DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending regarding this application.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 01/19/2025 and 07/25/2025 are
considered and attached.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
In claim 1: “an inspection subsystem configured for generating images for a specimen”
In claims 19 and 20: “generating one or more statistics for each of multiple tiles in difference image frames generated for a job of images for a specimen generated by an inspection subsystem”
After a careful analysis, as disclosed above, and a careful review of the specification, the above limitations in claims 1, 19, and 20 are NOT interpreted as computer-implemented 112(f). Below is the corresponding structure which is being read into the above limitation(s):
“an inspection subsystem” (In the specification, page 8, lines 15-19 define the inspection subsystem as a light-based, electron beam, or charged particle beam inspection subsystem. Additionally, applicant’s specification describes that “the inspection subsystem is configured for scanning energy (e.g., light, electrons, etc.) over a physical version of the specimen thereby generating output for the physical version of the specimen. In this manner, the inspection subsystem may be configured as an “actual” subsystem, rather than a “virtual” subsystem” on page 16, lines 18-28. Therefore, the corresponding structure for the inspection subsystem is a physical structure configured to scan energy and generate image(s) of a specimen).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 12, 14, 16, 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sousa et al. (U.S. Publication No. 2018/0238816 A1), hereinafter Sousa, in view of Patwary et al. (U.S. Publication No. 2020/0244963 A1), hereinafter Patwary.
Regarding claim 1, Sousa teaches a system configured for detecting defects on a specimen (Sousa, see FIG. 2), comprising:
an inspection subsystem configured for generating images for a specimen (Sousa teaches generating images of reticles in any suitable manner, including an incident beam, see para. [0043]-[0044], para. [0070], and FIG. 2); and
a computer subsystem configured for:
generating one or more statistics for each of multiple tiles in difference image frame(Sousa teaches a set of die portions (referred to as “patches”) which are interpreted as equivalent to the claimed tiles in para. [0055]-[0056]; see also FIG. 5A. Sousa further teaches that “a delta map may then be generated based on the difference integrated values that are determined for the patch images in operation 560” in para. [0080], wherein “the map can optionally or additionally be represented by a metric, such as the standard deviation or variance of the difference integrated intensity value” as shown in para. [0081]; the delta map (statistics) are generated with respect to the aligned reference images (difference image) as shown in FIG. 2 #205; see also Applicant’s specification which states that difference images may be generated through an image alignment process on page 2, lines 30-31);
identifying outlier tiles in the multiple tiles based on the one or more statistics generated for each of the multiple tiles (Sousa teaches that “the delta map will tend to indicate any variation between a pattern characteristic of a particular patch and a reference average or median pattern characteristic of the particular patch's die-equivalent patches from both reticles with optional exclusion of outlier patches” in para. [0080]; since there is an optional process of outlier patch exclusion, it is inherent that outlier patches are identified);
determining one or more defect detection parameters of a defect detection method for each of the difference image frames based on the identified outlier tiles (Sousa teaches that “defects may then be reported based on the delta map or a statistics map in operation 562”, wherein “the threshold can be based on the amount of variance from the average” as shown in para. [0092]; since it is optional that the outlier tiles may be included in the map, it is inherent that the threshold as taught by Sousa may at least in part be based on the outlier tiles; see FIG. 2 #206 wherein the thresholds (parameters) are used in regards to the difference image); and
detecting defects on the specimen by applying the defect detection method to the difference image frame(Sousa teaches using a threshold based on the delta map (the claimed defect detection method) to define defects in para. [0092]; see also FIG. 5B #562).
Sousa fails to teach multiple difference image frames generated for a job of images.
However, Patwary teaches multiple difference image frames generated for a job of images (Patwary teaches “one or more difference image frames of a sample are acquired” wherein “the one or more difference images may be acquired from any source known in the art including, but not limited to, the inspection sub-system 102” in para. [0080]; “the one or more difference image frames may include one or more difference image frames which are based on and/or generated from one or more target image frames and one or more reference image frames” in para. [0081]; here, “the controller 104 may be configured to cause the inspection sub-system 102 to acquire one or more target image frames 125 a - 125 h” as shown in para. [0046]. As a result, the “one or more” target image frames are interpreted as equivalent to the claimed job of images).
