This paper introduces proteanTecs groundbreaking Outlier Detection solution that eliminates that tradeoff. proteanTecs' Outlier Detection uses deep data analytics and ML to detect latent defects as early as Wafer Sort, achieving high fault detection accuracy by learning normal behavior with on-chip agents and comparing test measurements with predicted ones. It identifies marginal issues beyond simple pass/fail metrics, where traditional methods fail. ‍

This paper introduces proteanTecs groundbreaking Outlier Detection solution that eliminates that tradeoff. proteanTecs' Outlier Detection uses deep data analytics and ML to detect latent defects as early as Wafer Sort, achieving high fault detection accuracy by learning normal behavior with on-chip agents and comparing test measurements with predicted ones. It identifies marginal issues beyond simple pass/fail metrics, where traditional methods fail. ‍

This paper introduces proteanTecs groundbreaking Outlier Detection solution that eliminates that tradeoff. proteanTecs' Outlier Detection uses deep data analytics and ML to detect latent defects as early as Wafer Sort, achieving high fault detection accuracy by learning normal behavior with on-chip agents and comparing test measurements with predicted ones. It identifies marginal issues beyond simple pass/fail metrics, where traditional methods fail. ‍