Neural network for determining the endpoint in a process

• US 20070214098A1
• Filed: 12/27/2006
• Published: 09/13/2007
• Est. Priority Date: 06/21/2002
• Status: Active Grant

• Current Assignee: Macronix International Co., Ltd.
• Original Assignee: Macronix International Co., Ltd.
• Inventors: Ho, Tsung-Hsuan
• Granted Patent: US 7,403,930 B2
• Time in Patent Office: Days
• US Class Current: 706/18
• CPC Class Codes: G05B 13/027 using neural networks only
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: H01L 22/26 Acting in response to an ongoing measurement without interruption of processing, e.g. endpoint detection, in-situ thickness measurement endpoint detection arrangements in CMP apparatus B24B37/013, in discharge apparatus H01J37/32

Abstract

There is provided a system and method for pattern recognition of an endpoint curve for a dry etch process. The system trains a neural network with a group of training curves corresponding to the dry etch process, wherein the training curves contain normal and abnormal features. The system receives an endpoint curve at the neural network representing a dry etch process and detects an abnormal feature in the endpoint curve.

Claims

1. A method for pattern recognition of an endpoint curve for a process comprising:

training a neural network with a group of training curves corresponding to the process, wherein the training curves contain normal and abnormal features;

receiving an endpoint curve at the neural network representing a process; and

detecting an abnormal feature in the endpoint curve.

2. The method as set forth in claim 1, wherein the neural network uses a back-propagation learning rule.

3. The method as set forth in claim 1, further comprising collecting the group of training curves.

4. The method as set forth in claim 1, further comprising identifying a normal feature from the group of training curves.

5. The method as set forth in claim 1, further comprising collecting an analog signal of the endpoint curve.

6. The method as set forth in claim 5, further comprising transforming the analog signal to a digital signal.

7. A system for pattern recognition of an endpoint curve for a process comprising:

means for training a neural network to learn a feature corresponding to the process;

means for receiving the endpoint curve; and

means for detecting an abnormal feature in the endpoint curve.

8. The system as set forth in claim 7, further comprising a group of training curves corresponding to the process.

9. The system as set forth in claim 8, further comprising means for collecting the group of training curves.

10. The system as set forth in claim 7, further comprising means for collecting an analog signal of the endpoint curve.

11. The system as set forth in claim 10, further comprising means for transforming the analog signal to a digital signal.

12. The system as set forth in claim 7, further comprising means for comparing the endpoint curve with the feature.

13. The system as set forth in claim 7, wherein the means for training the neural network uses a back-propagation learning rule.

14. A computer readable medium including instructions for performing a method, when executed by a processor, for pattern recognition of an endpoint curve for a process comprising:

training a neural network with a group of training curves corresponding to the process, wherein the training curves contain normal and abnormal features;

receiving an endpoint curve at the neural network representing a process; and

detecting an abnormal feature in the endpoint curve.

15. The computer readable medium as set forth in claim 14, wherein the neural network uses a back-propagation learning rule.

16. The computer readable medium as set forth in claim 14, further comprising collecting the group of training curves.

17. The computer readable medium as set forth in claim 14, further comprising identifying a normal feature from the group of training curves.

18. The computer readable medium as set forth in claim 14, further comprising collecting an analog signal of the endpoint curve.

19. The computer readable medium as set forth in claim 18, further comprising transforming the analog signal to a digital signal.

20. A method for pattern recognition of an endpoint curve for a process comprising:

setting up a neural network;

collecting a group of analog training curves;

converting the group of analog training curves to a group of digital training curves;

training the neural network with the group of digital training curves;

collecting a group of analog experimental curves;

converting the group of analog training curves to a group of digital training curves;

inputting the group of digital experimental curves into the neural network; and

determining by the neural network the endpoint of the group of digital training curves.

21. The method as set forth in claim 20, wherein the group of analog training curves includes at least one of a normal or abnormal curve.

22. The method as set forth in claim 20, wherein the group of analog experimental training curves includes at least one of a normal or abnormal curve.

23. The method as set forth in claim 20, wherein the neural network uses a back-propagation learning rule.

24. The method as set forth in claim 20, wherein training the neural network comprises:

classifying the group of digital training curves;

dividing the group of digital training curves into a first and second set of digital training curves;

training the neural network with the first set of digital training curves;

testing the neural network;

inputting the second set of digital training curves into the neural network; and

determining whether the neural network is trained.

25. The method as set forth in claim 24, wherein training the neural network further comprises:

adding-a new classification to the group of digital training curves.

26. A system for pattern recognition of an endpoint curve for a process comprising:

a process chamber for etching a semiconductor substrate;

a detector for detecting radiation from the process chamber during the etching of the semiconductor substrate and producing a radiation signal;

a recorder coupled to the detector for recording the radiation signal;

an analog to digital converter coupled to the recorder for converting the radiation signal; and

a pattern recognition apparatus coupled to the analog to digital converter for detecting the endpoint curve of the converted radiation signal.

27. The system as set forth in claim 26, wherein the pattern recognition apparatus comprises a neural network using a back-propagation learning rule.