Feature extraction and health monitoring using image correlation for survivability of leadfree packaging under shock and vibration

Abstract

In this paper, the feature extraction for health monitoring based on optical measurements of transient-strain from digital image correlation (DIC) in conjunction with ultra high-speed imaging has been investigated. Full-field measurement of transient strain have been made in various board assemblies subjected to shock in various orientations. Feature-extraction for health monitoring of leadfree area array architectures based on statistical pattern recognition has been presented. Previous researchers have measured the transient-dynamics of board assemblies with high-speed imaging in conjunction with high-speed image analysis for measurement of relative displacement, angle, velocity, and acceleration [Lall 2006, Che 2006], high-speed data-acquisition systems with discrete strain gages [Lall 2004, 2005, Liang 2005] and with accelerometers for measurement of transient acceleration [Dunford 2004, Goyal 2000, Seah 2005]. Degradation in confidence value gives a leading indication of component failure. Package architectures examined include-flex ball-grid arrays, tape-array ball-grid arrays, and metal lead-frame packages. Statistical pattern recognition techniques including, mahalanobis- distance approach, wavelet packet energy decomposition, and time-frequency (TFA) techniques have been investigated for system identification, condition monitoring, and fault detection and diagnosis in electronic systems. Explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, package fall off and solder ball failure. Models developed include, smeared property models, Timoshenko-beam models, and explicit sub-models. Explicit finite-element models have been correlated with experimental data. The presented approach does not depend on continuity and therefore does not need daisy-chained devices for detection of failure.