© Part Average Analysis PAA Rev. 2.0 / Basic Methodology P AA

© Summary Short discription With increasing complexity of technical systems the quality requirements grows up for subsystems, modules and parts. The Part Average Analysis shows a methodical approach to improve the reliability and availability of technical systems efficiently. This method enables the detection of abnormal data or functionalities in series processes, which gives a sure reference to latent or “sleeping” faults. Components with a high loss risk will be preventively selected and be blocked for further process steps. Fokus The goal of this method is an early fault detection in order to improve the value process of technical systems.

© Basis References The basis for the PAA is the part AVERAGE test PAT: „Guidelines for Part Average Testing“ AEC – Q001 Rev. B, Typical application: Automotive semiconductor wafer test Detection of oulier chips to select them (inking) when data are inside of the specified range but outside of 6 sigma distance to the statistic average Optimized mathematical algorithm for Wafertesting includes testing and inking in one operation step No fault risk assessment

© Anomaly Testing Example The Part Average Analysis evaluates passed parts, whose parameter values are within a specified range which shows a significant different behavior in comparison to other parts Result on top: 3 defect arrays in a lot of 2500 pieces detected by abnormalities with the parameter „current consumtion“ In opposite to the PAT, the PAA evaluates the significance of anomalies based on technological criteria and selects components only when a loss risk has been identified unambiguously Pressure Sensor Array 66 66 Inside of specified range I/mA l number