IP Library › Granted Patent US 12,322,659
Granted Patent B2
US 12,322,659 · App. 17/680,165 · Granted Jun 3, 2025

Pixel classification of film non-uniformity based on processing of substrate images

Inventors: Dominic J. Benvegnu (La Honda, CA); Nojan Motamedi (Sunnyvale, CA)
Assignee: Applied Materials, Inc.
H01L22/12G01N21/9501G06T7/0004G06T7/001G06T7/10G06T7/90H01L21/30625G06T2207/10024G06T2207/20072G06T2207/30148
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,322,659
App. No.
17/680,165
Granted
Jun 3, 2025
Kind
B2
Abstract

A method of classification of a film non-uniformity on a substrate includes obtaining a color image of a substrate with the color image comprising a plurality of color channels, obtaining a standard color for the color image of the substrate, for each respective pixel along a path in the color image determining a difference vector between the a color of the respective pixel and the standard color to generate a sequence of difference vectors, and sorting the pixels along the path into a plurality of regions including at least one normal region and at least one abnormal region based on the sequence of difference vectors, including comparing a multiplicity of the difference vectors in the sequence to a threshold.

Claims (42)

1. A non-transitory computer readable medium comprising a computer program to classify a film non-uniformity on a substrate, the computer program including instructions to cause one or more computers to:

obtain a color image of a substrate, the color image comprising a plurality of color channels;

determine an intensity histogram for each channel of the plurality of channels of the color image;

select an intensity of a peak in each respective histogram;

set a standard color for the color image of the substrate as a tuple having values corresponding to the intensities of the peaks;

for each respective pixel along a path in the color image, determine a difference vector between a color of the respective pixel and the standard color to generate a sequence of difference vectors;

classify the pixels along the path as normal or abnormal based on the sequence of difference vectors by comparing a multiplicity of the difference vectors in the sequence to a threshold; and

sort the pixels into one or more regions in response to the pixels being identified as normal or abnormal.

2. The computer readable medium of claim 1 , wherein the instructions to classify the pixels comprise instructions to determine for each respective difference vector of the multiplicity of difference vectors whether a magnitude of the respective difference vector exceeds a first threshold value.

3. The computer readable medium of claim 2 , wherein the instructions to classify the pixels comprise instructions to label a pixel as abnormal in response to determining that the magnitude of the respective difference exceeds the first threshold value.

4. The computer readable medium of claim 3 , wherein the instructions to determine the difference comprise instructions to calculate a magnitude of a vector difference between a first tuple representing the color of the respective pixel and the tuple representing the standard color.

5. The computer readable medium of claim 2 , wherein the instructions to classify the pixels comprise instructions to label a pixel as normal based on determining that the magnitude of the respective difference is less than the first threshold value.

6. The computer readable medium of claim 1 , wherein the instructions to classify the pixels comprise instructions to label a pixel based on determining whether each pixel of a plurality of successive pixels along the path exceed a second threshold value.

7. The computer readable medium of claim 1 , wherein the instructions to obtain the standard color comprise instructions to determine a mean color of the color image.

8. The computer readable medium of claim 1 , comprising instructions to apply a mask to the color image to remove scribelines and/or regions outside the substrate.

9. The computer readable medium of claim 1 , comprising instructions to determine a plurality of paths on the substrate, and for each respective path to determine the difference for each pixel along the respective path and sort the pixels along the respective path into the one or more regions.

10. The computer readable medium of claim 9 , wherein the plurality of paths are a plurality of radial paths extending outward from a center of the substrate.

11. The computer readable medium of claim 10 , wherein the plurality of radial paths are spaced uniformly around the center of the substrate.

12. A method of classification of a film non-uniformity on a substrate, comprising:

obtaining a color image of a substrate, the color image comprising a plurality of color channels;

determine an intensity histogram for each channel of the plurality of channels of the color image;

select an intensity of a peak in each respective histogram;

set a standard color for the color image of the substrate as a tuple having values corresponding to the intensities of the peaks;

for each respective pixel along a path in the color image, determining a difference vector between a color of the respective pixel and the standard color to generate a sequence of difference vectors; and

sorting the pixels along the path into a plurality of regions including at least one normal region and at least one abnormal region based on the sequence of difference vectors, including comparing a multiplicity of the difference vectors in the sequence to a threshold.

