Method for controlling a manufacturing process and associated apparatuses

Patent metadata

Field	Value
Publication number	US-11947266-B2
Application number	US-201917297171-A
Country	US
Kind code	B2
Filing date	Nov 14, 2019
Priority date	Dec 19, 2018
Publication date	Apr 2, 2024
Grant date	Apr 2, 2024

Abstract

A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method including: obtaining a set of pre-process metrology data; processing the set of pre-process metrology data by decomposing the pre-process metrology data into one or more components which: a) correlate to the performance metric; or b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and applying a trained model to the processed set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

First claim

The invention claimed is:

1. A method for determining a correction relating to a performance metric of a semiconductor manufacturing process, the method comprising:
obtaining a first set of pre-process metrology data;
processing the first set of pre-process metrology data, the processing comprising decomposing the pre-process metrology data into one or more components which:
a) correlate to the performance metric; or
b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and

applying a trained model to the processed first set of pre-process metrology data to determine the correction for the semiconductor manufacturing process.

2. The method of claim 1 , wherein the semiconductor manufacturing process is a lithographic process and the pre-process metrology data is pre-exposure metrology data associated with a substrate subject to the lithographic process, wherein the lithographic process comprises an exposure process for exposing structures on to the substrate.

3. The method of claim 2 , wherein the one or more components are at least partially correctable by a control process which is part of the lithographic process and the processing further comprises removing the one or more components from the first set of pre-exposure metrology data.

4. The method as claimed in claim 2 , performed for each substrate individually which is subject to the lithographic process.

5. The method as claimed in claim 2 , wherein the first set of pre-exposure metrology data comprises data related to distortion of the substrate.

6. The method as claimed in claim 3 , wherein the first set of pre-exposure metrology data is of a similar type, but more densely measured, pre-exposure metrology data than that measured for the control process which is part of the lithographic process.

7. The method as claimed in claim 3 , wherein the first set of pre-exposure metrology data has been measured on a substrate in a process external to the exposure process.

8. The method as claimed in claim 7 , wherein the first set of pre-exposure metrology data comprises external alignment data as distinct from a second set of pre-exposure metrology data, the second set of pre-exposure metrology data comprising at least alignment data having been measured on the substrate by an exposure apparatus which performs the exposure process and control process.

9. The method as claimed in claim 7 , wherein the first set of pre-exposure metrology data comprises at least external leveling data as distinct from a second set of pre-exposure metrology data, the second set of pre-exposure metrology data comprising at least leveling data having been measured on the substrate by an exposure apparatus which performs the exposure process and control process.

10. The method as claimed in claim 3 , wherein the one or more components which are at least partially correctable by a control process which is part of the lithographic process comprise component data related to one or more models and/or one or more spatial frequencies used for alignment performance metric correction.

11. The method as claimed in claim 1 , wherein the applying a trained model comprises performing a model mapping based on first features extracted from the processed pre-exposure metrology data, the model mapping being operable to map the first features to corresponding second features previously observed in post processing metrology data relating to the performance metric, the post processing metrology data having been used to train the model.

12. The method as claimed in claim 1 , wherein the trained model comprises a trained neural network model.

13. The method as claimed in claim 12 , comprising:
obtaining training data comprising a training set of pre-exposure metrology data, equivalent to the first set of pre-exposure metrology data, and obtaining a corresponding training set of post processing metrology data relating to the performance metric, wherein the training set of pre-exposure metrology data is labeled by the corresponding training set of post processing metrology data;
processing the training data in a manner corresponding to the processing of the first set of pre-exposure metrology data, to obtain processed pre-exposure metrology data; and
training the model with the processed training data.

14. The method as claimed in claim 2 , comprising training the trained model, the training comprising:
obtaining training data comprising a first training set of pre-exposure metrology data, equivalent to the first set of pre-exposure metrology data, and obtaining a corresponding training set of post processing metrology data relating to the performance metric;
processing the training data in a manner corresponding to the processing of the first set of pre-exposure metrology data, to obtain processed pre-exposure metrology data; and
training the model with the processed training data by correlating the training set of pre-exposure metrology data with the corresponding training set of post processing metrology data.

15. The method as claimed in claim 1 , wherein the performance metric comprises an overlay metric or yield metric.

16. A method of obtaining at least one model trained for determining a correction relating to a performance metric of a lithographic process, the method comprising:
obtaining training data comprising a training set of pre-exposure metrology data, and obtaining a corresponding training set of post processing metrology data relating to the performance metric;
processing the training data to obtain processed pre-exposure metrology data comprising one or more components of the pre-exposure metrology data which correlate to the performance metric; and
training, by a hardware computer, the at least one model with the processed training data.

17. The method as claimed in claim 16 , wherein the model comprises a neural network model, and the training data comprises the training set of pre-exposure metrology data labeled by the corresponding training set of post processing metrology data.

18. A computer program product comprising a non-transitory computer-readable medium having instructions therein, the instructions, when executed by one or more computers, configured to cause the one or more computers to at least:
obtain a set of pre-process metrology data relating to a semiconductor manufacturing process;
decompose the pre-process metrology data into one or more components which:
a) correlate to a performance metric of the semiconductor manufacturing process; or
b) are at least partially correctable by a control process which is part of the semiconductor manufacturing process; and

apply a trained model to the processed set of pre-process metrology data to determine a correction for the semiconductor manufacturing process.

19. The computer program product of claim 18 , wherein the semiconductor manufacturing process is a lithographic process and the pre-process metrology data is pre-exposure metrology data associated with a substrate subject to the lithographic process, wherein the lithographic process comprises an exposure process for exposing structures on to the substrate.

20. A computer program product comprising a non-transitory computer-readable medium having instructions therein, the instructions, when executed by one or more computers, configured to cause the one or more computers to at least:
obtain training data comprising a training set of pre-exposure metrology data for a lithographic process, and obtaining a c

Assignees

• Asml Netherlands Bv

Inventors

• Brantjes Nicolaas Petrus Marcus
• Cox Matthijs
• Menchtchikov Boris
• Tabery Cyrus Emil
• Zhang Youping
• Zou Yi
• Lin Chenxi
• Cheng Yana
• Hastings Simon Philip Spencer
• Genin Maxim Philippe Frederic

Classifications

• G03F7/706839

  Modelling, e.g. modelling scattering or solving inverse problems · CPC title

• G03F7/706837

  Data analysis, e.g. filtering, weighting, flyer removal, fingerprints or root cause analysis · CPC title

• G03F7/70491 Primary

  Information management, e.g. software; Active and passive control, e.g. details of controlling exposure processes or exposure tool monitoring processes · CPC title

• G03F7/70633 Primary

  Overlay, i.e. relative alignment between patterns printed by separate exposures in different layers, or in the same layer in multiple exposures or stitching · CPC title

• G03F9/7046

  Strategy, e.g. mark, sensor or wavelength selection · CPC title