| dc.description.abstract | The yield rate of a product is a crucial factor in determining its final value, since the semiconductor manufacturing process is complex and varied. Product defects accumulate over different processes, making it essential to set up detection points in the manufacturing processes. However, the product won′t be able to enter the next process while detecting, which leads to a huge time cost. Currently, fetching the data of the product from scanning machines, and randomly sampling the defects, couldn′t solve the problem of the key product defects detection effectively. Therefore, analyzing the data from the inspection machine, and find out the current state of the product, will improve the yield of the product effectively, and reduce the measurement time.
Base on the basis mentioned about, this thesis uses artificial intelligence stacking model to establish a product defect prediction model based on the actual product detection data from a domestic factory, aiming at the imbalance between product defect and non-defect data, and through the combination of different machine learning models, in order to improve the detection speed and
accuracy. | en_US |