US6324298B1
Expired / Lapsed
US
⏱️ 0.0 years remaining
Automated wafer defect inspection system and a process of performing such inspection
Assignee: Rudolph Technologies Inc
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Filing: 1999-07-13
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Granted: 2001-11-27
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Expires: 2019-07-13
Abstract
Abstract An automated defect inspection system has been invented and is used on patterned wafers, whole wafers, broken wafers, partial wafers, sawn wafers such as on film frames, JEDEC trays, Auer boats, die in gel or waffle packs, MCMs, etc. and is specifically intended and designed for second optical wafer inspection for such defects as metalization defects (such as scratches, voids, corrosion, and bridging), diffusion defects, passivation layer defects, scribing defects, glassivation defects, chips and cracks from sawing, solder bump defects, and bond pad area defects.
Inventors
- 👤 Jeffrey O'Dell
- 👤 Mark Harless
- 👤 Thomas Verburgt
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-24 10:35
CPC Classifications
G; G01; G01N; G01N21/00; G01N21/84; G01N21/88; G01N21/95; G01N21/9501; G01N21/8803; G01N21/8806; G06; G06T; G06T7/00; G06T7/0002; G06T7/0004; H; H04; H04N; H04N23/00; H04N23/56; H10; H10P; H10P72/00; H10P72/06; H10P72/0611; H10P72/0616; G01N2021/8838; G01N2201/00; G01N2201/06; G01N2201/061; G01N2201/06113; G01N2201/069; G01N2201/0696; G01N2201/0697; G01N2201/12; G06T2207/00; G06T2207/30; G06T2207/30108; G06T2207/30148; H10P74/00; H10P74/20; H10P74/203
Claims Analysis
Total Claims: 34
Claim 1
Independent Claim
345 words
An automated method of inspecting a semiconductor wafer in any form including whole patterned wafers, sawn wafers, broken wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: training a model as to parameters of a good wafer via optical viewing of multiple known good wafers, the training including aligning and viewing the multiple known good wafers, taking a gray scale measurement of each pixel in a grid of pixels on each of the multiple known good wafers, calculating the mean and standard deviation of all of the gray scale measurements for each pixel location on the multiple known good wafers, and then determining an upper and lower limit for each pixel location in between which a gray scale measurement on an equivalent pixel on an unknown quality wafer is deemed good while a gray scale measurement on an equivalent pixel on an unknown quality wafer outside of the upper and lower limits is deemed of at least questionable integrity and so noted, the upper limit being calculated as the maximum of one of the mean plus the product of the standard deviation multiplied by a first sensitivity, or the mean plus a first minimum contrast, and the lower limit being calculated as the minimum of one of the mean minus the product of the standard deviation multiplied by a second sensitivity, or the mean plus a second minimum contrast; and inspecting unknown quality wafers using the model, where such inspecting involves calculating the gray scale measurement for each pixel on the unknown quality wafer and comparing the gray scale measurement of each pixel with the upper and lower limit for that given pixel location to determine if the gray scale measurement for that pixel is between the upper and lower limits and thus deemed good or is outside of a range between the upper and lower limits and thus deemed of at least questionable integrity.
Claim 2
Dependent Claim
345 words
An automated method of inspecting a semiconductor wafer in any form including whole patterned wafers, sawn wafers, broken wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: training a model as to parameters of a good wafer via optical viewing of multiple known good wafers, the training including aligning and viewing the multiple known good wafers, taking a gray scale measurement of each pixel in a grid of pixels on each of the multiple known good wafers, calculating the mean and standard deviation of all of the gray scale measurements for each pixel location on the multiple known good wafers, and then determining an upper and lower limit for each pixel location in between which a gray scale measurement on an equivalent pixel on an unknown quality wafer is deemed good while a gray scale measurement on an equivalent pixel on an unknown quality wafer outside of the upper and lower limits is deemed of at least questionable integrity and so noted, the upper limit being calculated as the maximum of one of the mean plus the product of the standard deviation multiplied by a first sensitivity, or the mean plus a first minimum contrast, and the lower limit being calculated as the minimum of one of the mean minus the product of the standard deviation multiplied by a second sensitivity, or the mean plus a second minimum contrast; and inspecting unknown quality wafers using the model, where such inspecting involves calculating the gray scale measurement for each pixel on the unknown quality wafer and comparing the gray scale measurement of each pixel with the upper and lower limit for that given pixel location to determine if the gray scale measurement for that pixel is between the upper and lower limits and thus deemed good or is outside of a range between the upper and lower limits and thus deemed of at least questionable integrity.
