Chamber matching by equipment constant updates
A method includes receiving, by a processing device, data indicative of performance of a plurality of process chambers. The method further includes providing the data indicative of performance of the plurality of process chambers to a model. The method further includes receiving as output from the model a first recommended equipment constant update associated with a first process chamber of the plurality of process chambers and a second recommended equipment constant update associated with a second process chamber of the plurality of process chambers. The method further includes updating a first equipment constant of the first process chamber and a second equipment constant of the second process chamber in view of the first recommended equipment constant update and the second recommended equipment constant update.
1 . A method, comprising:
receiving, by a processing device, data indicative of performance of a plurality of process chambers;
providing the data indicative of performance of the plurality of process chambers to a model;
processing the data indicative of performance of the plurality of process chambers using the model by determining that one or more process chambers of the plurality of process chambers satisfies a standard of performance, and generating a recommendation that one or more updates to first and second process chambers of the plurality of process chambers that do not satisfy the standard of performance be implemented;
receiving as output from the model a first recommended equipment constant update associated with the first process chamber of the plurality of process chambers and a second recommended equipment constant update associated with the second process chamber of the plurality of process chambers based on the standard of performance;
updating a first equipment constant of the first process chamber to generate a first updated equipment constant and a second equipment constant of the second process chamber to generate a second updated equipment constant in view of the first recommended equipment constant update and the second recommended equipment constant update; and
causing a substrate to be processed by the first process chamber using the first updated equipment constant.
2 . The method of claim 1 , further comprising:
determining, based on the data indicative of performance of the plurality of process chambers, that a third process chamber satisfies one or more performance metric thresholds; and
generating, based on data indicative of performance of the third process chamber, a standard of performance, wherein the first recommended equipment constant update is based on one or more differences between data indicative of performance of the first process chamber and data indicative of performance of the third process chamber.
3 . The method of claim 1 , wherein the data indicative of performance of the plurality of process chambers comprises trace data of the plurality of process chambers and metrology data of substrates associated with the plurality of process chambers.
4 . The method of claim 1 , wherein the first recommended equipment constant update adjusts performance of the first process chamber, wherein adjusting performance of the first process chamber reduces one or more differences between performance of the first process chamber and a third process chamber of the plurality of process chambers.
5 . The method of claim 4 , wherein the one of more differences between performance of the first process chamber and the third process chamber comprise one or more of differences between trace data associated with the first process chamber and the third process chamber or differences between metrology data of substrates associated with the first process chamber and metrology data of substrates associated with the third process chamber.
6 . The method of claim 1 , wherein the first equipment constant is associated with a first type of chamber component, and wherein the second equipment constant is associated with a second type of chamber component, different than the first type of chamber component, and wherein each of the plurality of process chambers comprises the first type of chamber component and the second type of chamber component.
7 . The method of claim 1 , wherein the model comprises a trained machine learning model, and wherein the trained machine learning model is to recommend a schedule for performing the first recommended equipment constant update and the second recommended equipment constant update.
8 . A system, comprising memory and a processing device coupled to the memory, wherein the processing device is to:
receive data indicative of performance of a plurality of process chambers;
provide the data indicative of performance of a plurality of process chambers to a model;
process the data indicative of performance of the plurality of process chambers using the model by determining that one or more process chambers of the plurality of process chambers satisfies a standard of performance, and generating a recommendation that one or more updates to first and second process chambers of the plurality of process chambers that do not satisfy the standard of performance be implemented;
receive as output from the model a first recommended equipment constant update associated with the first process chamber of the plurality of process chambers and a second recommended equipment constant update associated with the second process chamber of the plurality of process chambers based on the standard of performance;
update a first equipment constant of the first process chamber to generate a first updated equipment constant and a second equipment constant of the second process chamber to generate a second updated equipment constant in view of the first recommended equipment constant update and the second recommended equipment constant update; and
cause a substrate to be processed by the first process chamber using the first updated equipment constant.