13. The method of claim 12 , wherein obtaining the color image includes scanning the substrate with an in-line metrology system including a line-scan imager.

14. A polishing system, comprising:

a polisher to polish a substrate;

an in-line metrology system to obtain a color image of a substrate, the color image comprising a plurality of color channels; and

a controller configured to

receive the color image from the in-line metrology system,

determining an intensity histogram for each channel of the plurality of channels of the color image,

selecting an intensity of a peak in each respective histogram,

setting a standard color for the color image of the substrate as a tuple having values corresponding to the intensities of the peaks,

for each respective pixel along a path in the color image, determine a difference vector between a color of the respective pixel and the standard color to generate a sequence of difference vectors,

classify the pixels along the path as normal or abnormal based on the sequence of difference vectors by comparing a multiplicity of the difference vectors in the sequence to a threshold,

sort the pixels into one or more regions in response to the pixels being identified as normal or abnormal, and

adjust a polishing parameter of the polisher for a region having pixels identified as abnormal.

15. The polishing system of claim 14 , wherein the controller is configured to classify the pixels by determining for each respective difference vector of the multiplicity of difference vectors whether a magnitude of the respective difference vector exceeds a first threshold value.

16. The polishing system of claim 15 , wherein controller is configured to classify the pixels by labeling a pixel as abnormal in response to determining that the magnitude of the respective difference vector exceeds the first threshold value.

17. The polishing system of claim 15 , wherein the controller is configured to classify the pixels by labeling a pixel as normal based on determining that the magnitude of the respective difference is less than the first threshold value.