Claim 3
Dependent Claim
25 words
The automated inspection method of claim 1 wherein the training a model step includes aligning and viewing via a camera the multiple known good wafers.
Claim 4
Dependent Claim
29 words
The automated inspection method of claim 1 wherein the training model step further includes taking the gray scale measurement of each pixel on a 0 to 255 gray scale.
Claim 5
Dependent Claim
38 words
The automated inspection method of claim 1 further comprising training a system as to inspection parameters which are values that indicate how many standard deviations away from the mean are acceptable in defining the upper and lower limits.
Claim 6
Dependent Claim
20 words
The automated inspection method of claim 1 further comprising training a system as to alignment features and parameters of wafers.
Claim 7
Dependent Claim
51 words
The automated inspection method of claim 1 further comprising training a system as to at least one of the following: wafer geometry, wafer size, die geometry on a wafer, die size on a wafer, number of rows of die on a wafer, and number of columns of die on a wafer.
Claim 8
Dependent Claim
16 words
The automated inspection method of claim 1 further comprising training a system as to inspection parameters.
Claim 9
Dependent Claim
57 words
The automated inspection method of claim 1 further comprising creating an inspection recipe that is used during inspecting to perform at least one of the following: defining how substrates are selected from storage receptacles, defining how dies on a wafer are selected for defect inspection, and defining how an inspection map is imported or exported, if needed.
Claim 10
Dependent Claim
26 words
The method of claim 1 further comprising defect reviewing following the completion of inspecting whereby only die failing to meet the good wafer model are reviewed.
Claim 11
Dependent Claim
351 words
An automated method of inspecting a die on a substrate such as a wafer in any form including whole patterned wafers, sawn wafers, broken wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: training a model as to parameters of a good die via optical viewing of multiple known good die, the training including aligning and viewing the multiple known good die, taking a gray scale measurement of each pixel in a grid of pixels on each of the multiple known good die, calculating the mean and standard deviation of all of the gray scale measurements for each pixel location on the multiple known good die, and then determining an upper and lower limit for each pixel location in between which a gray scale measurement on an equivalent pixel on an unknown quality die is deemed good while a gray scale measurement on an equivalent pixel on an unknown quality die outside of the upper and lower limits is deemed of at least questionable integrity and so noted, the upper limit being calculated as the maximum of one of the mean plus the product of the standard deviation multiplied by a first sensitivity, or the mean plus a first minimum contrast, and the lower limit being calculated as the minimum of one of the mean minus the product of the standard deviation multiplied by a second sensitivity, or the mean plus a second minimum contrast; and inspecting unknown quality die using the model, where such inspecting involves calculating the gray scale measurement for each pixel on the unknown quality die and comparing the gray scale measurement of each pixel with the upper and lower limit for that given pixel location to determine if the gray scale measurement for that pixel is between the upper and lower limits and thus deemed good or is outside of a range between the upper and lower limits and thus deemed of at least questionable integrity.
Claim 12
Dependent Claim
351 words
An automated method of inspecting a die on a substrate such as a wafer in any form including whole patterned wafers, sawn wafers, broken wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: training a model as to parameters of a good die via optical viewing of multiple known good die, the training including aligning and viewing the multiple known good die, taking a gray scale measurement of each pixel in a grid of pixels on each of the multiple known good die, calculating the mean and standard deviation of all of the gray scale measurements for each pixel location on the multiple known good die, and then determining an upper and lower limit for each pixel location in between which a gray scale measurement on an equivalent pixel on an unknown quality die is deemed good while a gray scale measurement on an equivalent pixel on an unknown quality die outside of the upper and lower limits is deemed of at least questionable integrity and so noted, the upper limit being calculated as the maximum of one of the mean plus the product of the standard deviation multiplied by a first sensitivity, or the mean plus a first minimum contrast, and the lower limit being calculated as the minimum of one of the mean minus the product of the standard deviation multiplied by a second sensitivity, or the mean plus a second minimum contrast; and inspecting unknown quality die using the model, where such inspecting involves calculating the gray scale measurement for each pixel on the unknown quality die and comparing the gray scale measurement of each pixel with the upper and lower limit for that given pixel location to determine if the gray scale measurement for that pixel is between the upper and lower limits and thus deemed good or is outside of a range between the upper and lower limits and thus deemed of at least questionable integrity.
