Predicting Lifetime of Thick Al Wire Bonds Using Signals Obtained From Ultrasonic Generator

Routine monitoring of the wire bonding process requires real-time evaluation and control of wire bond quality. In this paper, we present a nondestructive technique for detecting bond quality by the application of a semisupervised classification algorithm to process the signals obtained from an ultrasonic generator. Experimental tests verified that the classification method is capable of accurately predicting bond quality, indicated by bonded area measured by X-ray tomography. Samples classified during bonding were subjected to temperature cycling between -55 °C and +125 °C, and the distribution of bond life amongst the different classes was analyzed. It is demonstrated that the as-bonded quality classification is closely correlated with thermal cycling life and can, therefore, be used as a nondestructive tool for monitoring bond quality and predicting useful service life.