CPC H01L 22/14 (2013.01) [B24B 37/013 (2013.01); B24B 49/105 (2013.01); G06F 17/15 (2013.01); G06N 3/08 (2013.01); H01L 21/304 (2013.01); H01L 21/3212 (2013.01); H01L 22/12 (2013.01); H01L 22/26 (2013.01)] | 17 Claims |
1. A computer program product, tangibly embodied in a non-transitory computer readable medium, comprising instructions to cause one or more computers to:
receive from a monitoring system a first set of sensor measurements generated by scanning a sensor of an in-situ monitoring system across a calibration substrate having a conductive layer formed of a first material having a first conductivity;
receive ground truth measurements of a thickness of the conductive layer of the calibrations substrate to provide a first thickness profile;
scale the first thickness profile based the first conductivity and a target second conductivity that is greater than the first conductivity to provide a modified second thickness profile equivalent to a thickness profile that would be generated if the conductive layer were formed of a second material of the second conductivity;
train a neutral network to convert sensor measurements from the in-situ monitoring system to thickness measurements for a layer formed of the second material to generate a trained neural network, the training performed using training data including the modified second thickness profile and calibration thickness values based on the first set of sensor measurements from the conductive layer formed of the first material;
during polishing of a conductive layer formed of a first material having a first conductivity on a substrate at a polishing station, receive from an in-situ eddy current monitoring system a plurality of measured signals values for a plurality of different locations on the layer;
generate thickness measurements for the locations, the instructions to generate thickness measurements including instructions to calculate initial thickness values based on the plurality of measured signals values and process the initial thickness values through the neural network; and
at least one of detect a polishing endpoint or modify a polishing parameter based on the thickness measurements.
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