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METHOD OF DEEP LEARINING-BASED EXAMINATION OF A SEMICONDUCTOR SPECIMEN AND SYSTEM THEREOF

机译:基于深度学习的半导体专业考试方法及其系统

摘要

There are provided system and method of examining a semiconductor specimen. The method comprises: upon obtaining a Deep Neural Network (DNN) trained for a given examination-related application within a semiconductor fabrication process, processing together one or more fabrication process (FP) images using the obtained trained DNN, wherein the DNN is trained using a training set comprising ground truth data specific for the given application; and obtaining examination-related data specific for the given application and characterizing at least one of the processed one or more FP images. The examination-related application can be, for example, classifying at least one defect presented by at least one FP image, segmenting the at least one FP image, detecting defects in the specimen presented by the at least one FP image, registering between at least two FP images, regression application enabling reconstructing the at least one FP image in correspondence with different examination modality, etc.
机译:提供了一种检查半导体样品的系统和方法。该方法包括:在获得在半导体制造过程中针对给定的检查相关应用而训练的深度神经网络(DNN)之后,使用所获得的训练后的DNN一起处理一个或多个制造过程(FP)图像,其中,使用训练集,包括特定于给定应用程序的地面真相数据;获得特定于给定应用的检查相关数据,并表征至少一张已处理的FP图像。与检查有关的应用可以是例如对至少一个FP图像呈现的至少一种缺陷进行分类,分割至少一个FP图像,检测由至少一个FP图像呈现的样本中的缺陷,在至少两张FP图像,回归应用程序可根据不同的检查方式重建至少一张FP图像等。

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