Sousa and Patwary are both considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa to incorporate the teachings of Patwary and include “multiple difference image frames generated for a job of images”. The motivation for doing so would have been that “by subtracting the estimated background structure of the reference image frame 145 c from the target image frame 125 c , a defect 137 within the target image frame 125 c may be clearly shown in the generated difference image frame 155 c . By removing the background structure (e.g., subtracting reference image frames 145 ), defects 137 within the target image frames 125 may be clearly revealed”, as suggested by Patwary in para. [0065]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa with Patwary to obtain the invention specified in claim 1.
Regarding claim 2, Sousa and Patwary teach the system of claim 1,
wherein the difference image frames comprise difference image frames generated for only one threshold region in the images (Sousa teaches using a threshold based on the delta map (the claimed defect detection method) to define defects in the difference image in para. [0092]; see also FIG. 5B #562. Here, there is a singular threshold being used for the difference image) (Patwary teaches multiple difference image frames as shown in claim 1). Similar motivations as applied to claim 1 can be applied here to claim 2.
Regarding claim 12, Sousa and Patwary teach the system of claim 1,
wherein when one of the difference image frames is determined to be a grossly defective image frame based on the one or more statistics (Sousa teaches that “defects may then be reported based on the delta map or a statistics map in operation 562”, wherein “the threshold can be based on the amount of variance from the average” as shown in para. [0092]; since it is optional that the outlier tiles may be included in the map, it is inherent that the threshold as taught by Sousa may at least in part be based on the outlier tiles; see FIG. 2 #206 wherein the thresholds (parameters) are used in regards to the difference image. Sousa additionally teaches that, “If an intensity variation is above the predefined or statistics-based threshold, the corresponding patch may then be more carefully reviewed to determine whether the reticle is defective and can no longer be used” in para. [0117]; determining that the reticle ius no longer able to be used is determined as equivalent to determining the image frame is “grossly defective”),
determining the one or more defect detection parameters for the one of the difference image frames comprises determining a threshold max based on at least one of the one or more statistics generated for non-outlier tiles in the job (Sousa teaches that “the [defect detection] threshold can be based on the amount of variance from the average” in para. [0092]; here, the statistics map can optionally exclude outlier patches (tiles), which means the aforementioned threshold may be based on statistics generated for non-outlier patches) (Patwary teaches multiple difference image frames in a job as shown in claim 1). Similar motivations as applied to claim 1 can be applied here to claim 12.
Regarding claim 14, Sousa and Patwary teach the system of claim 1,
wherein when one of the difference image frames is determined to be a grossly defective image frame based on the one or more statistics (Sousa teaches that “defects may then be reported based on the delta map or a statistics map in operation 562”, wherein “the threshold can be based on the amount of variance from the average” as shown in para. [0092]; since it is optional that the outlier tiles may be included in the map, it is inherent that the threshold as taught by Sousa may at least in part be based on the outlier tiles; see FIG. 2 #206 wherein the thresholds (parameters) are used in regards to the difference image. Sousa additionally teaches that, “If an intensity variation is above the predefined or statistics-based threshold, the corresponding patch may then be more carefully reviewed to determine whether the reticle is defective and can no longer be used” in para. [0117]; determining that the reticle ius no longer able to be used is determined as equivalent to determining the image frame is “grossly defective”),
determining the one or more defect detection parameters for the one of the difference image frames comprises determining a threshold max based on at least one of the one or more statistics generated for non-outlier tiles in the one of the difference image frames (Sousa teaches that “the [defect detection] threshold can be based on the amount of variance from the average” in para. [0092]; here, the statistics map can optionally exclude outlier patches (tiles), which means the aforementioned threshold may be based on statistics generated for non-outlier patches) (Patwary teaches multiple difference image frames as shown in claim 1). Similar motivations as applied to claim 1 can be applied here to claim 14.
Regarding claim 16, Sousa and Patwary teach the system of claim 1,
wherein determining the one or more defect detection parameters comprises determining a threshold max based on the one or more statistics to thereby detect defects on the specimen having a characteristic sufficient to affect the one or more statistics in any one of the difference image frames (Sousa teaches that “defects may then be reported based on the delta map or a statistics map in operation 562”, wherein “it may be determined whether any difference intensity value or delta value is above a predefined threshold” and “the threshold can be based on the amount of variance from the average” as shown in para. [0092]; see FIG. 2 #206 wherein the thresholds (parameters) are used in regards to the difference image) (Patwary teaches multiple difference image frames as shown in claim 1). Similar motivations as applied to claim 1 can be applied here to claim 16.
