US 2010/0138026 A1 · METHOD AND APPARATUS FOR SELF-LEARNING AND SELF-IMPROVING A SEMICONDUCTOR MANUFACTURING TOOL

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.

Claims21

Dependent claims

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 to generate a set of one or more adjusted manufacturing recipes to produce the asset;
an objective autonomous learning engine that infers one or more functions among asset output metrics and at least one of input material measurements, recipe parameters, or state records for the manufacturing tool based on the modified 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 recipe parameters from a set of input measurements and the one or more inferred functions to generate an adjusted manufacturing recipe with predicted output within a tolerance of target asset output metrics.

2 more elements

A method, comprising:

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 the following acts:
drifting an initial recipe to fabricate a product that fulfills target product output;
learning a set of relationships associated with product output through the drifting 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;
and
when generation of the adjusted recipe is successful, evaluating whether the target product output is fulfilled;
and
when the evaluation yields a negative outcome, continue drifting the initial recipe to fabricate a product that fulfills the target product output.

5 more elements

Dependencies are inferred from the claim text.

Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool

Life of this patent

Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool

Life of this patent

Abstract

Claims21