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Bruce W. McGaughy

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We present a unique case study of applying data mining and advanced statistical methods to analyze, diagnose and improve manufacturing yield, especially for rare failure event prediction. Intra-die process variations in nanometer technology nodes pose significant challenges to robust design practices. Geometric variations along with random dopant fluctuation effects have had significant impact on Memory functionality/yield. Inaccuracies in the models and variabilities in the process are more pronounced and force us to understand the variability effects in a processor chip with higher accuracy and fidelity and considering more physical effects than ever before. In this case study, we use predictive failure analytics to learn and optimize critical components of the processor, and deal with massive amounts of data using server farms for parallel processing. The techniq... (more)