Regarding claim 17, Sousa and Patwary teach the system of claim 16,
wherein the computer subsystem is further configured for determining one or more attributes of the detected defects based on results of the detecting (While Sousa teaches determining the detected defects based on results of the detecting (see claim 1), Patwary teaches “the controller 104 may be configured to determine”…”defects (e.g., defect location, defect type)” in para. [0068])).
Sousa and Patwary are both considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa to incorporate the teachings of Patwary and include “wherein the computer subsystem is further configured for determining one or more attributes of the detected defects based on results of the detecting”. The motivation for doing so would have been that “In the context of semiconductor fabrication, accurately identifying the type and size of defects is an important step in improving throughput and yield” as suggested by Patwary in para. [0003]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa with Patwary to obtain the invention specified in claim 17.
Regarding claim 19, Sousa teaches a non-transitory computer-readable medium, storing program instructions executable on a computer system (Sousa teaches “instructions/computer code for performing various operations described herein that can be stored on a non-transitory computer readable media” in para. [0142]) for performing a computer-implemented method for detecting defects on a specimen (Sousa, see FIGS. 2-4), wherein the computer-implemented method comprises:
generating one or more statistics for each of multiple tiles in difference image frame (Sousa teaches a set of die portions (referred to as “patches”) which are interpreted as equivalent to the claimed tiles in para. [0055]-[0056]; see also FIG. 5A. Sousa further teaches that “a delta map may then be generated based on the difference integrated values that are determined for the patch images in operation 560” in para. [0080], wherein “the map can optionally or additionally be represented by a metric, such as the standard deviation or variance of the difference integrated intensity value” as shown in para. [0081]; the delta map (statistics) are generated with respect to the aligned reference images (difference image) as shown in FIG. 2 #205; see also Applicant’s specification which states that difference images may be generated through an image alignment process on page 2, lines 30-31);
identifying outlier tiles in the multiple tiles based on the one or more statistics generated for each of the multiple tiles (Sousa teaches that “the delta map will tend to indicate any variation between a pattern characteristic of a particular patch and a reference average or median pattern characteristic of the particular patch's die-equivalent patches from both reticles with optional exclusion of outlier patches” in para. [0080]; since there is an optional process of outlier patch exclusion, it is inherent that outlier patches are identified);
determining one or more defect detection parameters of a defect detection method for each of the difference image frames based on the identified outlier tiles (Sousa teaches that “defects may then be reported based on the delta map or a statistics map in operation 562”, wherein “the threshold can be based on the amount of variance from the average” as shown in para. [0092]; since it is optional that the outlier tiles may be included in the map, it is inherent that the threshold as taught by Sousa may at least in part be based on the outlier tiles; see FIG. 2 #206 wherein the thresholds (parameters) are used in regards to the difference image); and
detecting defects on the specimen by applying the defect detection method to the difference image frame(Sousa teaches using a threshold based on the delta map (the claimed defect detection method) to define defects in para. [0092]; see also FIG. 5B #562).
Sousa fails to teach multiple difference image frames generated for a job of images.
However, Patwary teaches multiple difference image frames generated for a job of images (Patwary teaches “one or more difference image frames of a sample are acquired” wherein “the one or more difference images may be acquired from any source known in the art including, but not limited to, the inspection sub-system 102” in para. [0080]; “the one or more difference image frames may include one or more difference image frames which are based on and/or generated from one or more target image frames and one or more reference image frames” in para. [0081]; here, “the controller 104 may be configured to cause the inspection sub-system 102 to acquire one or more target image frames 125 a - 125 h” as shown in para. [0046]. As a result, the “one or more” target image frames are interpreted as equivalent to the claimed job of images).
Sousa and Patwary are both considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa to incorporate the teachings of Patwary and include “multiple difference image frames generated for a job of images”. The motivation for doing so would have been that “by subtracting the estimated background structure of the reference image frame 145 c from the target image frame 125 c , a defect 137 within the target image frame 125 c may be clearly shown in the generated difference image frame 155 c . By removing the background structure (e.g., subtracting reference image frames 145 ), defects 137 within the target image frames 125 may be clearly revealed”, as suggested by Patwary in para. [0065]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa with Patwary to obtain the invention specified in claim 19.
