Share Email Print
cover

Proceedings Paper • new

Detection of sealant delamination in integrated circuit package using a multivariate Gaussian model
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

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.

Paper Details

Date Published: 18 March 2019
PDF: 10 pages
Proc. SPIE 10973, Smart Structures and NDE for Energy Systems and Industry 4.0, 109730H (18 March 2019); doi: 10.1117/12.2513790
Show Author Affiliations
Guoyi Li, Arizona State Univ. (United States)
Javaid Ikram, Arizona State Univ. (United States)
Aditi Chattopadhyay, Arizona State Univ. (United States)
Rajesh Kumar Neerukatti, Intel Corp. (United States)
Kuang C. Liu, Intel Corp. (United States)


Published in SPIE Proceedings Vol. 10973:
Smart Structures and NDE for Energy Systems and Industry 4.0
Norbert G. Meyendorf; Kerrie Gath; Christopher Niezrecki, Editor(s)

© SPIE. Terms of Use
Back to Top