Improved Color Defect Detection With Machine Learning for After Develop Inspections in Lithography
Improved Color Defect Detection With Machine Learning for After Develop Inspections in Lithography
复制标题
通过机器学习改进光刻中开发后检测的颜色缺陷检测
DOI：
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发表时间：
2022
影响因子：
2.7
通讯作者：
C. Menser
中科院分区：
文献类型：
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作者：
Matthew P. McLaughlin;P. Mennell;A. Stamper;Gabriel Barber;Janice Paduano;Emerson Benn;M. Linnane;Justin Zwick;Chetan Khatumria;Robert L. Isaacson;Nathan Hoffman;C. Menser
随着用于光刻后显影检测的机器学习(ML)方法的发展，颜色缺陷检测得到了改进。对于涂层缺陷，在与参考方法的试验比较中，该方法显示出两倍的敏感性和三倍的特异性。使用ML方法进行处理，并针对涂层缺陷进行配方优化、预测性维护和返工，从长远来看，将飞溅造成的产量损失减少了20倍以上。在这里，我们描述了关于使用图像增强进行训练和处理的学习，用于支持理解的可解释的人工智能应用，以及基于性能的训练增强的过程流。
Color defect detection was improved with the development of a Machine Learning (ML) method for after develop inspections in lithography. For coating defects, the method exhibited two times the sensitivity and three times the specificity in a trial comparison against the reference method. Using the ML method for disposition along with recipe optimization, predictive maintenance, and rework for coating defects, reduced yield loss from splatters in the long run by over 20x. Herein we describe learnings on the use of image enhancement for training and disposition, an Explainable AI application to support understanding, and a process flow to train augmentation based on performance.