Xiaochun Yu Carnegie Mellon University

Multiple Defect Diagnosis

Experiments have demonstrated cases where failing circuits are affected by multiple defects. With increasing device and interconnect density, coupled with more pronounced variability, it is likely that more and more failing circuits will be affected by multiple defects. In order to increase yield and improve design quality, it is important to understand why circuits fail. Among current multiple defect diagnosis methodologies, some make restricting assumptions about defect behaviors. These methods cannot effectively diagnose circuits affected by defects with different behaviors from what the methods assume. Other multiple defect diagnosis methods make restricting assumptions about failing pattern characteristics. These methods fail when no or few failing patterns with the assumed characteristics are available.

In this talk, a generalized approach for diagnosing multiple defects based on a new error propagation analysis is presented. In this approach, no assumptions are made about defect behaviors or failing pattern characteristics. Results from simulation and silicon experiments are used to demonstrate the effectiveness of this new multiple-defect diagnosis methodology.

Bio

Xiaochun Yu received her B.S. degree in Microelectronics from Fudan University in 2005. She is currently a Ph.D. candidate in the department of Electrical and Computer Engineering at Carnegie Mellon University, where she is advised by Professor Shawn Blanton. Her research interests include various aspects of VLSI testing and diagnosis.