Multi-wafer virtual probe: minimum-cost variation characterization by exploring wafer-to-wafer correlation

Authors:
Wangyang Zhang;Xin Li;Emrah Acar;Frank Liu;Rob Rutenbar
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;IBM T. J. Watson Research Center, Yorktown Heights, NY;IBM Austin Research Lab, Austin, TX;University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
Proceedings of the International Conference on Computer-Aided Design
Year:
2010

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Abstract

In this paper, we propose a new technique, referred to as Multi-Wafer Virtual Probe (MVP) to efficiently model wafer-level spatial variations for nanoscale integrated circuits. Towards this goal, a novel Bayesian inference is derived to extract a shared model template to explore the wafer-to-wafer correlation information within the same lot. In addition, a robust regression algorithm is proposed to automatically detect and remove outliers (i.e., abnormal measurement data with large error) so that they do not bias the modeling results. The proposed MVP method is extensively tested for silicon measurement data collected from 200 wafers at an advanced technology node. Our experimental results demonstrate that MVP offers superior accuracy over other traditional approaches such as VP [7] and EM [8], if a limited number of measurement data are available.