Claim 13
Dependent Claim
17 words
The automated inspection method of claim 10 wherein the aligning and viewing is performed via a camera.
Claim 14
Dependent Claim
29 words
The automated inspection method of claim 10 wherein the training model step further includes taking the gray scale measurement of each pixel on a 0 to 255 gray scale.
Claim 15
Dependent Claim
38 words
The automated inspection method of claim 10 further comprising training a system as to inspection parameters which are values that indicate how many standard deviations away from the mean are acceptable in defining the upper and lower limits.
Claim 16
Dependent Claim
288 words
An automated method of inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other water and die package configurations for defects, the method comprising: determining an upper and a lower limit for each pixel on a substrate divided into a grid of pixels, the upper and lower limits for each pixel being calculated as a relationship of a plurality of visually perceived contrasts for that pixel on a plurality of substrates known as good quality substrates and specifically as the upper limit is a maximum of one of a mean of the visually perceived contrast for a given pixel plus a product of a standard deviation of the visually perceived contrast for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit is a minimum of one of the mean of the visually perceived contrast for a given pixel plus a product of the standard deviation of the visually perceived contrast for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel: comparing a visually perceived contrast, for each pixel from an unknown quality substrate with the upper and lower limit for that pixel; and categorizing each compared pixel as either good if Its visually perceived contrast is between the upper and lower limit, or bad if its visually perceived contrast is outside of the upper and lower limit.
Claim 17
Dependent Claim
288 words
An automated method of inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other water and die package configurations for defects, the method comprising: determining an upper and a lower limit for each pixel on a substrate divided into a grid of pixels, the upper and lower limits for each pixel being calculated as a relationship of a plurality of visually perceived contrasts for that pixel on a plurality of substrates known as good quality substrates and specifically as the upper limit is a maximum of one of a mean of the visually perceived contrast for a given pixel plus a product of a standard deviation of the visually perceived contrast for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit is a minimum of one of the mean of the visually perceived contrast for a given pixel plus a product of the standard deviation of the visually perceived contrast for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel: comparing a visually perceived contrast, for each pixel from an unknown quality substrate with the upper and lower limit for that pixel; and categorizing each compared pixel as either good if Its visually perceived contrast is between the upper and lower limit, or bad if its visually perceived contrast is outside of the upper and lower limit.
Claim 18
Dependent Claim
286 words
An automated method of inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: determining an upper and a lower limit for each pixel on a substrate divided into a grid of pixels, the upper and lower limits for each pixel being calculated as a relationship of a gray scale measurement for that pixel on a plurality of substrates known as good quality substrates, and specifically as the upper limit is a maximum of one of a mean of the gray scale measurement for a given pixel plus a product of a standard deviation of the gray scale measurement for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit is a minimum of one of the mean of the gray scale measurement for a given pixel plus a product of the standard deviation of the gray. scale measurement for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel; comparing a gray scale measurement for each pixel from an unknown quality substrate with the upper and lower limit for that pixel; and categorizing each compared pixel as either good if its gray scale measurement is between the upper and lower limit, or bad if its gray scale measurement is outside of the upper and lower limit.
Claim 19
Dependent Claim
286 words
An automated method of inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: determining an upper and a lower limit for each pixel on a substrate divided into a grid of pixels, the upper and lower limits for each pixel being calculated as a relationship of a gray scale measurement for that pixel on a plurality of substrates known as good quality substrates, and specifically as the upper limit is a maximum of one of a mean of the gray scale measurement for a given pixel plus a product of a standard deviation of the gray scale measurement for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit is a minimum of one of the mean of the gray scale measurement for a given pixel plus a product of the standard deviation of the gray. scale measurement for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel; comparing a gray scale measurement for each pixel from an unknown quality substrate with the upper and lower limit for that pixel; and categorizing each compared pixel as either good if its gray scale measurement is between the upper and lower limit, or bad if its gray scale measurement is outside of the upper and lower limit.
Claim 20
Dependent Claim
278 words
An automated method of inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: determining an upper and a lower limit for each pixel on a substrate divided into a grid of pixels, the upper and lower limits for each pixel being calculated as a relationship of a color measurement for that pixel on a plurality of substrates known as good quality substrates, and specifically as the upper limit is a maximum of one of a mean of the color measurement for a given pixel plus a product of a standard deviation of the color measurement for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit is a minimum of one of the mean of the color measurement for a given pixel plus a product of the standard deviation of the color measurement for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel; comparing a color measurement for each pixel from an unknown quality substrate with the upper and lower limit for that pixel; and categorizing each compared pixel as either good if its color measurement is between the upper and lower limit, or bad if its color measurement is outside of the upper and lower limit.
