Method and apparatus for predicting a process metric associated with a process
A method including: obtaining one or more models configured for predicting a process metric of a manufacturing process based on inputting process data; and using a reinforcement learning framework to evaluate the one or more models and/or model configurations of the one more models based on inputting new process data to the one or more models and determining a performance indication of the one or more models and/or model configurations in predicting the process metric based on inputting the new process data.
1 . A method comprising:
obtaining one or more models configured for predicting a process metric of a manufacturing process based on inputting process data, the process data comprising one or more Key Performance Indicators (KPIs) associated with a plurality of functional modules involved in the manufacturing process; and
using, by a hardware computer system, a reinforcement learning framework to evaluate the one or more models and/or model configurations of the one more models based on inputting new process data to the one or more models and determining an indication of a performance of the one or more models and/or model configurations in predicting the process metric based on inputting the new process data.
2 . The method of claim 1 , wherein the process metric is associated with a yield or quality parameter of the manufacturing process.
3 . The method of claim 1 , wherein the configuration comprises one or more selected from: a model type, model parameters and/or model hyperparameters.
4 . The method of claim 1 , wherein the process data and new process data comprise features of the manufacturing process.
5 . The method of claim 1 , wherein the manufacturing process is a process of patterning semiconductor wafers.
6 . The method of claim 1 , wherein the performance in predicting the process metric is based on comparing the predicted process metric and an otherwise obtained value of the process metric.
7 . The method of claim 1 , further comprising selecting a model and/or model configuration based on the evaluation.
8 . The method of claim 7 , wherein the process metric is indicative of the quality of a substrate being subject to a patterning process.
9 . The method of claim 1 , wherein the one or more models comprise at least two machine learning based models, each model being of a different architecture.
10 . The method of claim 9 , wherein the one or more models comprise at least a first model based on supervised learning and a second model based on unsupervised learning.
11 . A semiconductor manufacturing apparatus comprising a computer processor and a storage medium comprising instructions operable to execute the method of claim 1 .
12 . A computer program product comprising a non-transitory computer-readable storage medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:
obtain one or more models configured for predicting a process metric of a manufacturing process based on inputting process data, the process data comprising one or more Key Performance Indicators (KPIs) associated with a plurality of functional modules involved in the manufacturing process; and
use a reinforcement learning framework to evaluate the one or more models and/or model configurations of the one more models based on inputting new process data to the one or more models and determine an indication of a performance of the one or more models and/or model configurations in predicting the process metric based on inputting the new process data.
13 . The computer program product of claim 12 , wherein the process metric is associated with a yield or quality parameter of the manufacturing process.
14 . The computer program product of claim 12 , wherein the configuration comprises one or more selected from: a model type, model parameters and/or model hyperparameters.
15 . The computer program product of claim 12 , wherein the process data and new process data comprise features of the manufacturing process.
16 . The computer program product of claim 12 , wherein the performance in predicting the process metric is based on comparison of the predicted process metric and an otherwise obtained value of the process metric.
17 . The computer program product of claim 12 , wherein the one or more models comprise at least two machine learning based models, each model being of a different architecture.
18 . A computer program product comprising a non-transitory computer-readable storage medium having instructions therein, the instructions, when executed by a computer system, configured to cause the computer system to at least:
obtain one or more models configured for predicting a process metric of a manufacturing process based on inputting process data, wherein the process metric is indicative of the quality of a substrate being subject to a patterning process;
use a reinforcement learning framework to evaluate the one or more models and/or model configurations of the one more models based on inputting new process data to the one or more models and determine an indication of a performance of the one or more models and/or model configurations in predicting the process metric based on inputting the new process data;
select a model and/or model configuration based on the evaluation.