HOT SPOT DEFECT DETECTING METHOD AND HOT SPOT DEFECT DETECTING SYSTEM

• US 20190318471A1
• Filed: 08/29/2018
• Published: 10/17/2019
• Est. Priority Date: 04/13/2018
• Status: Active Grant

• Current Assignee: Taiwan Semiconductor Manufacturing Company Limited
• Original Assignee: Taiwan Semiconductor Manufacturing Company Limited
• Inventors: Chen, Chien-Huei, Su, Pei-Chao, Chen, Xiaomeng, Chang, Chan-Ming, Chen, Shih-Yung, Chung, Hung-Yi, Chen, Kuang-Shing, Lee, Li-Jou, Lin, Yung-Cheng, Wu, Wei-Chen, Wang, Shih-Chang, Lin, Chien-An
• Granted Patent: US 10,872,406 B2
• Time in Patent Office: Days
• CPC Class Codes: G01N 2021/8854 Grading and classifying of flaws
• CPC Class Codes: G01N 2021/8887 based on image processing techniques
• CPC Class Codes: G01N 21/8851 Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges G01N21/8806 and G01N21/93 - G01N21/95692 take precedence; optical measurement of dimensions G01B11/00; optical scanning G02B26/10; image transformation G06T3/00; computerised image enhancement G06T5/00; image processing per se for flaw detection G06T7/0002
• CPC Class Codes: G01N 21/9505 Wafer internal defects, e.g. microcracks
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/20084 Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30141 Printed circuit board [PCB]
• CPC Class Codes: G06T 2207/30148 Semiconductor; IC; Wafer
• CPC Class Codes: G06T 2207/30168 Image quality inspection
• CPC Class Codes: G06T 7/0006 using a design-rule based approach
• CPC Class Codes: G06T 7/001 using an image reference approach

Abstract

A hot spot defect detecting method and a hot spot defect detecting system are provided. In the method, hot spots are extracted from a design of a semiconductor product to define a hot spot map comprising hot spot groups, wherein local patterns in a same context of the design yielding a same image content are defined as a same hot spot group. During runtime, defect images obtained by an inspection tool performing hot scans on a wafer manufactured with the design are acquired and the hot spot map is aligned to each defect image to locate the hot spot groups. The hot spot defects in each defect image are detected by dynamically mapping the hot spot groups located in each defect image to a plurality of threshold regions and respectively performing automatic thresholding on pixel values of the hot spots of each hot spot group in the corresponding threshold region.

Claims

1. A method for detecting hot spot defects, adapted to an electronic apparatus, the method comprising:

extracting a plurality of hot spots from a design of a semiconductor product to define a hot spot map comprising a plurality of hot spot groups, wherein a plurality of local patterns in a same context of the design yielding a same image content are defined as a same hot spot group;

acquiring a plurality of defect images obtained by an inspection tool performing hot scans on a wafer manufactured with the design during runtime and aligning the hot spot map to each of the defect images to locate the hot spot groups in each defect image; and

detecting the hot spot defects in each defect image by dynamically mapping the hot spot groups located in each defect image to a plurality of threshold regions and respectively performing automatic thresholding on pixel values of the hot spots of each hot spot group in the corresponding threshold region.

2. The method according to claim 1, further comprising:

acquiring a plurality of defect images of a plurality of optical modes obtained by the inspection tool performing hot scans on the wafer manufactured with the design under the plurality of optical modes; and

selecting an optimal optical mode for detecting the hot spot defects from among the plurality of optical modes based on a separability of defects to nuisances in the defect images for each optical mode, wherein

the defect images obtained by the inspection tool performing hot scans on the wafer manufactured with the design under the selected optimal optical mode are acquired for detecting the hot spot defects.

3. The method according to claim 2, wherein the step of selecting the optimal optical mode comprises:

aligning the hot spot map to the defect image of each optical mode to locate the hot spot defects;

computing a signal level and a noise level of each of the hot spot defects in the defect image of each optical mode;

computing the separability for each optical mode by summarizing ratios of the signal level to the noise level of the hot spot defects; and

ranking the optical modes according to the computed separabilities to select the optimal optical mode.

4. The method according to claim 2, further comprising:

training a machine learning model for classifying the defects from the nuisances with the defect images of the selected optical mode to evaluate optimal filters for detecting the hot spot defects.

5. The method according to claim 4, wherein the step of training the machine learning model for classifying the defects from the nuisances with the defect images of the selected optical mode to evaluate the optimal filters for detecting the hot spot defects comprises:

creating the machine learning model with convolution filters for processing the defect images;

feeding a plurality of defect images of the selected optical mode to the machine learning model to train the machine learning model for classifying the defects from the nuisances in the defect images; and

adopting the convolution filters of the trained machine learning model as the optimal filters for detecting the hot spot defects, wherein

the optimal filters are applied to the pixel values of the hot spots of each hot spot group in the corresponding threshold region before performing the automatic thresholding.

6. The method according to claim 1, wherein the performing automatic thresholding on pixel values of the hot spots of each hot spot group in the corresponding threshold region comprises:

determining at least a detection threshold for each of the threshold region based on noise levels of the pixels of the hot spots of each hot spot group in the corresponding threshold region; and

determining the pixels having the pixel values deviating from the detection threshold as the hot spot defect.

7. The method according to claim 1, wherein defects detected in each of the threshold region are mapped to the corresponding hot spot groups as the hot spot defects in the defect image.

