Researchers Win Best Paper Award at DAC

 

June 22, 2010

ECE Ph.D. student Wangyang Zhang and his advisers, Assistant Research Professor Xin Li and Adjunct Professor Rob Rutenbar, recently won a Best Paper Award at the 47th Design Automation Conference (DAC), held June 13-18 in Anaheim, Calif. Their paper, "Bayesian Virtual Probe: Minimizing Variation Characterization Cost for Nanoscale IC Technologies Via Bayesian Inference," developed a new methodology to adaptively characterize process variation of silicon wafers from a minimum set of measurement data.

As integrated circuit (IC) technology approaches the nanoscale, process variation has become a critical issue that must be carefully considered for today's IC design. The new Bayesian virtual probe (BVP) methodology developed by the Carnegie Mellon team allows IC foundries to characterize process variation at very few spatial locations on silicon wafers. Based on this characterization data, BVP accurately predicts the variation at all other locations by Bayesian inference and, thus, substantially reduces the characterization cost of advanced manufacturing technology.

This year, 10 papers out of the 608 submitted were nominated for the Best Paper Award, and the DAC Award Committee selected one winner from the final group of 10. The winners received a prize of $500. For more information on DAC, visit www.dac.com.

ECE Ph.D. student Wangyang Zhang and his advisers, Assistant Research Professor Xin Li and Adjunct Professor Rob Rutenbar, recently won a Best Paper Award at the 47th Design Automation Conference.

The Bayesian Virtual Probe (BVP) methodology can adaptively characterize process variation of silicon wafers from a minimum set of measurement data.

Related People:

Rob Rutenbar

Xin Li