Ultrasonic guided waves have the potential to inspect integrated circuit (IC) packages using wave based techniques due to excellent sub surface penetration through metallic as well as dielectric material. Guided waves in a heterogeneous composite assembly such as an IC package have modes with complex dispersion characteristics due to multiple layers of material with intricate geometry. No analytical solution exists for predicting dispersion in highly anisotropic composites. Numerical methods, such as the finite element method, have been used to model dispersion in composites, however these methods are computationally intensive and not feasible for predicting dispersion in IC packages. In this paper, the time-frequency characteristics of guided waves propagating through a complex IC are studied using the synchrosqueezing transform (SST). This is a transform that has been shown to be robust to bounded signal perturbations, to provide highly localized time and frequency information for highly nonlinear modes, and to reconstruct the signal corresponding to each mode. Reference ultrasonic guided wave signals are collected for the IC package in its healthy and damaged states using piezoelectric transducers to characterize the dispersion modes in the excitation region. Initial results demonstrate that the dispersive mode information from the extracted SST ridges provide an effective damage indicator for IC packaging.
This paper presents the development of a delamination detection framework for integrated circuit packages aiming at quantitative detection of sealant delamination between integrated heat sink and substrate, which is one of the potential failure mechanisms in integrated circuit packages. This method is expected to overcome the destructive nature of most existing techniques and maintain a relatively low cost of development. Ultrasonic guided waves are used as the interrogation method due to their sensitivity to small-size damage and capability of through-thickness penetration. The complexity of the received ultrasonic signals, caused by the geometric heterogeneity, is resolved and interpreted using a time-frequency signal processing technique. The extracted ultrasonic information, including time-of-arrival and amplitude of wave modes received from different sensing paths under multiple excitation frequencies, is used to construct the feature space for training. An unsupervised learning method, multivariate Gaussian model, is implemented as an information fusion and delamination detection tool. The multivariate Gaussian model efficiently investigates the distribution of feature space including correlations between features and flag the outliers without labeled examples. Results from the developed model are compared with two existing evaluation methods, including pullout test and a metric indicating the extent of delamination, which indicates that the developed method possesses a similar level of accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.