Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool
First Claim
1. A system, comprising:
- a memory storing computer executable components; and
a processor configured to execute the following computer executable components stored in the memory;
a drift component that adjusts a manufacturing recipe for an asset produced by a manufacturing tool based at least on a set of variables and a probability distribution function to generate an adjusted manufacturing recipe for the asset, wherein a magnitude of change between a recipe parameter included in the manufacturing recipe and an adjusted recipe parameter included in the adjusted manufacturing recipe is determined based at least on the probability distribution function; and
an objective autonomous learning engine that determines a function that predicts output metrics for the asset based on the adjusted manufacturing recipe, and relaxes a constraint for the output metrics to infer the function for the manufacturing tool.
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Abstract
Performance of a manufacturing tool is optimized. Optimization relies on recipe drifting and generation of knowledge that capture relationships among product output metrics and input material measurement(s) and recipe parameters. Optimized recipe parameters are extracted from a basis of learned functions that predict output metrics for a current state of the manufacturing tool and measurements of input material(s). Drifting and learning are related and lead to dynamic optimization of tool performance, which enables optimized output from the manufacturing tool as the operation conditions of the tool changes. Features of recipe drifting and associated learning can be autonomously or externally configured through suitable user interfaces, which also can be drifted to optimize end-user interaction.
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Citations
20 Claims
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1. A system, comprising:
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a memory storing computer executable components; and a processor configured to execute the following computer executable components stored in the memory; a drift component that adjusts a manufacturing recipe for an asset produced by a manufacturing tool based at least on a set of variables and a probability distribution function to generate an adjusted manufacturing recipe for the asset, wherein a magnitude of change between a recipe parameter included in the manufacturing recipe and an adjusted recipe parameter included in the adjusted manufacturing recipe is determined based at least on the probability distribution function; and an objective autonomous learning engine that determines a function that predicts output metrics for the asset based on the adjusted manufacturing recipe, and relaxes a constraint for the output metrics to infer the function for the manufacturing tool. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A method, comprising:
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receiving, by a device comprising a processor, a manufacturing recipe for an asset produced by a manufacturing tool; generating, by the device, a modified manufacturing recipe by modifying the manufacturing recipe based on a set of variables and a probability distribution function that determines a magnitude of change between a parameter value associated with the manufacturing recipe and a modified parameter value associated with the modified manufacturing recipe; and determining, by the device, one or more functions that predict asset output metrics for the asset based on the modified manufacturing recipe, comprising relaxing one or more constraints for the asset output metrics to infer the one or more functions. - View Dependent Claims (8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A non-transitory computer-readable medium comprising a set of code instruction retained therein that, in response to execution, cause a computing system including at least one processor to perform operations, comprising:
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receiving a manufacturing recipe for an asset produced by a manufacturing tool; generating another manufacturing recipe by adjusting the manufacturing recipe based on a set of variables and a probability distribution function that determines a degree of change between a set of recipe parameters included in the manufacturing recipe and a set of other recipe parameters included in the other manufacturing recipe; and determining a function that predicts a set of output metrics for the asset based on the other manufacturing recipe, comprising relaxing a constraint for the set of output metrics to infer the function for the manufacturing tool. - View Dependent Claims (18, 19, 20)
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Specification