Semiconductor fabrication using machine learning approach to generating process control parameters

A computer program product for controlling processing of a substrate, the computer program product tangibly embodied in a non-transitory computer readable medium and comprising instructions for causing a processor to:

• train an artificial neural network by backpropagation using a plurality of groups of process parameter and a plurality of removal profiles as training data, the artificial neural network having a plurality of input nodes to receive respective removal values from a removal profile, a plurality of output nodes to output control parameter values, and a plurality of hidden nodes connecting the input nodes to the output nodes;
• obtain a target removal profile for each respective substrate of a plurality of substrates;
• for each respective substrate, determine respective control parameter values to apply to the respective substrate from the output nodes of the artificial neural network by applying the target removal profile to the input nodes of the artificial neural network;
• and
• cause a semiconductor processing system to subject each respective substrate to the process using the respective control parameter values.

A polishing system, comprising:

• a support to hold a polishing pad;
• a carrier head to hold a substrate in contact with the polishing pad, the carrier head having a plurality of chambers;
• a motor to generate relative motion between the support and the carrier head;
• and
• a controller configured to
• train an artificial neural network by backpropagation using a plurality of groups of process parameter and a plurality of removal profiles as training data, the artificial neural network having a plurality of input nodes to receive respective removal values from a removal profile, a plurality of output nodes to output control parameter values, and a plurality of hidden nodes connecting the input nodes to the output nodes, wherein the control parameter values comprise pressures for the chambers in the carrier head;
• obtain a target removal profile for each respective substrate of a plurality of substrates;
• for each respective substrate, determine respective control parameter values to apply to the respective substrate from the output nodes of the artificial neural network by applying the target removal profile to the input nodes of the artificial neural network, wherein the respective control parameter values comprise respective pressures for the chambers in the carrier head;
• and
• for each respective substrate, cause the carrier head to apply the respective pressures to the chambers in the carrier head during polishing.