Machine learning based metrology for semiconductor specimens

A computerized method of training a machine learning model usable for examining a semiconductor specimen, the method comprising:

• obtaining a plurality of training images collected from at least one metrology tool;
• for each training image, processing the training image to create one or more training image strips each containing an edge, and for each training image strip, extracting a sequence of topo points representative of a contour of the edge;
• providing the sequence of topo points to the ML model to process and obtaining a sequence of predicted topo points;
• obtaining predicted measurement data using the sequence of predicted topo points;
• and
• evaluating the predicted measurement data using a loss function representative of at least one metrology metric, and optimizing the ML model until the loss function meets a predefined criterion.

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 examining a semiconductor specimen, the method comprising:

• obtaining a runtime image of a semiconductor specimen acquired by an examination tool;
• processing the runtime image to create one or more image strips each containing an edge, and for each image strip, extracting a sequence of topo points representative of a contour of the edge therein;
• providing the sequence of topo points for each image strip to a trained machine learning (ML) model to be processed, and obtaining, as an output of the ML model, a sequence of updated topo points;
• and
• obtaining measurement data on the runtime image using the sequence of updated topo points, wherein the measurement data has improved performance with respect to at least one metrology metric.