Indexed on: 24 Jun '10Published on: 24 Jun '10Published in: Journal of Intelligent Manufacturing
Ultrasonic wire bonding is one of the most frequently used techniques in semiconductor production to establish electrical interconnections. Improper bonding process parameters, wire or substrate contamination or low substrate quality are some of the causes of failed bonds. Process integrated wire-bond quality control techniques compare process feedback signals to a reference for monitoring online the quality of a bond. The feedback signals sampled at high frequencies, constitute high dimensional vectors representing the bonding process characteristics. In the area of online bond failure detection, dimensionality reduction of the input signals and feature extraction of the characteristics of the process are very demanding. Cytokine-Formal Immune Network (cFIN) is a procedure for pattern recognition which presents a low recognition failure rate and a fast recognition due to the reduction of dimensions and feature extraction of the training pattern data set produced in the learning phase. We use cytokine-Formal Immune Networks for recognizing faults present during the wire bonding process. The recognition methodology is intended to be applied into a process integrated quality control system. Further an automated optimization procedure has been developed to find optimal cFIN training parameters. Very promising results for two wire bonding process setups are shown in this paper.