Systems and methods for microvoid analysis in crystals grown by continuous czochralski pullers

Patent metadata

Field	Value
Publication number	US-2024352616-A1
Application number	US-202418635262-A
Country	US
Kind code	A1
Filing date	Apr 15, 2024
Priority date	Apr 18, 2023
Publication date	Oct 24, 2024
Grant date	—

Abstract

A computer device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: a) receive at least one image of a silicon melt of a crystal in a crucible; b) execute a model trained to segment the at least one image into different classes; c) analyze segmentation to determine a quality of the crystal; and/or d) approve or reject the crystal based upon the analysis.

First claim

What is claimed is:

1 . A computer device comprising at least one processor in communication with at least one memory device, wherein the at least one processor programmed to:
receive at least one image of a silicon melt of a crystal in a crucible; execute a model trained to segment the at least one image into different classes; analyze segmentation to determine a quality of the crystal; and approve or reject the crystal based upon the analysis.

2 . The computer device of claim 1 , wherein the silicon melt is associated with a Continuous Czochralski process.

3 . The computer device of claim 2 , wherein the crucible is a triple crucible.

4 . The computer device of claim 1 , wherein the model is trained to segment the at least one image pixel by pixel.

5 . The computer device of claim 4 , wherein the model generates an output of a segmented image.

6 . The computer device of claim 5 , wherein the at least one processor is further programmed to mask the segmented image to determine a percentage of area associated with one or more classifications.

7 . The computer device of claim 4 , wherein the model is trained to segment the at least one image into a plurality of segments including, but not limited to, background, silicon melt liquid, cullet, and crucible.

8 . The computer device of claim 1 , wherein the at least one processor is programmed to reject the crystal if an area of silicon melt liquid exceeds one fourth of a total surface area in the crucible.

9 . The computer device of claim 1 , wherein the model is a semantic segmentation network, and wherein the at least one processor is further programmed to train the model with a plurality of pixel-labeled images for the model to classify pixels of images into pixel categories.

10 . The computer device of claim 9 , wherein the at least one processor is further programmed to retrain the model with a subsequent plurality of pixel-labeled images.

11 . The computer device of claim 1 , wherein the at least one processor is further programmed to approve or reject the crystal based upon a number of micro-voids predicted to occur in the crystal based upon the analysis.

12 . The computer device of claim 1 , wherein the at least one image is received from a camera positioned perpendicular to an external silicon melt annulus.

13 . A computer-implemented method performed by a computer system including at least one processor in communication with a chatbot and at least one memory device, the method comprising:
receiving at least one image of a silicon melt of a crystal in a crucible; executing a model trained to segment the at least one image into different classes; analyzing segmentation to determine a quality of the crystal; and approving or rejecting the crystal based upon the analysis.

14 . The computer-implemented method of claim 13 , wherein the silicon melt is associated with a Continuous Czochralski process.

15 . The computer-implemented method of claim 14 , wherein the crucible is a triple crucible.

16 . The computer-implemented method of claim 13 , wherein the model is trained to segment the at least one image pixel by pixel.

17 . The computer-implemented method of claim 16 , wherein the model generates an output of a segmented image.

18 . The computer-implemented method of claim 17 further comprising masking the segmented image to determine a percentage of area associated with one or more classifications.

19 . The computer-implemented method of claim 16 , wherein the model is trained to segment the at least one image into a plurality of segments including, but not limited to, background, silicon melt liquid, cullet, and crucible.

20 . The computer-implemented method of claim 13 further comprising rejecting the crystal if an area of silicon melt liquid exceeds one fourth of a total surface area in the crucible.

21 . The computer-implemented method of claim 13 , wherein the model is a semantic segmentation network, and wherein the method further compromises training the model with a plurality of pixel-labeled images for the model to classify pixels of images into pixel categories.

22 . The computer-implemented method of claim 21 further comprising retraining the model with a subsequent plurality of pixel-labeled images.

23 . The computer-implemented method of claim 13 further comprising approving or rejecting the crystal based upon a number of micro-voids predicted to occur in the crystal based upon the analysis.

24 . The computer-implemented method of claim 13 , wherein the at least one image is received from a camera positioned perpendicular to an external silicon melt annulus.

Assignees

• Globalwafers Co Ltd

Inventors

• Pannocchia Matteo
• Marchese Francesca
• Rigon Enrico

Classifications

• C30B29/06

  Silicon · CPC title

• C30B15/10

  Crucibles or containers for supporting the melt · CPC title

• C30B15/002

  Continuous growth · CPC title

• G06T2207/20072

  Graph-based image processing · CPC title

• G06T2207/20076

  Probabilistic image processing · CPC title