18. The polishing system of claim 15 , wherein the controller is configured to classify the pixels by labeling a pixel based on determining whether each pixel of a plurality of successive pixels along the path exceed a second threshold value.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 3, 2022
From: BENVEGNU, DOMINIC J.; MOTAMEDI, NOJAN
To: APPLIED MATERIALS, INC.
Reel/Frame 059796/0733 →
Continuity (2)
Provisional Application 63156856 · Mar 4, 2021
Related Publication 20220285227A1 · Sep 8, 2022
References Cited (136)
US 5738574A · Tolles et al. · 1998 [cited by applicant]
US 5823853A · Bartels et al. · 1998 [cited by applicant]
US 5911003A · Sones · 1999 [cited by applicant]
US 6004187A · Nyui et al. · 1999 [cited by applicant]
US 6071177A · Lin et al. · 2000 [cited by applicant]
US 6106662A · Bibby et al. · 2000 [cited by applicant]
US 6111634A · Pecen et al. · 2000 [cited by applicant]
US 6142855A · Nyui et al. · 2000 [cited by applicant]
US 6190234B1 · Swedek et al. · 2001 [cited by applicant]
US 6191864B1 · Sandhu · 2001 [cited by applicant]
US 6290572B1 · Hofmann · 2001 [cited by applicant]
US 6358362B1 · En et al. · 2002 [cited by applicant]
US 6361646B1 · Bibby et al. · 2002 [cited by applicant]
US 6466642B1 · Meloni · 2002 [cited by applicant]
US 6511363B2 · Yamane et al. · 2003 [cited by applicant]
US 6517413B1 · Hu et al. · 2003 [cited by applicant]
US 6618130B2 · Chen · 2003 [cited by applicant]
US 6930782B1 · Yi et al. · 2005 [cited by applicant]
US 7008295B2 · Wiswesser et al. · 2006 [cited by applicant]
US 7018271B2 · Wiswesser et al. · 2006 [cited by applicant]
US 7300332B2 · Kobayashi et al. · 2007 [cited by applicant]
US 7406394B2 · Swedek et al. · 2008 [cited by applicant]
US 7438627B2 · Kobayashi et al. · 2008 [cited by applicant]
US 7645181B2 · Kobayashi et al. · 2010 [cited by applicant]
US 7840375B2 · Ravid et al. · 2010 [cited by applicant]
US 8045142B2 · Kimba · 2011 [cited by applicant]
US 8088298B2 · Swedek et al. · 2012 [cited by applicant]
US 8157616B2 · Shimizu et al. · 2012 [cited by applicant]
US 8292693B2 · David et al. · 2012 [cited by applicant]
US 8657646B2 · Benvegnu et al. · 2014 [cited by applicant]
US 8814631B2 · David et al. · 2014 [cited by applicant]
US 9095952B2 · Benvegnu et al. · 2015 [cited by applicant]
US 9106771B2 · Kitai · 2015 [cited by applicant]
US 9822460B2 · Dineen et al. · 2017 [cited by applicant]
US 10325364B2 · Benvegnu · 2019 [cited by applicant]
US 10563973B2 · Li et al. · 2020 [cited by applicant]
US 11017524B2 · Benvegnu · 2021 [cited by applicant]
US 20020016066A1 · Birang et al. · 2002 [cited by applicant]
US 20020055192A1 · Redeker et al. · 2002 [cited by applicant]
US 20020159626A1 · Shiomi et al. · 2002 [cited by applicant]
US 20030007677A1 · Hiroi et al. · 2003 [cited by applicant]
US 20030182051A1 · Yamamoto · 2003 [cited by applicant]
US 20030184742A1 · Stanke et al. · 2003 [cited by applicant]
US 20030205664A1 · Abe et al. · 2003 [cited by applicant]
US 20030207651A1 · Kim et al. · 2003 [cited by applicant]
US 20040012795A1 · Moore · 2004 [cited by applicant]
US 20040028267A1 · Shoham et al. · 2004 [cited by applicant]
US 20040058543A1 · Bothra · 2004 [cited by applicant]
US 20040080757A1 · Stanke et al. · 2004 [cited by applicant]
US 20040259472A1 · Chalmers et al. · 2004 [cited by applicant]
US 20050013481A1 · Toba · 2005 [cited by applicant]
US 20050026542A1 · Battal et al. · 2005 [cited by applicant]
US 20050042975A1 · David · 2005 [cited by applicant]
US 20050089216A1 · Schiller et al. · 2005 [cited by applicant]
US 20050213793A1 · Oya et al. · 2005 [cited by applicant]
US 20050244049A1 · Onishi et al. · 2005 [cited by applicant]
US 20060017855A1 · Yamada · 2006 [cited by applicant]
US 20060020419A1 · Benvegnu · 2006 [cited by applicant]
US 20060061746A1 · Kok et al. · 2006 [cited by applicant]
US 20060166608A1 · Chalmers et al. · 2006 [cited by applicant]
US 20070042675A1 · Benvegnu et al. · 2007 [cited by applicant]
US 20070077671A1 · David et al. · 2007 [cited by applicant]
US 20070206843A1 · Douglass et al. · 2007 [cited by applicant]
