Systems and methods for semiconductor chip surface topography metrology

A system, comprising:

• a light source configured to provide low coherent light to a first beam splitter;
• a scanner configured to use the low coherent light reflected from the first beam splitter to scan a plurality of positions on a surface of a semiconductor chip;
• a second beam splitter configured to receive a plurality of reflected signals from the plurality of positions on the surface of the semiconductor chip;
• a detector configured to detect a plurality of interference signals from a first output of the second beam splitter, wherein each of the plurality of interference signals corresponds to a respective one of the plurality of positions;
• a spectrometer configured to detect a plurality of spectrum signals from a second output of the second beam splitter, wherein each of the plurality of spectrum signals corresponds to the respective one of the plurality of positions;
• and
• a processor configured to determine, using machine learning, surface topography of the semiconductor chip based at least on the plurality of interference signals and the plurality of spectrum signals,
• wherein the processor is further configured to:
• classify the plurality of spectrum signals into a plurality of categories using a machine learning model, wherein each of the plurality of categories corresponds to a region having a same material on the surface of the semiconductor chip,
• determine a surface height offset between a surface baseline and at least one of the categories based at least on a calibration signal associated with the region corresponding to the at least one of the categories, and
• characterize the surface topography of the semiconductor chip based at least on the surface height offset and the interference signals.