\n\n
\u201cThere are many gigabytes of data for each single product,\u201d says Andi Wang, a researcher whose work applies data science to advanced manufacturing processes. \u201cHow do you use that? Industry does not want to discard this data, but they do not know how to use it. This is where our research comes into play.\u201d<\/p>\n\n\n\n
Wang is joining the University of Wisconsin-Madison in fall 2024 as an assistant professor of industrial and systems engineering<\/a>, bringing experience employing a variety of machine learning and other analytical methods to real-world complex manufacturing systems.<\/p>\n\n\n\n He arrives in Madison after spending three years on the faculty of Arizona State University\u2019s School of Manufacturing Systems and Networks, which provided him with insights into emerging manufacturing technologies.<\/p>\n\n\n\n \u201cWhat I\u2019d like to do is bring that knowledge that I\u2019ve gained and also this experience in the manufacturing field back to industrial engineering,\u201d says Wang, who holds PhDs from Hong Kong University of Science and Technology and from Georgia Tech.<\/p>\n\n\n\n Specifically, Wang is interested in analyzing data to guide design decisions and to improve efficiency and performance in additive manufacturing systems and the semiconductor manufacturing industry. He\u2019s previously analyzed data from steel-rolling plants to uncover process and quality improvement strategies.<\/p>\n\n\n\n Wang has also expanded his research to include nuclear reactor design optimization. He currently leads a project, backed by a $1 million grant from the U.S. Department of Energy, to develop novel data-driven modeling and optimization methodologies (driven by the special characteristics of nuclear reactor core simulations) to shorten design cycles.<\/p>\n\n\n\n He says UW-Madison\u2019s longstanding excellence in nuclear engineering was a draw, along with a growing emphasis on semiconductor research across the College of Engineering.<\/p>\n\n\n\n \u201cI\u2019m impressed by the great research environment here,\u201d says Wang, who\u2019s seen it firsthand.<\/p>\n\n\n\n In 2015, Wang spent six months at UW-Madison as a visiting researcher while pursuing his first PhD, an experience that drove his interest in coming to the United States to continue his career. Several faculty members he met at UW-Madison, such as Professor Kaibo Liu<\/a>, who shares similar research interests as Wang<\/a>, have remained his academic mentors.<\/p>\n\n\n\n