There exist meaningful patterns in production test data and design validation data of integrated circuits. Test data analytics, which explores the hidden patterns and correlations in the test data, has a wide range of applications such as reducing test time, improving test quality, identifying outliers for diagnosis, discovering weak links in the manufacturing process, and improving the design robustness. There are clear benefits to formulate test problems as data analysis problems and employ tools in statistical modeling and data mining for uncovering hidden patterns, unknown correlations and other useful information in the test data. In the talk, we describe some promising directions in test data analytics and discuss some of their applications.
Cheng received his Ph.D. in EECS from the University of California, Berkeley. He worked at Bell Laboratories from 1988 to 1993 and joined the faculty at the University of California, Santa Barbara in 1993 where he is currently Acting Associate Vice Chancellor for Research and Professor of ECE. He was the founding director of UCSB’s Computer Engineering Program (1999-2002) and Chair of the ECE Department (2005-2008). He held a Visiting Professor position at National TsingHua Univ. (1999), Univ. of Tokyo, Japan (2008), and Hong Kong Univ. of Science and Technology (2012). His current research interests include mobile embedded systems, SoC design validation and test, and multimedia computing. He currently serves as Director for AFOSR MURI Center for 3D hybrid circuits which aims at 3D monolithic integration of CMOS with high-density crossbar arrays of memristors.
Cheng, an IEEE fellow, received 10+ Best Paper Awards from various IEEE conferences and journals. He has also received the 2004-2005 UCSB College of Engineering Outstanding Teaching Faculty Award. He served as Editor-in-Chief of IEEE Design and Test of Computers and was a board member of IEEE Council of Electronic Design Automation’s Board of Governors and IEEE Computer Society’s Publication Board.