Regarding claim 20, Sousa teaches a computer-implemented method for detecting defects on a specimen (Sousa, see FIGS. 2-4), comprising:
generating one or more statistics for each of multiple tiles in difference image frame (Sousa teaches a set of die portions (referred to as “patches”) which are interpreted as equivalent to the claimed tiles in para. [0055]-[0056]; see also FIG. 5A. Sousa further teaches that “a delta map may then be generated based on the difference integrated values that are determined for the patch images in operation 560” in para. [0080], wherein “the map can optionally or additionally be represented by a metric, such as the standard deviation or variance of the difference integrated intensity value” as shown in para. [0081]; the delta map (statistics) are generated with respect to the aligned reference images (difference image) as shown in FIG. 2 #205; see also Applicant’s specification which states that difference images may be generated through an image alignment process on page 2, lines 30-31);
identifying outlier tiles in the multiple tiles based on the one or more statistics generated for each of the multiple tiles (Sousa teaches that “the delta map will tend to indicate any variation between a pattern characteristic of a particular patch and a reference average or median pattern characteristic of the particular patch's die-equivalent patches from both reticles with optional exclusion of outlier patches” in para. [0080]; since there is an optional process of outlier patch exclusion, it is inherent that outlier patches are identified);
determining one or more defect detection parameters of a defect detection method for each of the difference image frames based on the identified outlier tiles (Sousa teaches that “defects may then be reported based on the delta map or a statistics map in operation 562”, wherein “the threshold can be based on the amount of variance from the average” as shown in para. [0092]; since it is optional that the outlier tiles may be included in the map, it is inherent that the threshold as taught by Sousa may at least in part be based on the outlier tiles; see FIG. 2 #206 wherein the thresholds (parameters) are used in regards to the difference image); and
detecting defects on the specimen by applying the defect detection method to the difference image frame(Sousa teaches using a threshold based on the delta map (the claimed defect detection method) to define defects in para. [0092]; see also FIG. 5B #562) wherein said generating, identifying, determining, and detecting are performed by a computer subsystem (Sousa teaches a computer system in para. [0141]-[0142] which is capable of performing the above limitations).
Sousa fails to teach multiple difference image frames generated for a job of images.
However, Patwary teaches multiple difference image frames generated for a job of images (Patwary teaches “one or more difference image frames of a sample are acquired” wherein “the one or more difference images may be acquired from any source known in the art including, but not limited to, the inspection sub-system 102” in para. [0080]; “the one or more difference image frames may include one or more difference image frames which are based on and/or generated from one or more target image frames and one or more reference image frames” in para. [0081]; here, “the controller 104 may be configured to cause the inspection sub-system 102 to acquire one or more target image frames 125 a - 125 h” as shown in para. [0046]. As a result, the “one or more” target image frames are interpreted as equivalent to the claimed job of images).
Sousa and Patwary are both considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa to incorporate the teachings of Patwary and include “multiple difference image frames generated for a job of images”. The motivation for doing so would have been that “by subtracting the estimated background structure of the reference image frame 145 c from the target image frame 125 c , a defect 137 within the target image frame 125 c may be clearly shown in the generated difference image frame 155 c . By removing the background structure (e.g., subtracting reference image frames 145 ), defects 137 within the target image frames 125 may be clearly revealed”, as suggested by Patwary in para. [0065]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa with Patwary to obtain the invention specified in claim 20.
Claims 3 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Sousa et al. (U.S. Publication No. 2018/0238816 A1), hereinafter Sousa, in view of Patwary et al. (U.S. Publication No. 2020/0244963 A1), hereinafter Patwary and Brauer et al. (U.S. Publication No. 2021/0097666 A1), hereinafter Brauer.
Regarding claim 3, Sousa and Patwary teach the system of claim 1,
wherein the one or more statistics comprise a mean for each of the multiple tiles, and wherein identifying the outlier tiles comprises generating a (Sousa teaches that “a delta map may be represented visually so that different delta values or ranges are shown in different visual ways, such as differently colored reticle patches, different bar graph heights, different graph values, or 3-dimensional representations, etc” in para. [0081]; “the delta map will tend to indicate any variation between a pattern characteristic of a particular patch and a reference average or median pattern characteristic of the particular patch's die-equivalent patches from both reticles with optional exclusion of outlier patches” in para. [0080]; see para. [0084] regarding the process of identifying outliers based on an average of the die-equivalent values).