8. A system for detecting hot spot defects, comprising:

a connecting device, configured to connect an inspection tool;

a storage medium, configured to store images acquired by the connecting device;

a processor, coupled to the connecting device and the storage medium, and configured to execute instructions to perform steps of;

extracting a plurality of hot spots from a design of a semiconductor product to define a hot spot map comprising a plurality of hot spot groups, wherein a plurality of local patterns in a same context of the design yielding a same image content are defined as a same hot spot group;

acquiring a plurality of defect images obtained by an inspection tool performing hot scans on a wafer manufactured with the design during runtime and aligning the hot spot map to each of the defect images to locate the hot spot groups in each defect image; and

detecting the hot spot defects in each defect image by dynamically mapping the hot spot groups located in each defect image to a plurality of threshold regions and respectively performing automatic thresholding on pixel values of the hot spots of each hot spot group in the corresponding threshold region.

9. The system according to claim 8, wherein the processor is further configured to execute instructions to perform steps of:

acquiring a plurality of defect images of a plurality of optical modes obtained by the inspection tool performing hot scans on the wafer manufactured with the design under the plurality of optical modes; and

selecting an optimal optical mode for detecting the hot spot defects from among the plurality of optical modes based on a separability of defects to nuisances in the defect images for each optical mode, wherein

the defect images obtained by the inspection tool performing hot scans on the wafer manufactured with the design under the selected optimal optical mode are acquired for detecting the hot spot defects.

10. The system according to in claim 9, wherein the processor is further configured to execute instructions to perform steps of:

aligning the hot spot map to the defect image of each optical mode to locate the hot spot defects;

computing a signal level and a noise level of each of the hot spot defects in the defect image of each optical mode;

computing the separability for each optical mode by summing ratios of the signal level to the noise level of the hot spot defects; and

ranking the optical modes according to the computed separabilities to select the optimal optical mode.

11. The system according to claim 9, wherein the processor is further configured to execute instructions to perform a step of:

training a machine learning model for classifying the defects from the nuisances with the defect images of the selected optical mode to evaluate optimal filters for detecting the hot spot defects.

12. The system according to claim 11, wherein the processor is further configured to execute instructions to perform steps of:

creating the machine learning model with convolution filters for processing the defect images;

feeding a plurality of defect images of the selected optical mode to the machine learning mode to train the machine learning model for classifying the defects from the nuisances in the defect images; and

adopting the convolution filters of the trained machine learning model as optimal filters for detecting the hot spot defects, wherein

the optimal filters are applied to the pixel values of the hot spots of each hot spot group in the corresponding threshold region before performing the automatic thresholding.

13. The system according to claim 8, wherein the processor is further configured to execute instructions to perform steps of:

determining at least a detection threshold for each of the threshold region based on noise levels of the pixels of the hot spots of each hot spot group in the corresponding threshold region; and

determining the pixels having the pixel values out of the detection threshold as the hot spot defect.

14. The system according to claim 8, wherein the processor maps defects detected in each of the threshold region to the corresponding hot spot groups as the hot spot defects in the defect image.

15. A method for detecting hot spot defects, adapted to an electronic apparatus, the method comprising:

acquiring a plurality of defect images of a plurality of optical modes obtained by an inspection tool performing hot scans on a wafer manufactured with a design of a semiconductor product under the plurality of optical modes and selecting an optimal optical mode for detecting the hot spot defects from among the plurality of optical modes based on a separability of defects to nuisances in the defect images for each optical mode;

training a machine learning model for classifying the defects from the nuisances with the defect images of the selected optimal optical mode to evaluate optimal filters for detecting the hot spot defects for the optimal optical mode; and

acquiring a plurality of defect images obtained by the inspection tool performing hot scans on the wafer under the optimal optical mode in runtime and aligning a hot spot map comprising a plurality of groups of hot spots extracted from the design to each of the defect images to locate the hot spot groups in each defect image; and

detecting the hot spot defects in each defect image by dynamically mapping the hot spot groups located in each defect image to a plurality of threshold regions, applying the optimal filters to the pixel values of the hot spots of each hot spot group in the corresponding threshold region, and respectively performing automatic thresholding on pixel values of the hot spots of each hot spot group in the corresponding threshold region.

16. The method according to claim 15, wherein in extracting the plurality of hot spots, a plurality of local patterns in a same context of the design that yields a same image content are defined as the hot spots of a same group.

17. The method according to claim 15, wherein the step of selecting the optimal optical mode comprises:

aligning the hot spot map to the defect image of each optical mode to locate the hot spot defects;

computing a signal level and a noise level of each of the hot spot defects in the defect image of each optical mode;

computing the separability for each optical mode by summing ratios of the signal level to the noise level of the hot spot defects; and

ranking the optical modes according to the computed separabilities to select the optimal optical mode.

18. The method according to claim 15, wherein the step of training the machine learning model for classifying the defects from the nuisances with the defect images of the selected optical mode to evaluate the optimal filters for detecting the hot spot defects for the optimal optical mode comprises:

creating the machine learning model with convolution filters for processing the defect images;

feeding a plurality of defect images of the selected optical mode to the machine learning mode to train the machine learning model for classifying the defects from the nuisances in the defect images; and

adopting the convolution filters of the trained machine learning model as optimal filters for detecting the hot spot defects, wherein

the optimal filters are applied to the pixel values of the hot spots of each hot spot group in the corresponding threshold region before performing the automatic thresholding.

19. The method according to claim 15, wherein the step of performing automatic thresholding on pixel values of the hot spots of each hot spot group in the corresponding threshold region comprises:

determining at least a detection threshold for each of the threshold region based on noise levels of the pixels of the hot spots of each hot spot group in the corresponding threshold region; and

determining the pixels having the pixel values out of the detection threshold as the hot spot defect.

20. The method according to claim 15, wherein defects detected in each of the threshold region are mapped to the corresponding hot spot groups as the hot spot defects in the defect image.