Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool
First Claim
1. An autonomous biologically based learning system, comprising:
- a manufacturing tool that produces an asset;
a drift component that modifies a manufacturing recipe processed by the manufacturing tool at least in part using a set of driving variables and a predetermined probability distribution function to generate one or more adjusted manufacturing recipes for the asset, wherein the set of driving variables determine a particular sequence to modify a set of recipe parameters associated with the manufacturing recipe;
an objective autonomous learning engine that infers one or more functions for the manufacturing tool based on the modified manufacturing recipe processed by the manufacturing tool, wherein the one or more functions predict asset output metrics for the produced asset; and
an autonomous optimization engine that extracts a set of updated recipe parameters from a set of input measurements and the one or more inferred functions to generate an adjusted recipe within a predefined tolerance of a target value for the asset output metrics.
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Abstract
System(s) and method(s) for optimizing performance of a manufacturing tool are provided. 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
21 Claims
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1. An autonomous biologically based learning system, comprising:
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a manufacturing tool that produces an asset; a drift component that modifies a manufacturing recipe processed by the manufacturing tool at least in part using a set of driving variables and a predetermined probability distribution function to generate one or more adjusted manufacturing recipes for the asset, wherein the set of driving variables determine a particular sequence to modify a set of recipe parameters associated with the manufacturing recipe; an objective autonomous learning engine that infers one or more functions for the manufacturing tool based on the modified manufacturing recipe processed by the manufacturing tool, wherein the one or more functions predict asset output metrics for the produced asset; and an autonomous optimization engine that extracts a set of updated recipe parameters from a set of input measurements and the one or more inferred functions to generate an adjusted recipe within a predefined tolerance of a target value for the asset output metrics. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A method, comprising:
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employing at least one processor to execute code instructions retained in a memory, the code instructions when executed by at least the one processor carry out acts, comprising; determining a particular sequence for modifying one or more parameters associated with an initial recipe; modifying the initial recipe at least in part using the particular sequence and a predetermined probability distribution function to fabricate a product that fulfills target product output; learning a set of relationships associated with product output through the modifying of the initial recipe; generating an adjusted recipe to accomplish the target product output based at least in part on the set of learned relationships; evaluating whether the target product output is fulfilled in response to determining that generation of the adjusted recipe is successful; and continuing the modifying in response to determining that the evaluation yields an unsuccessful outcome. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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21. A non-transitory computer-readable storage 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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determining a particular sequence for modifying one or more parameters associated with an initial recipe; modifying the initial recipe at least in part using the particular sequence and a predetermined probability distribution function to fabricate a product that fulfills target product output; learning a set of relationships associated with product output through the modifying of the initial recipe; generating an adjusted recipe to accomplish the target product output based at least in part on the set of learned relationships; evaluating whether the target product output is fulfilled in response to determining that generation of the adjusted recipe is successful; and continuing the modifying in response to determining that the evaluation results in an unsuccessful outcome.
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Specification