Claim 21
Dependent Claim
278 words
An automated method of inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: determining an upper and a lower limit for each pixel on a substrate divided into a grid of pixels, the upper and lower limits for each pixel being calculated as a relationship of a color measurement for that pixel on a plurality of substrates known as good quality substrates, and specifically as the upper limit is a maximum of one of a mean of the color measurement for a given pixel plus a product of a standard deviation of the color measurement for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit is a minimum of one of the mean of the color measurement for a given pixel plus a product of the standard deviation of the color measurement for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel; comparing a color measurement for each pixel from an unknown quality substrate with the upper and lower limit for that pixel; and categorizing each compared pixel as either good if its color measurement is between the upper and lower limit, or bad if its color measurement is outside of the upper and lower limit.
Claim 22
Dependent Claim
286 words
An automated method of inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: determining a gray scale measurement for each pixel in a plurality of pixels on a known good quality substrate; repeating the determining a gray scale measurement step for a plurality of known good quality substrates; determining an upper and a lower limit from the gray scale measurements for each pixel, the upper limit being determined by calculating a maximum of one of a mean of the gray scale measurement for a given pixel plus a product of a standard deviation of the gray scale measurement for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit being determined by calculating a minimum of one of the mean of the gray scale measurement for a given pixel plus a product of the standard deviation of the gray scale measurement for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel; comparing each pixel from an unknown quality substrate with the upper and lower limit for that given pixel location; and categorizing each compared pixel as either good if its gray scale measurement is between the upper and lower limit, or bad if its gray scale measurement is outside of the upper and lower limit.
Claim 23
Dependent Claim
286 words
An automated method of inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the method comprising: determining a gray scale measurement for each pixel in a plurality of pixels on a known good quality substrate; repeating the determining a gray scale measurement step for a plurality of known good quality substrates; determining an upper and a lower limit from the gray scale measurements for each pixel, the upper limit being determined by calculating a maximum of one of a mean of the gray scale measurement for a given pixel plus a product of a standard deviation of the gray scale measurement for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit being determined by calculating a minimum of one of the mean of the gray scale measurement for a given pixel plus a product of the standard deviation of the gray scale measurement for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel; comparing each pixel from an unknown quality substrate with the upper and lower limit for that given pixel location; and categorizing each compared pixel as either good if its gray scale measurement is between the upper and lower limit, or bad if its gray scale measurement is outside of the upper and lower limit.
Claim 24
Dependent Claim
326 words
An automated system for inspecting a substrate such as a wafer in any form including whole patterned wafers, sawn wafers, broken wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the system comprising: a wafer test plate; means for providing a wafer to the test plate; a wafer alignment device for aligning each and every wafer provided to the test plate at the same x, y, z, and θ location; a visual inspection device for visual inputting of a plurality of known good quality wafers during training and for visual Inspection of other unknown quality wafers during inspection, a microprocessor having processing and memory capabilities for developing a model of a good quality wafer by taking a gray scale measurement for each said pixel on a plurality of known good quality wafers and determining an upper and a lower limit for each pixel from the plurality of known good quality wafers, the upper limit being a maximum of one of a mean of the gray scale measurement for a given pixel plus a product of a standard deviation of the gray scale measurement for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit being a minimum of one of the mean of the gray scale measurement for a given pixel plus a product of the standard deviation of the gray scale measurement for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel, and the microprocessor further comparing other unknown quality wafers in comparison to the model by determining a gray scale measurement for each pixel thereon and comparing it to the upper and lower limits.
Claim 25
Dependent Claim
326 words
An automated system for inspecting a substrate such as a wafer in any form including whole patterned wafers, sawn wafers, broken wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the system comprising: a wafer test plate; means for providing a wafer to the test plate; a wafer alignment device for aligning each and every wafer provided to the test plate at the same x, y, z, and θ location; a visual inspection device for visual inputting of a plurality of known good quality wafers during training and for visual Inspection of other unknown quality wafers during inspection, a microprocessor having processing and memory capabilities for developing a model of a good quality wafer by taking a gray scale measurement for each said pixel on a plurality of known good quality wafers and determining an upper and a lower limit for each pixel from the plurality of known good quality wafers, the upper limit being a maximum of one of a mean of the gray scale measurement for a given pixel plus a product of a standard deviation of the gray scale measurement for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit being a minimum of one of the mean of the gray scale measurement for a given pixel plus a product of the standard deviation of the gray scale measurement for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel, and the microprocessor further comparing other unknown quality wafers in comparison to the model by determining a gray scale measurement for each pixel thereon and comparing it to the upper and lower limits.
