IEEE Trans Neural Netw Learn Syst. 2015 May;26(5):933-50. doi: 10.1109/TNNLS.2014.2329097. Epub 2014 Jul 1.
Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.
使用智能系统进行晶圆缺陷检测是半导体制造中提高质量的一种方法，旨在提高其工艺稳定性、增加产能并提高产量。偶尔，只有少数记录表明存在缺陷的单元，并且它们在大型数据库中被归类为少数群体。这种情况导致数据集不平衡问题，这给应用机器学习技术来获得有效解决方案带来了巨大挑战。此外，数据库可能包含不同类别的重叠样本。本文介绍了两种进化模糊 ART MAP（FAM）神经网络模型，以解决半导体制造操作中的数据集不平衡问题。特别是，FAM 模型和混合遗传算法都集成在提出的进化人工神经网络（EANNs）中，以对不平衡数据集进行分类。此外，提出的 EANN 之一具有从不平衡数据环境中学习不同类别的重叠样本的功能。使用几种分类指标对所提出的进化 FAM 神经网络的分类结果进行了呈现、比较和分析。结果积极表明了所提出的网络在处理不平衡数据集的分类问题方面的有效性。
