Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool
First Claim
1. A system, comprising:
- a processor, coupled to a memory, comprising computer-executable components, comprising;
a drift component that modifies a manufacturing recipe processed by a manufacturing tool that produces an asset based at least in part on a set of driving variables and a defined probability distribution function to generate a modified manufacturing recipe for the asset, wherein the set of driving variables determine a sequence in which to modify a set of recipe parameters associated with the manufacturing recipe, and wherein a magnitude of change between the manufacturing recipe and the modified manufacturing recipe is determined based at least in part on the defined probability distribution function.
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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 processor, coupled to a memory, comprising computer-executable components, comprising; a drift component that modifies a manufacturing recipe processed by a manufacturing tool that produces an asset based at least in part on a set of driving variables and a defined probability distribution function to generate a modified manufacturing recipe for the asset, wherein the set of driving variables determine a sequence in which to modify a set of recipe parameters associated with the manufacturing recipe, and wherein a magnitude of change between the manufacturing recipe and the modified manufacturing recipe is determined based at least in part on the defined probability distribution function. - View Dependent Claims (2, 3, 4, 5)
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6. A method, comprising:
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determining, by a device comprising a processor, a sequence for modifying one or more parameters associated with an initial recipe; and modifying the initial recipe to fabricate a product that is predicted to fulfill a target product output based at least in part on the sequence and a defined probability distribution function that determines a magnitude of the modifying of the initial recipe. - View Dependent Claims (7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17)
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18. 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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determining a sequence for modifying one or more parameters associated with an initial recipe; and modifying the initial recipe to fabricate a product that fulfills a target product output based at least in part on the sequence and a defined probability distribution function that determines a degree of the modifying of the initial recipe. - View Dependent Claims (19, 20)
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Specification