Methods and systems to detect sub-surface defects in electronics modules using shear force microscopy

A method comprising:

• positioning a probe adjacent to a plurality of points of a sample at a first distance from the sample;
• emitting a first plurality of ultrasonic waves at a plurality of frequencies from the probe towards the sample at each of the plurality of points adjacent to the sample at the first distance;
• measuring bulk shear force amplitudes of reflection of each of the first plurality of ultrasonic waves at the probe for each of the plurality of points adjacent to the sample at the first distance;
• repositioning the probe adjacent to the plurality of points of the sample at a second distance from the sample, wherein the second distance is less than the first distance;
• emitting a second plurality of ultrasonic waves at the plurality of frequencies from the probe towards the sample at each of the plurality of points adjacent to the sample at the second distance;
• measuring surface shear force amplitudes of reflection of each of the second plurality of ultrasonic waves at the probe for each of the plurality of points adjacent to the sample at the second distance;
• computing an error value for each of the plurality of frequencies at each of the plurality of points by subtracting the surface shear force amplitude from the bulk shear force amplitude measured for each of the plurality of frequencies at each of the plurality of points;
• determining a set of frequencies from the plurality of frequencies comprising a frequency that has a maximum error value for each of the plurality of points;
• selecting a predetermined number of points from among the plurality of points to determine a set of selected points;
• for each point among the set of selected points, creating an approach curve at that point using an associated frequency from the set of frequencies that has the maximum error value for that point;
• for each point among the set of selected points, determining a slope of the approach curve at that point;
• and
• determining, using a machine learning algorithm, an optimum frequency based on the slope of the approach curve at each point of the set of selected points.