9 . The system of claim 8 , wherein the processing device is further to:
determine, based on the data indicative of performance of the plurality of process chambers, that a third process chamber satisfies one or more performance metric thresholds; and
generate, based on data indicative of performance of the third process chamber, a standard of performance, wherein the first recommended equipment constant update is based on one or more differences between data indicative of performance of the first process chamber and data indicative of performance of the third process chamber.
10 . The system of claim 8 , wherein the data indicative of performance of the plurality of process chambers comprises trace data of the plurality of process chambers and metrology data of substrates associated with the plurality of process chambers.
11 . The system of claim 8 , wherein the first recommended equipment constant update adjusts performance of the first process chamber, wherein adjusting the performance of the first process chamber reduces one or more differences between performance of the first process chamber and a third process chamber of the plurality of process chambers.
12 . The system of claim 11 , wherein the one of more differences between performance of the first process chamber and the third process chamber comprise one or more of differences between trace data associated with the first process chamber and the third process chamber or differences between metrology data of substrates associated with the first process chamber and metrology data of substrates associated with the third process chamber.
13 . The system of claim 8 , wherein the first equipment constant is associated with a first type of chamber component, and wherein the second equipment constant is associated with a second type of chamber component, different than the first type of chamber component, and wherein each of the plurality of process chambers comprises the first type of chamber component and the second type of chamber component.
14 . The system of claim 8 , wherein the model comprises a trained machine learning model, and wherein the trained machine learning model is to recommend a schedule for performing the first recommended equipment constant update and the second recommended equipment constant update.
15 . A non-transitory machine-readable storage medium storing instructions which, when executed, cause a processing device to perform operations comprising:
receiving, by a processing device, data indicative of performance of a plurality of process chambers;
providing the data indicative of performance of the plurality of process chambers to a model;
processing the data indicative of performance of the plurality of process chambers using the model by determining that one or more process chambers of the plurality of process chambers satisfies a standard of performance, and generating a recommendation that one or more updates to first and second process chambers of the plurality of process chambers that do not satisfy the standard of performance be implemented;
receiving as output from the model a first recommended equipment constant update associated with the first process chamber of the plurality of process chambers and a second recommended equipment constant update associated with the second process chamber of the plurality of process chambers based on the standard of performance;
updating a first equipment constant of the first process chamber to generate a first updated equipment constant and a second equipment constant of the second process chamber to generate a second updated equipment constant in view of the first recommended equipment constant update and the second recommended equipment constant update; and
causing a substrate to be processed by the first process chamber using the first updated equipment constant.
16 . The non-transitory machine-readable storage medium of claim 15 , wherein the operations further comprise:
determining, based on the data indicative of performance of the plurality of process chambers, that a third process chamber satisfies one or more performance metric thresholds; and
generating, based on data indicative of performance of the third process chamber, a standard of performance, wherein the first recommended equipment constant update is based on one or more differences between data indicative of performance of the first process chamber and data indicative of performance of the third process chamber.
17 . The non-transitory machine-readable storage medium of claim 15 , wherein the data indicative of performance of the plurality of process chambers comprises trace data of the plurality of process chambers and metrology data of substrates associated with the plurality of process chambers.
18 . The non-transitory machine-readable storage medium of claim 15 , wherein the first recommended equipment constant update adjusts performance of the first process chamber, wherein adjusting performance of the first process chamber reduces one or more differences between performance of the first process chamber and a third process chamber of the plurality of process chambers.
19 . The non-transitory machine-readable storage medium of claim 18 , wherein the one of more differences between performance of the first process chamber and the third process chamber comprise one or more of differences between trace data associated with the first process chamber and the third process chamber or differences between metrology data of substrates associated with the first process chamber and metrology data of substrates associated with the third process chamber.
20 . The non-transitory machine-readable storage medium of claim 15 , wherein the first equipment constant is associated with a first type of chamber component, and wherein the second equipment constant is associated with a second type of chamber component, different than the first type of chamber component, and wherein each of the plurality of process chambers comprises the first type of chamber component and the second type of chamber component.