Applied Materials ML-based equipment parameter management patent granted

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ChangeBridge: Patent Grants - AI & Computing (G06N)

Published March 31st, 2026

Detected March 31st, 2026

Summary

USPTO granted Applied Materials, Inc. patent US12591805B2 for a machine learning-based equipment parameter management method in manufacturing systems. The patent covers predicting equipment metric values using trained ML models and performing corrective actions based on comparisons. The patent application (No. 17821349) was filed August 22, 2022, and contains 20 claims.

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What changed

USPTO granted Applied Materials, Inc. patent US12591805B2 for a method of managing equipment parameters in manufacturing systems using machine learning. The patent covers receiving equipment data, providing it to trained ML models, predicting equipment metric values, comparing predictions with actual data, and initiating corrective actions. The three inventors are Tsung-Liang Chen, Lars Henrik Schneider, and Michael David Armacost.

This patent grant confers exclusive intellectual property rights to Applied Materials in this technological area. Competitors developing ML-based manufacturing equipment parameter management systems should review the patent claims to assess potential infringement exposure. Licensing discussions may be warranted for parties seeking to implement similar systems.

Source document (simplified)

← USPTO Patent Grants

Equipment parameter management at a manufacturing system using machine learning

Grant US12591805B2 Kind: B2 Mar 31, 2026

Assignee

Applied Materials, Inc.

Inventors

Tsung-Liang Chen, Lars Henrik Schneider, Michael David Armacost

Abstract

A method includes receiving first data associated with an equipment parameter. The first data is indicative of an equipment setting of a process tool of a plurality of process tools at a first manufacturing system. The method further includes providing the first data as input to a trained machine learning model. The trained machine learning model is trained using historical data pertaining to equipment parameters of the plurality of process tools at the first manufacturing system. The method further includes obtaining, as output of the trained machine learning model, a predicted value of a metric corresponding to the equipment parameter. The method further includes comparing the predicted value of the metric with the first data, and performing a corrective action based on the comparing.