US 20080099443A1 · Benvegnu et al. · 2008 [cited by applicant]
US 20080117226A1 · Edge et al. · 2008 [cited by applicant]
US 20090014409A1 · Grimbergen · 2009 [cited by applicant]
US 20090153352A1 · Julio · 2009 [cited by applicant]
US 20090153859A1 · Kimba · 2009 [cited by applicant]
US 20090298387A1 · Shimizu et al. · 2009 [cited by applicant]
US 20100015889A1 · Shimizu et al. · 2010 [cited by applicant]
US 20100067010A1 · Sakai et al. · 2010 [cited by applicant]
US 20100093260A1 · Kobayashi et al. · 2010 [cited by applicant]
US 20100124870A1 · Benvegnu et al. · 2010 [cited by applicant]
US 20110104987A1 · David et al. · 2011 [cited by applicant]
US 20110275281A1 · David et al. · 2011 [cited by applicant]
US 20110318992A1 · David et al. · 2011 [cited by applicant]
US 20120019830A1 · Kimba · 2012 [cited by applicant]
US 20120021672A1 · David et al. · 2012 [cited by applicant]
US 20120026492A1 · Zhang et al. · 2012 [cited by applicant]
US 20120034844A1 · Zhang et al. · 2012 [cited by applicant]
US 20120129277A1 · Lahiri et al. · 2012 [cited by applicant]
US 20120289124A1 · Benvegnu et al. · 2012 [cited by applicant]
US 20130052916A1 · David et al. · 2013 [cited by applicant]
US 20130149938A1 · Kobayashi et al. · 2013 [cited by applicant]
US 20130280989A1 · Lee et al. · 2013 [cited by applicant]
US 20130288571A1 · David et al. · 2013 [cited by applicant]
US 20130344625A1 · Benvegnu et al. · 2013 [cited by applicant]
US 20140011429A1 · David et al. · 2014 [cited by applicant]
US 20140148008A1 · Wu · 2014 [cited by applicant]
US 20140206259A1 · Benvegnu et al. · 2014 [cited by applicant]
US 20150017745A1 · Kimba et al. · 2015 [cited by applicant]
US 20150221077A1 · Kawabata et al. · 2015 [cited by applicant]
US 20150318327A1 · Zheng et al. · 2015 [cited by applicant]
US 20150364387A1 · Cho et al. · 2015 [cited by applicant]
US 20160121451A1 · Moore · 2016 [cited by applicant]
US 20170140525A1 · Benvegnu et al. · 2017 [cited by applicant]
US 20180061032A1 · Benvegnu · 2018 [cited by applicant]
US 20180197052A1 · Yanson et al. · 2018 [cited by applicant]
US 20180301515A1 · Huang · 2018 [cited by applicant]
US 20190244374A1 · Benvegnu et al. · 2019 [cited by applicant]
US 20190295239A1 · Benvegnu · 2019 [cited by applicant]
US 20200258214A1 · Motamedi · 2020 [cited by applicant]
US 20200357836A1 · Miyata et al. · 2020 [cited by applicant]
US 20210248730A1 · Benvegnu · 2021 [cited by applicant]
US 20220284562A1 · Benvegnu et al. · 2022 [cited by applicant]
CN 104982026 · 2015 [cited by applicant]
CN 105354281 · 2016 [cited by applicant]
CN 105957878 · 2016 [cited by applicant]
JP S62042006 · 1987 [cited by applicant]
JP S63221209 · 1988 [cited by applicant]
JP H11207614 · 1999 [cited by applicant]
JP 2000033561A · 2000 [cited by applicant]
JP 2001345299 · 2001 [cited by applicant]
JP 2002359217 · 2002 [cited by applicant]
JP 2003346285 · 2003 [cited by applicant]
JP 2004279297 · 2004 [cited by applicant]
JP 2005129065 · 2005 [cited by applicant]
JP 2010021417 · 2010 [cited by applicant]
JP 2010093147 · 2010 [cited by applicant]
KR 1020170048984 · 2017 [cited by applicant]
TW 200404643 · 2004 [cited by applicant]
TW 200407528 · 2004 [cited by applicant]
TW 200512476 · 2005 [cited by applicant]
TW 201213050 · 2012 [cited by applicant]
TW 201249598 · 2012 [cited by applicant]
TW 201538804 · 2015 [cited by applicant]
TW 201620676 · 2016 [cited by applicant]
TW I549277 · 2016 [cited by applicant]
TW 201938693 · 2019 [cited by applicant]
TW 202043697 · 2020 [cited by applicant]
WO WO2011094706 · 2011 [cited by applicant]
WO WO2012148716 · 2012 [cited by applicant]
Office Action in Taiwanese Appln. No. 111107949, dated Oct. 12, 2022, 13 pages (with English search report). [cited by applicant]
Office Action in Taiwanese Appln. No. 112115313, dated Jun. 30, 2023, 10 pages (with English search report). [cited by applicant]
International Search Report and Written Opinion in International Appln. No. PCT/US2022/017764, dated Jun. 17, 2022, 10 pages. [cited by applicant]
Office Action in Japanese Appln. No. 2023-553541, dated Dec. 3, 2024, 6 pages (with English translation). [cited by applicant]