Feature Prediction Using Nondestructive Workpiece Imaging

A method for training a machine-learned inspection model, the method comprising:

• obtaining a first type of nondestructive data indicative of at least a portion of a semiconductor workpiece;
• obtaining a second type of nondestructive data indicative of at least the portion of the semiconductor workpiece;
• generating a segmented map based on the second type of nondestructive data, wherein the segmented map is indicative of one or more features of at least the portion of the semiconductor workpiece;
• aligning the segmented map with the second type of nondestructive data and the first type of nondestructive data;
• and
• training a machine-learned inspection model to detect the one or more features of the portion of the semiconductor workpiece within the first type of nondestructive data using the segmented map as ground truth data.

A system for inspection of a semiconductor workpiece, the system comprising:

• one or more imaging devices configured to capture a first type of non-destructive data of at least a portion of the semiconductor workpiece;
• processing circuitry configured to perform operations, the operations comprising:
• obtaining the first type of nondestructive data of at least a portion of a semiconductor workpiece;
• providing the first type of nondestructive data to an inspection model, wherein the inspection model is trained using a second type of nondestructive data;
• and
• obtaining an output from the inspection model, wherein the output is indicative of one or more features of the semiconductor workpiece.