Claim 26
Dependent Claim
28 words
The automated system of claim 18 further comprising a focusing mechanism for focusing an image of at least a portion of the wafer as seen by the camera.
Claim 27
Dependent Claim
14 words
The automated system of claim 18 where the visual inspection device is a camera.
Claim 28
Dependent Claim
15 words
The automated system of claim 18 where the visual inspection device is a CCD camera.
Claim 29
Dependent Claim
37 words
The automated system of claim 18 further comprising a parameter input device for inputting parameters and other constraints or information including sensitivity parameters, geometries, die size, die shape, die pitch, number of rows, and number of columns.
Claim 30
Dependent Claim
29 words
The automated system of claim 18 further comprising a display for displaying the view being seen by the visual inspection device at present or at any previous saved period.
Claim 31
Dependent Claim
29 words
The automated system of claim 18 further including a marking head for placing a mark on or adjacent a pixel on a wafer that is outside of the model.
Claim 32
Dependent Claim
42 words
The automated system of claim 18 further including a frame on which the wafer test plate, the wafer alignment device, the visual inspection device, and the computer system are attached, and a hood for enclosing an inspection area over the test plate.
Claim 33
Dependent Claim
327 words
An automated system for inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the system comprising: a wafer test plate; means for providing a wafer to the test plate; a wafer alignment device for aligning each and every wafer provided to the test plate at the same x, y, z, and θ location; a visual inspection device for visual inputting of a plurality of known good quality wafers during training and for visual inspection of other unknown quality wafers during inspection, a microprocessor having processing and memory capabilities for developing a model of a good quality wafer by taking a visually perceived contrast for each said pixel on a plurality of known good quality wafers and determining an upper and a lower limit for each pixel from the plurality of known good quality wafers, the upper limit being a maximum of one of a mean of the visually perceived contrast for a given pixel plus a product of a standard deviation of the visually perceived contrast for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit being a minimum of one of the mean of the visually perceived contrast for a given pixel plus a product of the standard deviation of the visually perceived contrast for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel, and the microprocessor further comparing other unknown quality wafers in comparison to the model by determining a visually perceived contrast for each pixel thereon and comparing it to the upper and lower limits.
Claim 34
Dependent Claim
327 words
An automated system for inspecting a substrate such as a wafer in any form including bare wafers, whole wafers, patterned wafers, sawn wafers, and wafers of any kind on film frames, dies, die in gel paks, die in waffle paks, multi-chip modules often called MCMs, JEDEC trays, Auer boats, and other wafer and die package configurations for defects, the system comprising: a wafer test plate; means for providing a wafer to the test plate; a wafer alignment device for aligning each and every wafer provided to the test plate at the same x, y, z, and θ location; a visual inspection device for visual inputting of a plurality of known good quality wafers during training and for visual inspection of other unknown quality wafers during inspection, a microprocessor having processing and memory capabilities for developing a model of a good quality wafer by taking a visually perceived contrast for each said pixel on a plurality of known good quality wafers and determining an upper and a lower limit for each pixel from the plurality of known good quality wafers, the upper limit being a maximum of one of a mean of the visually perceived contrast for a given pixel plus a product of a standard deviation of the visually perceived contrast for the given pixel multiplied by a first sensitivity, or the mean for the given pixel plus a first minimum contrast for the given pixel, and the lower limit being a minimum of one of the mean of the visually perceived contrast for a given pixel plus a product of the standard deviation of the visually perceived contrast for the given pixel multiplied by a second sensitivity, or the mean for the given pixel plus a second minimum contrast for the given pixel, and the microprocessor further comparing other unknown quality wafers in comparison to the model by determining a visually perceived contrast for each pixel thereon and comparing it to the upper and lower limits.
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USPTO Annuity & Maintenance Windows
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| 7.5 Years (Year 8) | 2008-11-26 | 2009-05-28 | 2009-11-27 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2012-11-26 | 2013-05-28 | 2013-11-27 | $7700.00 | Window Closed / Lapsed |