Determination of a simulated image of a specimen

A method of examining a semiconductor specimen, the method comprising, by a processor and memory circuitry (PMC):

• obtaining a plurality of training samples, each training sample comprising:
• an image of a semiconductor specimen, and
• a design image based on design data,
• training a machine learning module based on a given training sample comprising a given image and a given design image, wherein a given displacement matrix is initialized for the given image,
• wherein the training includes minimizing a function representative of a difference between:
• a simulated image generated by the machine learning module based on the given design image, and
• a corrected image corresponding to the given image after correction of pixel position of the given image in accordance with the given displacement matrix,
• wherein the minimizing includes optimizing parameters of the machine learning module and of the given displacement matrix,
• wherein the machine learning module is usable, after the training, to generate a simulated image of a specimen based on a design image of the specimen.

A non-transitory computer readable medium comprising instructions that, when executed by a PMC, cause the PMC to perform operations comprising:

• obtaining a plurality of training samples, each training sample comprising:
• an image of a semiconductor specimen, and
• a design image based on design data,
• training a machine learning module based on a given training sample comprising a given image and a given design image, wherein a given displacement matrix is initialized for the given image, p1 wherein the training includes minimizing a function representative of a difference between:
• a simulated image generated by the machine learning module based on the given design image, and
• a corrected image corresponding to the given image after correction of pixel position of the given image in accordance with the given displacement matrix,
• wherein the minimizing includes optimizing parameters of the machine learning module and of the given displacement matrix,
• wherein the machine learning module is usable, after the training, to generate a simulated image of a specimen based on a design image of the specimen.