Lu Chen - Sunnyvale CA, US Jason Kirkwood - Santa Clara CA, US Mohan Mahadevan - Livermore CA, US James A. Smith - Los Altos CA, US Lisheng Gao - Morgan Hill CA, US Junqing (Jenny) Huang - Fremont CA, US Tao Luo - Fremont CA, US Richard Wallingford - San Jose CA, US
Assignee:
KLA-Tencor Corp. - San Jose CA
International Classification:
G01N 21/00
US Classification:
3562372, 3562373
Abstract:
Systems and methods for detecting defects on a wafer are provided. One method includes generating output for a wafer by scanning the wafer with an inspection system using first and second optical states of the inspection system. The first and second optical states are defined by different values for at least one optical parameter of the inspection system. The method also includes generating first image data for the wafer using the output generated using the first optical state and second image data for the wafer using the output generated using the second optical state. In addition, the method includes combining the first image data and the second image data corresponding to substantially the same locations on the wafer thereby creating additional image data for the wafer. The method further includes detecting defects on the wafer using the additional image data.
Systems And Methods For Detecting Defects On A Wafer
Lu Chen - Sunnyvale CA, US Jason Kirkwood - Santa Clara CA, US Mohan Mahadevan - Livermore CA, US James A. Smith - Los Altos CA, US Lisheng Gao - Morgan Hill CA, US Junqing (Jenny) Huang - Fremont CA, US Tao Luo - Fremont CA, US Richard Wallingford - San Jose CA, US
Assignee:
KLA-Tencor Corp. - San Jose CA
International Classification:
G01N 21/00
US Classification:
3562372, 3562373
Abstract:
Systems and methods for detecting defects on a wafer are provided. One method includes generating output for a wafer by scanning the wafer with an inspection system using first and second optical states of the inspection system. The first and second optical states are defined by different values for at least one optical parameter of the inspection system. The method also includes generating first image data for the wafer using the output generated using the first optical state and second image data for the wafer using the output generated using the second optical state. In addition, the method includes combining the first image data and the second image data corresponding to substantially the same locations on the wafer thereby creating additional image data for the wafer. The method further includes detecting defects on the wafer using the additional image data.
Yong Gao - Fremont CA, US Junqing Huang - Fremont CA, US Lisheng Gao - Morgan Hill CA, US
Assignee:
KLA-TENCOR CORPORATION - Milpitas CA
International Classification:
G06K 9/78
US Classification:
382149, 382170
Abstract:
A processor-based method for detecting defects in an integrated circuit, by creating an image of at least a portion of the integrated circuit with a sensor, grouping pixels of the image into bins based at least in part on a common characteristic of the pixels that are grouped within a given bin, creating a histogram of the pixels in each of the bins, calculating a mean value of the histogram for each of the bins, comparing the mean value for each of the bins to a threshold value, flagging as defect candidates those bins where the mean value of the bin varies from the threshold value by more than a predetermined amount, and performing signature detection on the bins that are flagged as defect candidates, where the image of the integrated circuit is not directly compared to any other image of an integrated circuit.
Lisheng Gao - Morgan Hill CA, US Kenong Wu - Davis CA, US Allen Park - San Jose CA, US Ellis Chang - Saratoga CA, US Khurram Zafar - San Jose CA, US Junqing Huang - Fremont CA, US Ping Gu - Milpitas CA, US Christopher Maher - Campbell CA, US Grace H. Chen - Los Gato CA, US Songnian Rong - San Jose CA, US
Assignee:
KLA-TENCOR CORPORATION - Milpitas CA
International Classification:
G06K 9/62
US Classification:
382149
Abstract:
The present invention includes searching imagery data in order to identify one or more patterned regions on a semiconductor wafer, generating one or more virtual Fourier filter (VFF) working areas, acquiring an initial set of imagery data from the VFF working areas, defining VFF training blocks within the identified patterned regions of the VFF working areas utilizing the initial set of imagery data, wherein each VFF training block is defined to encompass a portion of the identified patterned region displaying a selected repeating pattern, calculating an initial spectrum for each VFF training block utilizing the initial set of imagery data from the VFF training blocks, and generating a VFF for each training block by identifying frequencies of the initial spectrum having maxima in the frequency domain, wherein the VFF is configured to null the magnitude of the initial spectrum at the frequencies identified to display spectral maxima.
Junqing Huang - Fremont CA, US Yong Zhang - Cupertino CA, US Stephanie Chen - Fremont CA, US Tao Luo - Fremont CA, US Lisheng Gao - Morgan Hill CA, US Richard Wallingford - San Jose CA, US
Assignee:
KLA-TENCOR CORPORATION - Milpitas CA
International Classification:
G01N 21/88 G06F 19/00
US Classification:
702 40
Abstract:
Methods and systems for detecting defects on a wafer are provided.
Systems And Methods For Detecting Defects On A Wafer
Jason Kirkwood - Santa Clara CA, US Mohan Mahadevan - Livermore CA, US James A. Smith - Los Altos CA, US Lisheng Gao - Morgan Hill CA, US Junqing (Jenny) Huang - Fremont CA, US Tao Luo - Fremont CA, US Richard Wallingford - San Jose CA, US
Assignee:
KLA-Tencor Corporation - San Jose CA
International Classification:
G01N 21/95
US Classification:
3562375
Abstract:
Systems and methods for detecting defects on a wafer are provided. One method includes generating output for a wafer by scanning the wafer with an inspection system using first and second optical states of the inspection system. The first and second optical states are defined by different values for at least one optical parameter of the inspection system. The method also includes generating first image data for the wafer using the output generated using the first optical state and second image data for the wafer using the output generated using the second optical state. In addition, the method includes combining the first image data and the second image data corresponding to substantially the same locations on the wafer thereby creating additional image data for the wafer. The method further includes detecting defects on the wafer using the additional image data.
Nuisance Reduction Using Location-Based Attributes
- Milpitas CA, US Junqing Huang - Fremont CA, US Lisheng Gao - Saratoga CA, US
International Classification:
G06T 7/00 G06T 7/73
Abstract:
Methods and systems are disclosed that provide nuisance reduction in images, such as semiconductor images that include one or more metal lines. A potential defect is correlated against pixel grey level intensity charts for two perpendicular axes. A position of the potential defect relative to a pattern, such as a metal line, is determined along the two axes. The potential defect can be classified as a defect of interest or nuisance event.
- Milpitas CA, US Xiaochun Li - Milpitas CA, US Pavan Kumar - San Jose CA, US Junqing Huang - Fremont CA, US Lisheng Gao - Saratoga CA, US Grace H. Chen - Los Gatos CA, US Yalin Xiong - Pleasanton CA, US Hawren Fang - San Jose CA, US
International Classification:
G06T 7/00
Abstract:
Systems and methods increase the signal to noise ratio of optical inspection of wafers to obtain higher inspection sensitivity. The computed reference image can minimize a norm of the difference of the test image and the computed reference image. A difference image between the test image and a computed reference image is determined. The computed reference image includes a linear combination of a second set of images.