Classification of Mixed-Type Defect Patterns in Wafer Bin Maps Using Convolutional Neural Networks

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In semiconductor manufacturing, a wafer bin map (WBM) represents the results of wafer testing for dies using a binary pass or fail value. For WBMs, defective dies are often clustered into groups of local systematic defects. Determining their specific patterns is important, because different patterns are related to different root causes of failure. Recently, because wafer sizes have increased and the process technology has become more complicated, the probability of observing mixed type defect patterns, i.e., two or more defect patterns in a single wafer, has increased. In this paper, we propose the use of convolutional neural networks (CNNs) to classify mixed-type defect patterns in WBMs in the framework of an individual classification model for each defect pattern. Through simulated and real data examples, we show that the CNN is robust to random noise and performs effectively, even if there are many random defects in WIIMs.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2018-08
Language
English
Article Type
Article
Keywords

AUTOMATIC IDENTIFICATION; RECOGNITION

Citation

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING, v.31, no.3, pp.395 - 402

ISSN
0894-6507
DOI
10.1109/TSM.2018.2841416
URI
http://hdl.handle.net/10203/244949
Appears in Collection
IE-Journal Papers(저널논문)
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