Abstract: Provided is a system and method of generating training data for training a Deep Neural Network usable for examination of a semiconductor specimen. The method includes: obtaining a first training image and first labels respectively associated with a group of pixels selected in each segment, extract a set of features characterizing the first training image, train a machine learning (ML) model using the first labels, values of the group of pixels, and the feature values of each of the set of features corresponding to the group of pixels, process the first training image using the trained ML model to obtain a first segmentation map, and determine to include the first training image and the first segmentation map into the DNN training data upon a criterion being met, and to repeat the extracting of the second features, the training and the processing upon the criterion not being met.

Abstract: Provided is a system and method of generating training data for training a Deep Neural Network usable for examination of a semiconductor specimen. The method includes: obtaining a first training image and first labels respectively associated with a group of pixels selected in each segment, extract a set of features characterizing the first training image, train a machine learning (ML) model using the first labels, values of the group of pixels, and the feature values of each of the set of features corresponding to the group of pixels, process the first training image using the trained ML model to obtain a first segmentation map, and determine to include the first training image and the first segmentation map into the DNN training data upon a criterion being met, and to repeat the extracting of the second features, the training and the processing upon the criterion not being met.

Abstract: There is provided a system and method of generating training data for training a Deep Neural Network usable for examination of a semiconductor specimen. The method includes: obtaining a first training image and first labels respectively associated with a group of pixels selected in each segment, extract a set of features characterizing the first training image, train a machine learning (ML) model using the first labels, values of the group of pixels, and the feature values of each of the set of features corresponding to the group of pixels, process the first training image using the trained ML model to obtain a first segmentation map, and determine to include the first training image and the first segmentation map into the DNN training data upon a criterion being met, and to repeat the extracting of the second features, the training and the processing upon the criterion not being met.

Abstract: There is provided a system and method of generating training data for training a Deep Neural Network usable for examination of a semiconductor specimen. The method includes: obtaining a first training image and first labels respectively associated with a group of pixels selected in each segment, extract a set of features characterizing the first training image, train a machine learning (ML) model using the first labels, values of the group of pixels, and the feature values of each of the set of features corresponding to the group of pixels, process the first training image using the trained ML model to obtain a first segmentation map, and determine to include the first training image and the first segmentation map into the DNN training data upon a criterion being met, and to repeat the extracting of the second features, the training and the processing upon the criterion not being met.