Pre-bonding automatic optical inspection defect classification

Patent metadata

Field	Value
Publication number	US-2024331131-A1
Application number	US-202318128491-A
Country	US
Kind code	A1
Filing date	Mar 30, 2023
Priority date	Mar 30, 2023
Publication date	Oct 3, 2024
Grant date	—

Abstract

A method, apparatus and system for the automatic detection and measurement of chipping defects on diced wafers includes receiving an image of at least a portion of a diced wafer, aligning the received image of the at least the portion of the diced wafer, determining edges of the at least the portion of the diced wafer depicted in the aligned, received image, automatically determining at least one baseline from which to measure chipping defects on the at least the portion of the diced wafer from the determined edges, and measuring chipping defects on the at least the portion of the diced wafer using at least one determined, respective baseline. In some embodiments, the method, apparatus and system can further include applying a machine learning model to measured chipping defects to determine if a critical failure exists on the diced wafer.

First claim

1 . A method for the automatic detection and measurement of chipping defects on diced wafers, comprising:
receiving an image of at least a portion of a diced wafer; aligning the received image of the at least the portion of the diced wafer; determining edges of the at least the portion of the diced wafer depicted in the aligned, received image; automatically determining at least one baseline from which to measure chipping defects on the at least the portion of the diced wafer from the determined edges; and measuring chipping defects on the at least the portion of the diced wafer using at least one determined, respective baseline.

2 . The method of claim 1 , further comprising:
aligning the received image along a vertical axis.

3 . The method of claim 1 , further comprising:
aligning the received image along a horizontal axis.

4 . The method of claim 1 , further comprising:
determining a reference axis line; and aligning the received image along the determined, reference axis line.

5 . The method of claim 1 , further comprising:
measuring chipping defects as a distance from the determined respective baseline to an end of a chipping defect.

6 . The method of claim 1 , further comprising:
applying a machine learning model to measured chipping defects to determine if a critical failure exists on the diced wafer.

7 . The method of claim 6 wherein the machine learning model is trained to recognize measured chipping defects that result in a critical failure of the diced wafer.

8 . An apparatus for the automatic detection and measurement of chipping defects on diced wafers, comprising:
a processor; and a memory coupled to the processor, the memory having stored therein at least one of programs or instructions executable by the processor to configure the apparatus to: receive an image of at least a portion of a diced wafer; align the received image of the at least the portion of the diced wafer; determine edges of the at least the portion of the diced wafer depicted in the aligned, received image; automatically determine at least one baseline from which to measure chipping defects on the at least the portion of the diced wafer from the determined edges; and measure chipping defects on the at least the portion of the diced wafer using the at least one determined baseline.

9 . The apparatus of claim 8 , wherein the received image is aligned along a vertical axis.

10 . The apparatus of claim 8 wherein the received image is aligned along a horizontal axis.

11 . The apparatus of claim 8 , wherein the apparatus is further configured to:
determine a reference axis line; and align the received image along the determined, reference axis line.

12 . The apparatus of claim 8 , wherein chipping defects are measured as a distance from the determined respective baseline to an end of a chipping defect.

13 . The apparatus of claim 8 , wherein the apparatus is further configure to:
apply a machine learning model to measured chipping defects to determine if a critical failure exists on the diced wafer.

14 . The apparatus of claim 13 , wherein the machine learning model is trained to recognize measured chipping defects that result in a critical failure of the diced wafer.

15 . A system for the automatic detection and measurement of chipping defects on diced wafers, comprising:
an image capture device for capturing images of at least a portion of a diced wafer; and an apparatus comprising:
a processor; and
a memory having stored therein at least one program, the at least one program including instructions which, when executed by the processor, cause the system to perform a method, comprising;
capturing an image of at least a portion of a diced wafer;
aligning the captured image of the at least the portion of the diced wafer;
determining edges of the at least the portion of the diced wafer depicted in the aligned, received image;
automatically determining at least one baseline from which to measure chipping defects on the at least the portion of the diced wafer from the determined edges; and
measuring chipping defects on the at least the portion of the diced wafer using at least one determined, respective baseline.

16 . The system of claim 15 , wherein the method further comprises:
aligning captured images along at least one of a vertical axis or a horizontal axis.

17 . The system of claim 15 , wherein the method further comprises:
determining a reference axis line; and aligning the captured images along the determined, reference axis line.

18 . The system of claim 15 , wherein the method further comprises:
Measuring chipping defects as a distance from the determined respective baseline to an end of a chipping defect.

19 . The system of claim 15 , wherein the method further comprises:
applying a machine learning model to measured chipping defects to determine if a critical failure exists on the diced wafer.

20 . The system of claim 19 , wherein the machine learning model is trained to recognize measured chipping defects that result in a critical failure of the diced wafer.

Assignees

• Applied Materials Inc

Inventors

• Komatireddi Rahul Reddy
• Cherikkallil Rohith
• Kongara Sneha Rupa
• Mishra Satwik Swarup
• Dangayach Sachin
• Chan Si En
• Koh Remus Zhen Hui
• Lianto Prayudi
• Lim Yin Wei
• Suo Peng
• Mallavaram Krishnaprasad Reddy
• Cheng Khor Wui

Classifications

• G06T7/13

  Edge detection · CPC title

• G06T7/001 Primary

  using an image reference approach · CPC title

• G06T7/0004 Primary

  Industrial image inspection · CPC title

• G06T2207/30148

  Semiconductor; IC; Wafer · CPC title

• G06T2207/20081

  Training; Learning · CPC title