An Optical System for the Ball Grid Array Inspection and Measurement Using the Back-Propagation Neural Network Technology

Abstract

In this study, the back-propagation neural network technology (BPN) is utilized to identify the shape of the defective solder ball of ball grid array (BGA) so as to promote the accuracy of the optical inspection and measurement. The two dimensional BGA optical inspecting system is implemented by Visual Basic as the developing tool incorporated with the Halcon’s function which is the database of the image processing on Windows operation system. For the development of the processing procedure of the automatic optical inspecting system, the precise geometrical information of the solder ball is evaluated by the sub-pixel method to identify the shape of solder ball and its location which are acquired to classify the defects of solder ball including the ball offset, the ball over scale, the ball absence, and the ball shape under the BGA board is offset and rotated at any angle. From the experimental results, the back-propagation neural network technology is proved to properly identify and classify the shape defects, especially for the ball deformation and the ball bridging of the solder ball which can achieve and contribute the requirements for the automatic inspection and the high identification efficiency.