Applied Materials Israel Limited

Applied Materials Isreal Ltd

Irad Peleg

Boaz Cohen

Ran Schleyen

Ore Shtalrid

Ran Badanes

Denis Suhanov

There is provided a system to examine a semiconductor specimen, the system comprising a processor and memory circuitry configured to obtain a training sample comprising an image of a semiconductor specimen and a design image based on design data, train a machine learning module, wherein the training includes minimizing a function representative of a difference between a simulated image generated by the machine learning module based on a given design image, and a corrected image corresponding to a given image after correction of pixel position of the given image in accordance with a given displacement matrix, wherein the minimizing includes optimizing parameters of the machine learning module and of the given displacement matrix, wherein the trained machine learning module is usable to generate a simulated image of a specimen based on a design image of the specimen.

Determination of a simulated image of a specimen

A system for classifying a pattern of interest (POI) on a semiconductor specimen is disclosed. The system comprises a processor and memory circuitry. The memory circuitry is configured to obtain a high-resolution image of the POI, and to generate data usable for classifying the POI in accordance with a defectiveness-related classification. To generate the data, a machine learning model is utilized that has been trained in accordance with training samples. The training samples include a high-resolution training image captured by scanning a respective training pattern on a specimen, the respective training pattern being similar to the POI. The training samples also include a label associated with the image, the label being derivative of low-resolution inspection of the respective training pattern.

Detecting defects in semiconductor specimens using weak labeling

There is provided a method of defect detection on a specimen and a system thereof. The method includes: obtaining a runtime image representative of at least a portion of the specimen; processing the runtime image using a supervised model to obtain a first output indicative of the estimated presence of first defects on the runtime image; processing the runtime image using an unsupervised model component to obtain a second output indicative of the estimated presence of second defects on the runtime image; and combining the first output and the second output using one or more optimized parameters to obtain a defect detection result of the specimen.

Machine learning-based defect detection of a specimen

A system of classifying a pattern of interest (POI) on a semiconductor specimen, where the system includes a processor and memory circuitry configured to obtain a high-resolution image of the POI, and generate data usable for classifying the POI in accordance with a defectiveness-related classification. Generating the data utilizes a machine learning model that has been trained in accordance with training samples. The training samples include a high-resolution training image captured by scanning a respective training pattern on a specimen, the respective training pattern being similar to the POI, and a label associated with the image. The label is derivative of low-resolution inspection of the respective training pattern.

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Tel Aviv, Israel

Irad Peleg