Predictive process control for a manufacturing process
AssigneeNanotronics Imaging, Inc.
InventorsMatthew C. Putman, John B. Putman, Vadim Pinskiy, Damas Limoge
Life of this patent
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Abstract
Aspects of the disclosed technology encompass the use of a deep-learning controller for monitoring and improving a manufacturing process. In some aspects, a method of the disclosed technology includes steps for: receiving control values associated with a process station in a manufacturing process, predicting an expected value for an article of manufacture output from the process station, and determining if the deep-learning controller can control the manufacturing process based on the expected value. Systems and computer-readable media are also provided.
Claims13
A computer-implemented method for training a deep-learning controller to control a manufacturing process executed in a manufacturing system comprising a plurality of process stations, comprising:
- receiving, by the deep-learning controller, a first set of control values associated with a first process station of the plurality of process stations, wherein the first set of control values are attributes of the first process station;
- predicting, by the deep-learning controller, an expected final output for an article of manufacture, based on the first set of control values;
- receiving, by the deep-learning controller, a second set of control values associated with a second process station of the plurality of process stations, wherein the second set of control values are attributes of the second process station;
- predicting, by the deep-learning controller, an updated expected final output for the article of manufacture, based on the second set of control values;
- determining if the deep-learning controller can control the manufacturing process based on the expected final output and the updated expected final output by:
- receiving, by the deep-learning controller, an actual value associated with the article of manufacture following processing in the manufacturing system,
- comparing the expected final output with the actual value, and
- determining that the expected final output is within a threshold confidence level of the actual value,
- comparing the updated expected final output with the actual value, and
- determining that the updated expected final output is within the threshold confidence level of the actual value;
- and
- based on the determining, assuming control of the manufacturing process by the deep-learning controller.
A system for training a deep-learning controller to control a manufacturing process executed in a manufacturing system comprising a plurality of process stations comprising:
- one or more processors;
- and
- a non-transitory memory storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
- receiving, by the deep-learning controller, a first set of control values associated with a first process station of the plurality of process stations, wherein the first set of control values are attributes of the first process station;
- predicting, by the deep-learning controller, an expected final output for an article of manufacture, based on the first set of control values;
- receiving, by the deep-learning controller, a second set of control values associated with a second process station of the plurality of process stations, wherein the second set of control values are attributes of the second process station;
- predicting, by the deep-learning controller, an updated expected final output for the article of manufacture, based on the second set of control values;
- determining if the deep-learning controller can control the manufacturing process based on the expected final output and the updated expected final output by:
- receiving, by the deep-learning controller, an actual value associated with the article of manufacture following processing in the manufacturing system,
- comparing the expected final output with the actual value, and
- determining that the expected final output is within a threshold confidence level of the actual value,
- comparing the updated expected final output with the actual value, and
- determining that the updated expected final output is within the threshold confidence level of the actual value;
- and
- based on the determining, assuming control of the manufacturing process by the deep-learning controller.
A non-transitory computer-readable storage medium for training a deep-learning controller to control a manufacturing process executed in a manufacturing system comprising a plurality of process stations, the non-transitory computer-readable storage medium comprising instructions stored therein, which when executed by one or more processors, cause the one or more processors to perform operations comprising:
- receiving, by the deep-learning controller, a first set of control values associated with a first process station of the plurality of process stations, wherein the first set of control values are attributes of the first process station;
- predicting, by the deep-learning controller, an expected final output for an article of manufacture, based on the first set of control values;
- receiving, by the deep-learning controller, a second set of control values associated with a second process station of the plurality of process stations, wherein the second set of control values are attributes of the second process station;
- predicting, by the deep-learning controller, an updated expected final output for the article of manufacture, based on the second set of control values;
- determining if the deep-learning controller can control the manufacturing process based on the expected final output and the updated expected final output by:
- receiving, by the deep-learning controller, an actual value associated with the article of manufacture following processing in the manufacturing system,
- comparing the expected final output with the actual value, and
- determining that the expected final output is within a threshold confidence level of the actual value,
- comparing the updated expected final output with the actual value, and
- determining that the updated expected final output is within the threshold confidence level of the actual value;
- and
- based on the determining, assuming control of the manufacturing process by the deep-learning controller.
Dependencies are inferred from the claim text.