Sousa and Patwary fail to specifically teach the above process in the context of a histogram.
However, Brauer teaches generating a histogram and identifying the outliers based on the histogram (Brauer teaches “a 1D defect detection method or algorithm may use a 1D histogram for outlier detection with the difference grey level on the x axis” in para. [0096]). It should be noted that, since Sousa already teaches identifying outlier tiles based on a mean value (see claim 3), Brauer is only included to teach determining outliers based on a histogram.
Sousa, Patwary, and Brauer are all considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa (as modified by Patwary) to incorporate the teachings of Brauer and include “generating a histogram and identifying the outliers based on the histogram”. The motivation for doing so would have been “to enhance sensitivity to DOIs, to lower nuisance rate, to improve within wafer and wafer-to-wafer recipe performance stability”, as suggested by Brauer in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa and Patwary with Brauer to obtain the invention specified in claim 3.
Regarding claim 10, Sousa and Patwary teach the system of claim 1,
wherein the one or more statistics comprise a standard deviation for each of the multiple tiles (Sousa teaches “the [delta] map can optionally or additionally be represented by a metric, such as the standard deviation” in para. [0081]; as shown in claim 1, the delta map represents delta values for each patch (tile) of the reticles), and wherein identifying the outlier tiles comprises generating a (Sousa teaches that “a delta map may be represented visually so that different delta values or ranges are shown in different visual ways, such as differently colored reticle patches, different bar graph heights, different graph values, or 3-dimensional representations, etc” in para. [0081]; Sousa additionally teaches “integrated intensity values that are more than a predefined number of standard deviations from the average of the die-equivalent values may be excluded from each reference value determination” in para. [0084]; these excluded values are interpreted as outliers).
Sousa and Patwary fail to specifically teach the above process in the context of a histogram.
However, Brauer teaches generating a histogram and identifying the outliers based on the histogram (Brauer teaches “a 1D defect detection method or algorithm may use a 1D histogram for outlier detection with the difference grey level on the x axis” in para. [0096]). It should be noted that, since Sousa already teaches identifying outlier tiles based on a standard deviation value (see claim 10), Brauer is only included to teach determining outliers based on a histogram.
Sousa, Patwary, and Brauer are all considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa (as modified by Patwary) to incorporate the teachings of Brauer and include “generating a histogram and identifying the outliers based on the histogram”. The motivation for doing so would have been “to enhance sensitivity to DOIs, to lower nuisance rate, to improve within wafer and wafer-to-wafer recipe performance stability”, as suggested by Brauer in para. [0035]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa and Patwary with Brauer to obtain the invention specified in claim 10.
Claims 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Sousa et al. (U.S. Publication No. 2018/0238816 A1), hereinafter Sousa, in view of Patwary et al. (U.S. Publication No. 2020/0244963 A1), hereinafter Patwary, Brauer et al. (U.S. Publication No. 2021/0097666 A1), hereinafter Brauer, and Zheng et al. (U.S. Publication No. 2022/0223481 A1), hereinafter Zheng.
Regarding claim 4, Sousa, Patwary, and Brauer teach the system of claim 3.
Sousa further teaches the outlier tiles (see claim 1).
Patwary further teaches the multiple difference image frames (see claim 1).
Sousa, Patwary, and Brauer fail to teach wherein the computer subsystem is further configured for designating a first of the difference image frames as a grossly defective image frame when a number of the outlier tiles in the first of the difference image frames is above a predetermined number.
However, Zheng teaches designating a first of the difference image frames as a grossly defective image frame when a number of the outliers in the first of the difference image frames is above a predetermined number (Zheng teaches “determining as an unqualified region the predetermined region where the number of the predetermined defects is greater than or equal to the set threshold” as shown in para. [0059]; here, a region (which is interpreted as equivalent to the claimed image frame) is determined to be unqualified based on a number of defects. This can be combined with the teaching of Sousa which includes the specific teaching of the defects being outlier tiles to teach the claimed limitation).
