Adaptation of neural network and application of digital ultrasonic image processing for the pattern recognition of defects in semiconductor

In this study, the classification of artificial defects in semiconductor devices are performed by using pattern recognition technology. For this target, a pattern recognition algorithm including user made software was developed and the total procedure including image processing and self-organizing map was treated by a backpropagation neural network, where image processing was composed of ultrasonic image acquisition, equalization filtering, binary processing and edge detection. Image processing and self-organizing map were compared as preprocessing methods for the reduction of dimensionality as input data into multi-layer perceptron or backpropagation neural networks. Also, the pattern recognition technique has been applied to classify two kinds of semiconductor defects: cracks and delamination. According to these results, it was found that the self-organizing map provided recognition rates of 83.4% and 75.7% for delamination and cracks, respectively, while BP provided 100% recognition rates for the results.

1. Introduction

Semiconductor components are essential to electronic devices such as medical equipments, military weapons and so on. Therefore, defects in semiconductor components may affect the mechanical or electronic performance of devices. For these reason, it is very important to detect such defects in the manufacturing process. Some detection methods for semiconductor components largely depend on nondestructive tests and have been accomplished by human experrience. Recently, defects detection system for semiconductor like SAT(Scanning Acoustic Tomograph) has been adopted in the actual manufacturing process. Manufacturers like HITACIII and SONIX provide powerful image results with users of ultrasonic nondestructive methods. It help users to analyze displayed results on the screen.