Generating training data usable for examination of a semiconductor specimen

A computerized method for generating training data for training a Deep Neural Network (DNN training data) usable for examination of a specimen, the method performed by a processor and memory circuitry (PMC) and comprising:

• obtaining a first training image representative of at least a portion of the semiconductor specimen, and first labels respectively associated with a group of pixels selected in each of one or more segments identified by a user from the first training image;
• extracting a set of features characterizing the first training image, each feature having feature values corresponding to pixels in the first training image, the set of features including first features informative of contextual relations between the one or more segments in the first training image, and second features informative of pixel distribution in the first training image relative to a statistical measure of the group of pixels in each segment;
• training a machine learning (ML) model using the first labels, values of the group of pixels selected in each segment associated with the first labels, and the feature values of each feature of the set of features corresponding to the group of pixels in each segment, wherein the ML model is trained for image segmentation;
• processing the first training image using the trained ML model to obtain a first segmentation map informative of predicted labels associated with respective pixels in the first training image, each predicted label indicative of a segment to which a respective pixel belongs;
• and
• determining to include a first training sample comprising 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.

A non-transitory computer readable storage medium tangibly embodying a program of instructions that, when executed by a computer, cause the computer to perform a method of generating training data for training a Deep Neural Network (DNN training data) usable for examination of a specimen, the method comprising:

• obtaining a first training image representative of at least a portion of the semiconductor specimen, and first labels respectively associated with a group of pixels selected in each of one or more segments identified by a user from the first training image;
• extracting a set of features characterizing the first training image, each feature having feature values corresponding to pixels in the first training image, the set of features including first features informative of contextual relations between the one or more segments in the first training image, and second features informative of pixel distribution in the first training image relative to a statistical measure of the group of pixels in each segment;
• training a machine learning (ML) model using the first labels, values of the group of pixels selected in each segment associated with the first labels, and the feature values of each feature of the set of features corresponding to the group of pixels in each segment, wherein the ML model is trained for image segmentation;
• processing the first training image using the trained ML model to obtain a first segmentation map informative of predicted labels associated with respective pixels in the first training image, each predicted label indicative of a segment to which a respective pixel belongs;
• and
• determining to include a first training sample comprising 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.