Wafer bin maps (WBM) provides crucial information regarding process abnormalities and facilitate the diagnosis of low-yield problems in semiconductor manufacturing. Most studies of WBM classification and analysis apply a statistical-based method or machine learning method operating on raw wafer data and extracted features. With increasing WBM pattern diversity and complexity, the useful features for effective WBM recognition are highly dependent on domain knowledge. This study proposes an ensemble convolutional neural network (ECNN) framework for WBM pattern classification, in which a weighted majority function is adopted to select higher weights for the base classifiers that have higher predictive performance. An industrial WBM dataset (namely, WM-811K) from a wafer fabrication process was used to demonstrate the effectiveness of the proposed ECNN framework. The proposed ECNN has superior performance in terms of precision, recall, F1 and other conventional machine learning classifiers such as linear regression, random forest, gradient boosting machine, and artificial neural network. The experimental results show that the proposed ECNN framework is able to identify common WBM defect patterns effectively.

晶圆仓图（WBM）提供有关工艺异常的重要信息，并有助于诊断半导体制造中的低良率问题。WBM分类和分析的大多数研究都采用基于统计的方法或机器学习方法来处理原始晶圆数据和提取的特征。随着WBM模式多样性和复杂性的增加，有效识别WBM的有用功能高度依赖领域知识。这项研究提出了用于WBM模式分类的集成卷积神经网络（ECNN）框架，其中采用加权多数函数为具有较高预测性能的基本分类器选择较高的权重。工业WBM数据集（即，晶圆制造过程中的WM-811K）被用来证明所提出的ECNN框架的有效性。提出的ECNN在精度，召回率，F1和其他常规机器学习分类器（例如线性回归，随机森林，梯度提升机和人工神经网络）方面均具有出色的性能。实验结果表明，提出的ECNN框架能够有效地识别常见的WBM缺陷模式。