Selecting the most relevant structural Fmax for system Fmax correlation

Abstract

The use of low-cost structural Fmax measurement as a replacement for in-system Fmax measurement for speed binning has been aided by the use of a data-learning approach that can be used to build a reliable system Fmax predictor given structural Fmax. This paper uses industry test measurements to demonstrate why a data-learning approach for correlation is better than simple correlation approaches, how to select the most relevant structural Fmax, and how the proposed methodology works on multiple lots.