Sousa, Patwary, Brauer, Zheng are all considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa (as modified by Patwary and Brauer) to incorporate the teachings of Zheng and include “designating a first of the difference image frames as a grossly defective image frame when a number of the outliers in the first of the difference image frames is above a predetermined number”. The motivation for doing so would have been “to analyze the defects with the same defect location on the different wafers, which is used to guide the optimization of the wafer preparation process, so as to improve the quality of the wafer”, as suggested by Zheng in para. [0083]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa, Patwary, and Brauer with Zheng to obtain the invention specified in claim 4.
Regarding claim 11, Sousa, Patwary, and Brauer teach system of claim 10,
Sousa further teaches the outlier tiles (see claim 1).
Patwary further teaches the multiple difference image frames (see claim 1).
Sousa, Patwary, and Brauer fail to teach wherein the computer subsystem is further configured for designating a first of the difference image frames as a grossly defective image frame when a number of the outlier tiles in the first of the difference image frames is above a predetermined number.
However, Zheng teaches designating a first of the difference image frames as a grossly defective image frame when a number of the outliers in the first of the difference image frames is above a predetermined number (Zheng teaches “determining as an unqualified region the predetermined region where the number of the predetermined defects is greater than or equal to the set threshold” as shown in para. [0059]; here, a region (which is interpreted as equivalent to the claimed image frame) is determined to be unqualified based on a number of defects. This can be combined with the teaching of Sousa which includes the specific teaching of the defects being outlier tiles to teach the claimed limitation).
Sousa, Patwary, Brauer, Zheng are all considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa (as modified by Patwary and Brauer) to incorporate the teachings of Zheng and include “designating a first of the difference image frames as a grossly defective image frame when a number of the outliers in the first of the difference image frames is above a predetermined number”. The motivation for doing so would have been “to analyze the defects with the same defect location on the different wafers, which is used to guide the optimization of the wafer preparation process, so as to improve the quality of the wafer”, as suggested by Zheng in para. [0083]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa, Patwary, and Brauer with Zheng to obtain the invention specified in claim 11.
Claims 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Sousa et al. (U.S. Publication No. 2018/0238816 A1), hereinafter Sousa, in view of Patwary et al. (U.S. Publication No. 2020/0244963 A1), hereinafter Patwary, and Rakhshanfar et al. (U.S. Publication No. 2017/0178309 A1), hereinafter Rakhshanfar.
Regarding claim 13, Sousa and Patwary teach the system of claim 12,
Sousa further teaches that the delta map (which is optionally made up of all the non-outlier tiles as shown in para. [0084]) may be based on one or more noise factors in para. [0089] and [0091], and standard deviation of a noisiest of the non-outlier tiles in the job (Sousa teaches “delta intensity values that vary more than a certain number of standard deviations may also be defined as defects” in para. [0092]; see para. [0089] in which these delta values may be based on a noise factor).
Patwary further teaches multiple difference images as part of a job as shown in claim 1.
Sousa and Patwary fail to specifically teach “wherein the at least one of the one or more statistics comprises peak noise of all of the non-outlier tiles in the job”.
However, Rakhshanfar teaches wherein the at least one of the one or more statistics comprises peak noise of all of the non-outlier tiles in the job (Rakhshanfar teaches “the most homogeneous cluster is selected and the mean variance of patches of this cluster is considered as the noise variance peak of the input noisy signal” in para. [0073]; see also para. [0048] in which noise representative regions are ranked according to temporal differences between current and neighboring frames. See para. [0089] regarding the calculation of the peak noise level using statistics. Rakhshanfar further teaches processing multiple images of a job in para. [0057]).
Sousa, Patwary, and Rakhshanfar are all considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa (as modified by Patwary) to incorporate the teachings of Rakhshanfar and include “wherein the at least one of the one or more statistics comprises peak noise of all of the non-outlier tiles in the job”. The motivation for doing so would have been reject out of range clusters by defining a possible noise range and a proposed variance margin, as suggested by Rakhshanfar in para. [0145]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa and Patwary with Rakhshanfar to obtain the invention specified in claim 13.
