Deposition thickness drift compensation in substrate processing systems
A method includes identifying a material associated with a substrate processing operation of a recipe. The method further includes determining an expected total residual thickness value subsequent to the substrate processing operation. The method further includes determining, based on the expected total residual thickness value, an expected material thickness value of the material associated with the substrate processing operation. The method further includes updating the recipe based on the material and the expected material thickness value of the material to generate an updated recipe. The method further includes causing a substrate to be processed based on the updated recipe.
1 . A method comprising:
identifying a material associated with a substrate processing operation of a recipe;
determining an expected total residual thickness value subsequent to the substrate processing operation;
determining, based on the expected total residual thickness value, an expected material thickness value of the material associated with the substrate processing operation;
updating, based on the material and the expected material thickness value of the material, deposition operation parameters to generate an updated recipe to compensate for deposition drift; and
causing a substrate to be processed based on the updated recipe.
2 . The method of claim 1 , wherein the expected material thickness value is a difference between a desired material thickness value and an actual material thickness value.
3 . The method of claim 1 , wherein the updating of the recipe comprises determining an updated time value associated with the substrate processing operation.
4 . The method of claim 3 , wherein the determining of the updated time value associated with the substrate processing operation is based on a time value associated with the substrate processing operation, the expected total residual thickness value, and the expected material thickness value.
5 . The method of claim 1 , wherein the updating of the recipe comprises determining at least one of an updated radio frequency (RF) power of the substrate processing operation, an updated spacing value of the substrate processing operation, an updated gas flow value of the substrate processing operation, or an updated chamber pressure value of the substrate processing operation.
6 . The method of claim 1 , wherein the determining of the expected material thickness value comprises:
providing the expected total residual thickness value as input to a trained machine learning model; and
receiving, from the trained machine learning model, output associated with predictive data, wherein the expected material thickness value is associated with the predicted data.
7 . The method of claim 6 , wherein the trained machine learning model is trained with a data input comprising historical total residual thickness values and target output of historical material thickness values.
8 . A non-transitory computer-readable storage medium storing instructions which, when executed, cause a processing device to perform operations comprising:
identifying a material associated with a substrate processing operation of a recipe;
determining an expected total residual thickness value subsequent to the substrate processing operation;
determining, based on the expected total residual thickness value, an expected material thickness value of the material associated with the substrate processing operation;
updating, based on the material and the expected material thickness value of the material, deposition operation parameters to generate an updated recipe to compensate for deposition drift; and
causing a substrate to be processed based on the updated recipe.
9 . The non-transitory computer-readable storage medium of claim 8 , wherein the expected material thickness value is a difference between a desired material thickness value and an actual material thickness value.
10 . The non-transitory computer-readable storage medium of claim 8 , wherein the updating of the recipe comprises determining an updated time value associated with the substrate processing operation.
11 . The non-transitory computer-readable storage medium of claim 10 , wherein the determining the updated time value associated with the substrate processing operation is based on a time value associated with the substrate processing operation, the expected total residual thickness value, and the expected material thickness value.
12 . The non-transitory computer-readable storage medium of claim 8 , wherein the updating of the recipe comprises determining at least one of an updated radio frequency (RF) power of the substrate processing operation, an updated spacing value of the substrate processing operation, an updated gas flow value of the substrate processing operation, or an updated chamber pressure value of the substrate processing operation.
13 . The non-transitory computer-readable storage medium of claim 8 , wherein the determining of the expected material thickness value comprises:
providing the expected total residual thickness value as input to a trained machine learning model; and
receiving, from the trained machine learning model, output associated with predictive data, wherein the expected material thickness value is associated with the predicted data.
14 . The non-transitory computer-readable storage medium of claim 13 , wherein the trained machine learning model is trained with a data input comprising historical total residual thickness values and target output of historical material thickness values.
15 . A system comprising:
a memory; and
a processing device coupled to the memory, the processing device to:
identify a material associated with a substrate processing operation of a recipe;
determine an expected total residual thickness value subsequent to the substrate processing operation;
determine, based on the expected total residual thickness value, an expected material thickness value of the material associated with the substrate processing operation;
update, based on the material and the expected material thickness value of the material, deposition operation parameters to generate an updated recipe to compensate for deposition drift; and
cause a substrate to be processed based on the updated recipe.
16 . The system of claim 15 , wherein the expected material thickness value is a difference between a desired material thickness value and an actual material thickness value.
17 . The system of claim 15 , wherein to update the recipe, the processing device is to determine an updated time value associated with the substrate processing operation.
18 . The system of claim 17 , wherein the processing device is to determine the updated time value associated with the substrate processing operation based on a time value associated with the substrate processing operation, the expected total residual thickness value, and the expected material thickness value.
19 . The system of claim 15 , wherein to determine the expected material thickness value, the processing device is to:
provide the expected total residual thickness value as input to a trained machine learning model; and
receive, from the trained machine learning model, output associated with predictive data, wherein the expected material thickness value is associated with the predicted data.
20 . The system of claim 19 , wherein the trained machine learning model is trained with a data input comprising historical total residual thickness values and target output of historical material thickness values.