Design and Development of a Deep Learning Model for Electronic Manufacturing Industry Using TeetuNet
Keywords:
Artificial Intelligence, Semiconductor wafer, Electronic chip Manufacturing, Deep LearningAbstract
One of the biggest challenges in electronic manufacturing industry is to take care of quality check process of manufacturing semiconductor wafers. Post pandemic the supply chain for electronic chips has disturbed badly and the supply is much less than the demand all over the world. The present systems are not as efficient and smart to reduce the testing time of semiconductor wafer drastically. Many a times it so happens that one or the other kind of defects are present in the manufactured wafer which degrades the quality of wafer and adds up more time to produce same number of wafers. Therefore a much more efficient, reliable quality check system is necessary to tackle the issue. Since the advantage of Artificial Intelligence is in almost every field nowadays, in this paper we propose a much more capable Deep Learning model based on CNN, which can detect a defected and non defected wafer using image. Unlike the present systems where all the testing work is done using sensor data points, this Deep Learning model process on the image of wafer and gives results with greater accuracy.
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