Regarding claim 15, Sousa and Patwary teach the system of claim 14,
Sousa further teaches that the delta map (which is optionally made up of all the non-outlier tiles as shown in para. [0084]) may be based on one or more noise factors in para. [0089] and [0091], and standard deviation of a noisiest of the non-outlier tiles in the one of the difference image frames (Sousa teaches “delta intensity values that vary more than a certain number of standard deviations may also be defined as defects” in para. [0092]; see para. [0089] in which these delta values may be based on a noise factor).
Sousa and Patwary fail to specifically teach “wherein the at least one of the one or more statistics comprises peak noise of all of the non-outlier tiles in the one of the difference image frames”.
However, Rakhshanfar teaches wherein the at least one of the one or more statistics comprises peak noise of all of the non-outlier tiles in the one of the difference image frames (Rakhshanfar teaches “the most homogeneous cluster is selected and the mean variance of patches of this cluster is considered as the noise variance peak of the input noisy signal” in para. [0073]; see also para. [0048] in which noise representative regions are ranked according to temporal differences between current and neighboring frames. See para. [0089] regarding the calculation of the peak noise level using statistics).
Sousa, Patwary, and Rakhshanfar are all considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa (as modified by Patwary) to incorporate the teachings of Rakhshanfar and include “wherein the at least one of the one or more statistics comprises peak noise of all of the non-outlier tiles in the one of the difference image frames”. The motivation for doing so would have been reject out of range clusters by defining a possible noise range and a proposed variance margin, as suggested by Rakhshanfar in para. [0145]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa and Patwary with Rakhshanfar to obtain the invention specified in claim 15.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Sousa et al. (U.S. Publication No. 2018/0238816 A1), hereinafter Sousa, in view of Patwary et al. (U.S. Publication No. 2020/0244963 A1), hereinafter Patwary, and Tandia et al. (U.S. Publication No. 2020/0410660 A1), hereinafter Tandia.
Regarding claim 18, Sousa and Patwary teach the system of claim 1.
Sousa and Patwary fail to teach wherein each of the multiple tiles has a size of between 64 pixels by 64 pixels to 256 pixels by 256 pixels.
However, Tandia teaches wherein each of the multiple tiles has a size of between 64 pixels by 64 pixels to 256 pixels by 256 pixels (Tandia teaches that “the tile size and overlap of the data set of tile size images is based on results of the machine-learning model” in para. [0050], wherein the example given in para. [0051] defines the tiles as 200 by 200 pixels, which falls within the range outlined in the claim limitation).
Sousa, Patwary, and Tandia are all considered to be analogous to the claimed invention because they are in the same field of determining defects in semiconductors (see para. [0029] of Tandia). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Sousa (as modified by Patwary) to incorporate the teachings of Tandia and include “wherein each of the multiple tiles has a size of between 64 pixels by 64 pixels to 256 pixels by 256 pixels”. The motivation for doing so would have been to identify the optimal tile size, as suggested by Tandia in para. [0033] and FIG. 4. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Sousa and Patwary with Tandia to obtain the invention specified in claim 18.
Allowable Subject Matter
Claims 5-9 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter.
The best prior art of record is Sousa, Patwary, Brauer, Zheng, Rakhshanfar, and Tandia. Prior art applied alone or in combination with fails to anticipate or render obvious claims 5-9.
Claim 5
Regarding claim 5, Sousa, Patwary, and Brauer teach the system of claim 3.
Sousa further teaches standard deviation and range (See para. [0084]).
Brauer further teaches wherein the computer subsystem is further configured for designating the multiple tiles not identified as the outlier tiles based on the histogram (Brauer teaches identifying the outlier tiles as tiles which have potential defects based on a histogram in para. [0096] and [0147] (see also claim 3)).
However, neither Sousa, nor Patwary, nor Brauer, nor Zheng, nor Rakhshanfar, nor Tandia, nor the combination, teaches wherein the one or more statistics further comprise range and standard deviation for each of the remaining tiles in a first of the difference image frames, and wherein identifying the outlier tiles further comprises generating range and standard deviation histograms for the remaining tiles in the first of the difference image frames and identifying outlier remaining tiles in the first of the difference image frames based on the range and standard deviation histograms as additional outlier tiles.
Claims 6-9 include allowable subject matter by virtue of being dependent upon claim 5.
Conclusion
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/Kyla Guan-Ping Tiao Allen/
Examiner, Art Unit 2661
/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661