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http://dx.doi.org/10.4313/JKEM.2018.31.7.450

Defect Prediction Using Machine Learning Algorithm in Semiconductor Test Process  

Jang, Suyeol (Department of Electrical Engineering, Korea University)
Jo, Mansik (Department of Electrical Engineering, Korea University)
Cho, Seulki (Department of Electrical Engineering, Korea University)
Moon, Byungmoo (Department of Electrical Engineering, Korea University)
Publication Information
Journal of the Korean Institute of Electrical and Electronic Material Engineers / v.31, no.7, 2018 , pp. 450-454 More about this Journal
Abstract
Because of the rapidly changing environment and high uncertainties, the semiconductor industry is in need of appropriate forecasting technology. In particular, both the cost and time in the test process are increasing because the process becomes complicated and there are more factors to consider. In this paper, we propose a prediction model that predicts a final "good" or "bad" on the basis of preconditioning test data generated in the semiconductor test process. The proposed prediction model solves the classification and regression problems that are often dealt with in the semiconductor process and constructs a reliable prediction model. We also implemented a prediction model through various machine learning algorithms. We compared the performance of the prediction models constructed through each algorithm. Actual data of the semiconductor test process was used for accurate prediction model construction and effective test verification.
Keywords
Machine learning; Semiconductor test process; Prediction model; Classification; Package test;
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