Determining a correction to a process
AssigneeASML Netherlands B.V.
InventorsSarathi Roy, Edo Maria Hulsebos, Roy Werkman, Junru Ruan
Life of this patent
Estimating the term…
Abstract
A method for configuring a semiconductor manufacturing process, the method including: obtaining a first value of a first parameter based on measurements associated with a first operation of a process step in the semiconductor manufacturing process and a first sampling scheme; using a recurrent neural network to determine a predicted value of the first parameter based on the first value; and using the predicted value of the first parameter in configuring a subsequent operation of the process step in the semiconductor manufacturing process.
Claims20
A method comprising:
- obtaining first sparse data representing measured values of a parameter across one or more substrates subject to a process and measured using a sparse sampling layout;
- obtaining dense data representing measured values of the parameter across one or more substrates subject to the process and measured using a dense sampling layout that is more spatially dense than the sparse sampling layout;
- applying a model to the sparse data and dense data to determine a sparse-to-dense mismatch;
- adapting the model based on the sparse-to-dense mismatch;
- obtaining second sparse data representing measured values of the parameter across one or more substrates subject to the process and measured using a sparse sampling layout;
- applying the adapted model to the second sparse data to determine a sparse model result;
- and
- determining a correction to the process based on the sparse model result.
A computer program product comprising a non-transitory computer-readable medium comprising instructions therein, the instructions, upon execution by a computer system, configured to cause the computer system to at least:
- obtain first sparse data representing measured values of a parameter across one or more substrates subject to a process and measured using a sparse sampling layout;
- obtain dense data representing measured values of the parameter across one or more substrates subject to the process and measured using a dense sampling layout that is more spatially dense than the sparse sampling layout;
- apply a model to the sparse data and dense data to determine a sparse-to-dense mismatch;
- adapt the model based on the sparse-to-dense mismatch;
- obtain second sparse data representing measured values of the parameter across one or more substrates subject to the process and measured using a sparse sampling layout;
- apply the adapted model to the second sparse data to determine a sparse model result;
- and
- determine a correction to the process based on the sparse model result.
Dependencies are inferred from the claim text.