Life prediction of copper wire bonds in commercial devices using principal component analysis (PCA)

Manoharan, Subramani; Patel, Chandradip; Dunford, S.; Beshears, John; McCluskey, Patrick

doi:10.1016/j.microrel.2019.05.013

Cited by 7 publications

(1 citation statement)

References 35 publications

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“…To obtain the required background data for the INFPLS model, the aforementioned authors used PCA to eliminate the experimental data's collinearity. Manoharan et al 10 developed a data-based predictive method on the basis of PCA to predict the wire bond time to failure for plastic encapsulated components. Uddin et al 11 used a PCA-based assessment for identification of vulnerable areas in Bangladesh's coastal region.…”

Section: Related Workmentioning

confidence: 99%

Dynamic weight-based learning method for data detection in manufacturing

Lin

2014

Advances in Mechanical Engineering

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In this study, a dynamic weight-based method combined with principal component analysis (PCA) was developed for the first time for detecting measurement data in manufacturing. This weight-based learning technique can learn and train the measurement data sequence to isolate incorrect data sources for achieving high accuracy when detecting various types of data. Research has revealed that unsuitable image or data features might cause poor performance in industrial inspections. In contrast to the previous inspection methods, the weight-based learning method proposed in this study employs a dynamic learning algorithm for effectively and adaptively selecting optimal principle components to the support vector machine (SVM) algorithm and then establishes indicators. Finally, these PCA-based indicators act as substitutes for massive amounts of data in data processing and can be applied to timely detect data when the data contain redundant and incorrect inputs in a sequence. The experimental results indicate that the proposed method, which combines dynamic weight-based feature extraction with PCA, can provide useful indicators for detecting various types of manufacturing data and exhibited satisfactory performance in the data detection.

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Section: Related Workmentioning

confidence: 99%