RECOGNITION AND CLASSIFICATION OF WIRE BONDING JOINT VIA IMAGE FEATURE AND SVM MODEL

Abstract

Recognition and classification for wire bonding joint are important to quality assurance in semiconductor device manufacturing. In this study, a precision recognition and classification system for bonding joint of ultrasonic heavy aluminum wire based on image feature and support vector machine (SVM) is presented. This system consists of feature extraction from images and classification model. In feature extraction, image processing algorithms including Canny edge extraction, histogram equalization, and image morphology closed operation are utilized to extract and locate a joint contour in a complicated background image. In the classification model, principal component analysis (PCA) is employed to visualize, reconstruct and reduce the images data dimension for less computation time. SVM-based model is chosen as the classifier to identify and recognize joint types. Gauss-RBF kernel function is adopted in SVM and its optimal parameters are determined by cross validation. In the experiment, 588 bonding images are used to implement in this recognition and classification system. The results prove that the classification accuracy for wire bonding joint based on image feature, PCA and SVM can achieve to 97.3% and the computation time can be reduced significantly.

Existing System

Genetic algorithm (GA), vertical edge detection, chain code descriptor-based, morphology and template matching.

Proposed System

In this present study, we propose some image processing methods to locate and extract bonding joint features. In order to decrease the computing time, we use principal component analysis (PCA) as a pre-processing algorithm to decrease the image dimensions. Support vector machine (SVM), which is an intelligent machining learning algorithm, is utilized to classify the joint types.

Architecture

BLOCK DIAGRAM