Patent ID: 11915406
Assignee: APPLIED MATERIALS ISRAEL LTD.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 19:
20. 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 semiconductor specimen, the method comprising:
obtaining a first training image 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, the first labels provided as partial label data of 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 indicative of contextual information 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 for image segmentation using the first labels, values of the group of pixels selected in each segment, and the feature values of each feature of the set of features corresponding to the group of pixels in each segment;
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, wherein the first segmentation map is used as full label data of the first training image.