Organic defect source identification using artificial intelligence

An AI-based system accurately identifies and classifies organic defects in manufacturing systems, improving efficiency and yield by predicting defect types and sources in substrate images, thus reducing manual inspection errors.

TW202632260APending Publication Date: 2026-08-01APPLIED MATERIALS INC
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Patent Information

Application Number
TW114140383
Authority / Receiving Office
TW · TW
Patent Type
Applications
Current Assignee / Owner
Priority Date
2025-01-27
Filing Date
2025-10-20
Publication Date
2026-08-01

AI Technical Summary

Technical Problem

Existing methods for identifying organic defects in manufacturing systems are inefficient due to the transparent nature of these defects, which are difficult to detect and classify using standard imaging techniques, often requiring manual inspection prone to human error, leading to misclassification and reduced manufacturing efficiency and yield.

Method used

A system using artificial intelligence (AI) to analyze substrate images, identifying and classifying organic defects by training an AI model to predict defect types and sources based on image data, reducing the need for manual inspection and improving accuracy.

Benefits of technology

The AI-based system enhances defect detection and classification accuracy, minimizing human error and resource waste, thereby increasing manufacturing efficiency and yield by promptly addressing the true sources of defects.

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Abstract

Methods and systems for organic defect source identification using artificial intelligence (AI) are provided. One or more images depicting a surface of a substrate processed using one or more manufacturing equipment is provided as input to an artificial intelligence (AI) model. Output(s) of the AI model is obtained, where the output(s) indicate defect characterization data associated with a defect detected on the surface of the substrate depicted by the one or more images. A determination is made of whether a level of confidence of at least one defect type and associated defect source from the defect characterization data satisfies one or more confidence criteria. Upon a determination that at least one defect type and associated defect source satisfy the one or more confidence criteria, the at least one defect type and associated defect source are provided to a client device for presentation to one or more users.
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