TY - GEN
T1 - Detection of sealant delamination in integrated circuit package using a multivariate Gaussian model
AU - Li, Guoyi
AU - Ikram, Javaid
AU - Chattopadhyay, Aditi
AU - Neerukatti, Rajesh Kumar
AU - Liu, Kuang C.
N1 - Funding Information:
This work was partly realized as part of the Quaero Programme, funded by OSEO, French State agency for innovation. The authors would like to thank Qingyue He for her support and Ngoc Thang Vu for useful discussions.
Publisher Copyright:
© 2019 SPIE.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Delamination Detection
KW - Integrated Circuit Package
KW - Multivariate Gaussian
KW - Ultrasonic Guided Wave
KW - Unsupervised Learning
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U2 - 10.1117/12.2513790
DO - 10.1117/12.2513790
M3 - Conference contribution
AN - SCOPUS:85069633895
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Smart Structures and NDE for Energy Systems and Industry 4.0
A2 - Meyendorf, Norbert G.
A2 - Gath, Kerrie
A2 - Niezrecki, Christopher
PB - SPIE
T2 - Smart Structures and NDE for Energy Systems and Industry 4.0 2019
Y2 - 4 March 2019 through 5 March 2019
ER -