Machine vision inspection of wafer processing tool

Patent metadata

Field	Value
Publication number	US-2025187845-A1
Application number	US-202218683942-A
Country	US
Kind code	A1
Filing date	Aug 21, 2022
Priority date	Sep 3, 2021
Publication date	Jun 12, 2025
Grant date	—

Abstract

Examples are disclosed that relate to diagnosing a condition of a wafer processing tool using a machine learning classifier. One example provides an electrodeposition tool comprising a cup. The cup comprises a wafer interface. The wafer interface comprises a lip seal and a plurality of electrical contacts. The electrodeposition tool further comprises a camera positioned to image at least a portion of the wafer interface. The electrodeposition tool further comprises a logic machine, and a storage machine storing instructions executable by the logic machine. The instructions are executable to acquire an image of the wafer interface via the camera. The instructions are further executable to obtain a classification of the image of the wafer interface from a trained machine learning function. The instructions are further executable to control the electrodeposition tool to take an action based on the classification.

First claim

1 . An electrodeposition tool, comprising:
a cup comprising a wafer interface, the wafer interface comprising a lip seal and a plurality of electrical contacts; a camera positioned to image at least a portion of the wafer interface; a logic machine; and a storage machine storing instructions executable by the logic machine to:
acquire an image of the wafer interface via the camera,
obtain a classification of the image of the wafer interface from a trained machine learning function, and
control the electrodeposition tool to take an action based on the classification.

2 . The electrodeposition tool of claim 1 , wherein the wafer interface is configured to rotate, and wherein the camera is configured to capture a plurality of images of the wafer interface at a corresponding plurality of angles of rotation of the wafer interface.

3 . The electrodeposition tool of claim 2 , wherein the instructions are executable to obtain a classification of each image of the plurality of images.

4 . The electrodeposition tool of claim 1 , wherein the instructions are executable to transmit the image of the wafer interface to a remote computing system hosting the trained machine learning function, and to obtain the classification of the image from the remote computing system.

5 . The electrodeposition tool of claim 1 , wherein the trained machine learning function comprises a residual neural network.

6 . The electrodeposition tool of claim 1 , wherein the instructions are executable to control the electrodeposition tool to execute a cleaning program in response to obtaining a classification of dirty.

7 . The electrodeposition tool of claim 1 , wherein the instructions are executable to control the electrodeposition tool to execute a cell drying program in response to obtaining a classification of wet.

8 . The electrodeposition tool of claim 1 , wherein the instructions are executable to control the electrodeposition tool to output an error code for user intervention in response to obtaining a classification of damaged.

9 . The electrodeposition tool of claim 1 , wherein the instructions are executable to control the electrodeposition tool to continue normal operation in response to obtaining a classification of one of normal or ambiguous.

10 . A method for operating an electrodeposition tool, the method comprising:
acquiring an image of a wafer interface of the electrodeposition tool via a camera; obtaining a classification of the image from a trained machine learning function; and upon obtaining the classification, controlling the electrodeposition tool to execute a maintenance program based upon the classification.

11 . The method of claim 10 , wherein the classification comprises a dirty classification, and the maintenance program comprises a cell cleaning program.

12 . The method of claim 10 , wherein the classification comprises a wet classification, and the maintenance program comprises a cell drying program.

13 . The method of claim 10 , wherein the classification comprises a damage classification, and the maintenance program comprises triggering output of an error code for user intervention.

14 . The method of claim 10 , further comprising acquiring a plurality of images of the wafer interface at a corresponding plurality of angles of rotation of the wafer interface, and obtaining a classification of each image of the plurality of images from the trained machine learning function.

15 . The method of claim 10 , further comprising obtaining a classification of normal, and controlling the electrodeposition tool to continue normal operation.

16 . The method of claim 10 , further comprising obtaining an ambiguous classification, and, in response, triggering a warning code.

17 . A computing system comprising:
a logic machine; and a storage machine holding instructions executable by the logic machine to
obtain an image of a wafer interface of an electrodeposition tool, the wafer interface comprising a lip seal and a plurality of electrical contacts,
obtain a classification via inputting the image into a trained machine learning function, and
output the classification.

18 . The computing system of claim 17 , wherein the trained machine learning function comprises a residual neural network.

19 . The computing system of claim 17 , wherein the instructions are further executable to crop the image of the wafer interface prior to inputting the image into the trained machine learning function.

20 . The computing system of claim 17 , wherein the instructions are further executable to train the trained machine learning function using labeled training images that are each labeled with a classification of one of normal, wet, dirty, or damaged.

Assignees

• Lam Res Corp

Inventors

• Yang Liu
• Li Mengping
• Ghongadi Shantinath

Classifications

• G05B13/027

  using neural networks only · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06V10/82

  using neural networks · CPC title

• G06V2201/06

  Recognition of objects for industrial automation · CPC title

• G06N3/08

  Learning methods · CPC title