TECHNICAL FIELD

The presently disclosed subject matter relates, in general, to the field of examination of a semiconductor specimen, and more specifically, to training data generation usable for the examination of a specimen.

BACKGROUND

Current demands for high density and performance associated with ultra large-scale integration of fabricated devices require submicron features, increased transistor and circuit speeds, and improved reliability. As semiconductor processes progress, pattern dimensions such as line width, and other types of critical dimensions, are continuously shrunken. Such demands require formation of device features with high precision and uniformity, which, in turn, necessitates careful monitoring of the fabrication process, including automated examination of the devices while they are still in the form of semiconductor wafers.

By way of non-limiting example, run-time examination can employ a two-phase procedure, e.g. inspection of a specimen, followed by review of sampled locations of potential defects. Examination generally involves generating certain output (e.g., images, signals, etc.) for a specimen by directing light or electrons to the wafer and detecting the light or electrons from the wafer. During the first phase, the surface of a specimen is inspected at high-speed and relatively low-resolution. Defect detection is typically performed by applying a defect detection algorithm to the inspection output. A defect map is produced to show suspected locations on the specimen having high probability of a defect. Most often, the goal of inspection is to provide high sensitivity to detect defects of interest, while suppressing detection of nuisance and noise on the wafer. During the second phase, at least some of the suspected locations are more thoroughly analyzed with relatively high resolution. In some cases, both phases can be implemented by the same inspection tool, and, in some other cases, these two phases are implemented by different inspection tools.

Examination processes can include a plurality of examination steps. During the manufacturing process, the examination steps can be performed a multiplicity of times, for example after the manufacturing or processing of certain layers, or the like. Additionally or alternatively, each examination step can be repeated multiple times, for example for different wafer locations or for the same wafer locations with different examination settings.

Examination processes are used at various steps during semiconductor fabrication to detect and classify defects on specimens, as well as perform metrology related operations. Effectiveness of examination can be increased by automatization of process(es) as, for example, defect detection, Automatic Defect Classification (ADC), Automatic Defect Review (ADR), image segmentation, automated metrology-related operations, etc.

SUMMARY

In accordance with certain aspects of the presently disclosed subject matter, there is provided a computerized system of generating training data for training a Deep Neural Network (DNN training data) usable for examination of a semiconductor specimen, the system comprising a processor and memory circuitry (PMC) configured to: obtain 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; extract 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; train 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; process 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 that a respective pixel belongs to; and determine 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.

In addition to the above features, the system according to this aspect of the presently disclosed subject matter can comprise one or more of features (i) to (xi) listed below, in any desired combination or permutation which is technically possible:(i). The training data can be usable for training a Deep Neural Network (DNN) for at least one examination process selected from a group comprising: automated segmentation, automated metrology, automated defect detection, automated defect review, and automated defect classification based on runtime images.(ii). The criterion is based on a user feedback on the first segmentation map, and the PMC is configured to, upon receiving a negative user feedback on the first segmentation map, obtain additional first labels associated with an additional group of pixels in at least one of the segments, the first labels and the additional first labels constituting aggregated label data, and repeat the extracting of the second features, the training and the processing based on the aggregated label data until receiving a positive user feedback.(iii). The PMC is configured to, upon receiving a positive user feedback on the first segmentation map, include the first training sample into the training data.(iv). The PMC is further configured to obtain a second training image and second labels respectively associated with a group of pixels selected in each of one or more segments identified by a user from the second training image, the second labels being added to the aggregated label data, extract a set of features characterizing the second training image and including the first features and the second features, train the ML model using the aggregated label data, values of pixels associated with the aggregated label data, and the feature values of each feature of the set of features corresponding to the pixels associated with the aggregated label data, and performing the processing and determining based on the second training image.(v). At least one of the one or more segments correspond to one or more structural elements presented in the first training image.(vi). The first features include texture features extracted by: processing the first training image with a set of filters, giving rise to a set of feature responses each comprising feature response values corresponding to respective pixels of the first training image, wherein each pixel corresponds to a feature vector including a set of feature response values thereof; clustering the feature vectors which correspond to the pixels in the first training image into a plurality of clusters; assigning, for each pixel, a feature value according to the cluster that the feature vector thereof belongs to, giving rise to a feature map comprising feature values corresponding to pixels of the first training image, the feature map separable into a plurality of channels corresponding to respective feature values; and sampling each channel of the feature map using a sampling filter, giving rise to a feature map with a plurality of sampled channels.(vii). The first features further include intensity features extracted by: clustering pixel values of the first training image into a plurality of clusters, assigning, for each pixel, a feature value according to the cluster that the pixel value belongs to, giving rise to a feature map separable into a plurality of channels corresponding to respective feature values, and sampling each channel of the feature map using a sampling filter, giving rise to a feature map with a plurality of sampled channels.(viii). The set of filters comprise one or more of the following: derivative filters, Gabor filters and Laplacian filters.(ix). The second features are extracted by calculating a statistical measure for the group of pixels in each segment, and for each pixel in the first training image, calculating a corresponding feature value as a distance between a value of the pixel and the statistical measure of each segment, giving rise to one or more feature maps, each comprising feature values corresponding to distances between pixel values and the statistical measure of a respective segment.(x). The training image comprises multiple channels captured from different perspectives, and the set of features is extracted from the multiple channels.(xi). The PMC is further configured to use the DNN training data to train the DNN, and validate the trained DNN using a validation set of images.

In accordance with other aspects of the presently disclosed subject matter, there is provided a 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 that a respective pixel belongs to; 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.

This aspect of the disclosed subject matter can comprise one or more of features (i) to (xi) listed above with respect to the system, mutatis mutandis, in any desired combination or permutation which is technically possible.

In accordance with other aspects of the presently disclosed subject matter, there is provided a non-transitory computer readable medium comprising instructions that, when executed by a computer, cause the computer to perform a method for 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 that a respective pixel belongs to; 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.

This aspect of the disclosed subject matter can comprise one or more of features (i) to (xi) listed above with respect to the method, mutatis mutandis, in any desired combination or permutation which is technically possible.

