US5982920A — Automated defect spatial signature analysis for s…

Claim 1

Independent Claim 77 words

A method of performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising the steps of: categorizing data from the data set into a plurality of high level event categories including global events, curvilinear events, amorphous events, and micro-structure events; classifying the categorized data contained in each high level category into user-labeled signature events; and correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Claim 2

Independent Claim 77 words

A method of performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising the steps of: categorizing data from the data set into a plurality of high level event categories including global events, curvilinear events, amorphous events, and micro-structure events; classifying the categorized data contained in each high level category into user-labeled signature events; and correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Claim 3

Dependent Claim 34 words

A method according to claim 1, wherein the categorizing step includes generating a grey-scale density image ρ(x,y) from the data set and inputting the grey-scale density image to means for performing the classifying step.

Claim 4

Dependent Claim 21 words

A method according to claim 2, wherein the density image is a composite of several overlaying events within the data set.

Claim 5

Dependent Claim 30 words

A method according to claim 3, further comprising reducing the data set to simpler, non-overlapping or nearly non-overlapping sets that can be individually analyzed to assign the user-labeled signature events.

Claim 6

Dependent Claim 108 words

A method according to claim 1, wherein the global events category is defined as including any unclustered single pixel events, the curvilinear category is defined as including any curvilinear and radial clustered events associated with wafer spinning processes and mechanical surface damage, polishing anomalies, the amorphous category is defined as including nonlinear, tightly grouped cluster events arising from various processes, including lithography, etch, watermarks, and particle stains, and the microstructure category is defined as a collection of distributed single-pixel events which contain laser distributions of defects which reside on the image sub-pixel level and are related to small fine micro-scratching from chemical and mechanical polishing for wafer planarization.

Claim 7

Independent Claim 90 words

A method of performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising the steps of: forming a density image, ρ(x,y), from the data set; parsing the density image into two categories based on defect density values: low-density, potentially random events and higher-density, potentially clustered events, thereby segmenting random defect distributions from other signature type events; classifying the data contained in each category into user-labeled signature events; and correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Claim 8

Independent Claim 90 words

A method of performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising the steps of: forming a density image, ρ(x,y), from the data set; parsing the density image into two categories based on defect density values: low-density, potentially random events and higher-density, potentially clustered events, thereby segmenting random defect distributions from other signature type events; classifying the data contained in each category into user-labeled signature events; and correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Claim 9

Dependent Claim 34 words

A method according to claim 6, further comprising re-evaluating the low-density, random event image using a connectivity analysis to determine if some of the events should be removed and merged with the clustered-objects image.

Claim 10

Dependent Claim 27 words

A method according to claim 7, further comprising moving the initially random events back into the clustered image if connected groups of low-density defect pixels are found.

Claim 11

Dependent Claim 31 words

A method according to claim 6, further comprising binarizing the images for higher-density clustered events and processing the binarized imaged to account for nearest-neighbor relationships and relationships between large connected groups.

Claim 12

Dependent Claim 37 words

A method according to claim 9, wherein the processing includes applying a dilation technique to the clustered images so that neighboring objects are grouped into single objects, thereby pulling in some scattered defects around a single event.

Claim 13

Dependent Claim 34 words

A method according to claim 10, further comprising determining the features of the resulting image, wherein the features include area, elongation, compactness, and proximity, and then combining the measured features using fuzzy logic-type constraints.

Claim 14

Dependent Claim 29 words

A method according to claim 11, further comprising breaking the combined features into initial approximations of a curvilinear mask, M C (x,y), and an amorphous mask, M A (x,y).

Claim 15

Dependent Claim 67 words

A method according to claim 12, wherein the mask is a binary image used to filter out areas of the target image, whereby if the mask value is 0, areas are removed and a remainder of the target image where the value is 1 passes through, and wherein the masks are eventually used to generate three final event images on a display device coupled to a computer.

Claim 16

Dependent Claim 20 words

A method according to claim 13, further comprising directionally dilating objects based on surrounding structure prior to final feature measurement.

Claim 17

Dependent Claim 39 words

A method according to claim 14, wherein each object in the initial curvilinear mask, M C (x,y), is measured to determine a direction of desired elongation for a selected object, based on the distance and size of neighboring objects.

Claim 18

Dependent Claim 14 words

A method according to claim 6, wherein the data set is wafer map data.

Claim 19

Dependent Claim 15 words

A method according to claim 6, wherein the data set is flat panel display data.

Claim 20

Independent Claim 79 words

An apparatus for performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising: means for categorizing data from the data set into a plurality of high level categories including global events, curvilinear events, amorphous events, and micro-structure events; means for classifying the categorized data contained in each high level category into user-labeled signature events; and means for correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Claim 21

Independent Claim 79 words

An apparatus for performing automated defect spatial signature analysis on a data set representing defect coordinates and wafer processing information, comprising: means for categorizing data from the data set into a plurality of high level categories including global events, curvilinear events, amorphous events, and micro-structure events; means for classifying the categorized data contained in each high level category into user-labeled signature events; and means for correlating the categorized, classified signature events to a present or incipient anomalous process condition.

Claim 22

Dependent Claim 35 words

An apparatus according to claim 18, wherein the categorizing means includes means for generating a grey-scale density image ρ(x,y) from the data set and means for inputtng the grey-scale density image to the classifying means.

Claim 23

Dependent Claim 21 words

An apparatus according to claim 19, wherein the density image is a composite of several overlaying events within the data set.

Claim 24

Dependent Claim 32 words

An apparatus according to claim 20, further comprising means for reducing the data set to simpler, non-overlapping or nearly non-overlapping sets that can be individually analyzed to assign the user-labeled signature events.

Claim 25

Dependent Claim 108 words

An apparatus according to claim 19, wherein the global events category is defined as including any unclustered single pixel events, the curvilinear category is defined as including any curvilinear and radial clustered events associated with wafer spinning processes and mechanical surface damage, polishing anomalies, the amorphous category is defined as including nonlinear, tightly grouped cluster events arising from various processes, including lithography, etch, watermarks, and particle stains, and the micro-structure category is defined as a collection of distributed single-pixel events which contain linear distributions of defects which reside on the image sub-pixel level and are related to small fine micro-scratching from chemical and mechanical polishing for wafer planarization.

Claim 26

Dependent Claim 47 words

An apparatus according to claim 19, further comprising means for forming a density image, ρ(x,y), and parsing the density image into two categories based on defect density values: low-density, potentially random events and higher-density, potentially, clustered events, thereby segmenting random defect distributions from other signature type events.

Claim 27

Dependent Claim 36 words

An apparatus according to claim 23, further comprising means for re-evaluating the low-density, random event image using a connectivity analysis to determine if some of the events should be removed and merged with the clustered-objects image.

Claim 28

Dependent Claim 29 words

An apparatus according to claim 24, further comprising means for moving the initial random events back into the clustered image if connected groups of low-density defect pixels are found.

Annuity Stage	Window Opens	Payment Deadline	Surcharge Period Until	Estimated Fee	Status
3.5 Years (Year 4)	2002-11-08	2003-05-10	2003-11-09	$2000.00	Window Closed / Lapsed
7.5 Years (Year 8)	2006-11-08	2007-05-10	2007-11-09	$3760.00	Window Closed / Lapsed
11.5 Years (Year 12)	2010-11-08	2011-05-10	2011-11-09	$7700.00	Window Closed / Lapsed

Automated defect spatial signature analysis for semiconductor manufacturing process

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