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 .
This communication is in response to Application No. 18/907,563 filed 10/06/2024. Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 07/20/2025 have been entered and considered. Initialed copies of the PTO-1449 by the examiner are attached.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in IN on 2024-01-04. It is noted, however, that applicant has not filed a certified copy of the IN202441000838 application as required by 37 CFR 1.55.
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.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claim(s) 1, 14 and 15 recite limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claims 1 and 14; recites the limitation, “imaging subsystem configured for …,”.
Claims 1 and 15; recites the limitation, “computer subsystem configured for …,”.
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.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim(s) 1, 14 and 15:
“imaging subsystem” (Fig. 1, #10. Paragraphs [0027-0028]- “One embodiment of such a system includes an imaging subsystem configured for generating images of a specimen. In general, the imaging subsystem includes at least an energy source and a detector. The energy source is configured to generate energy that is directed to a specimen. The detector is configured to detect energy from the specimen and to generate output responsive to the detected energy... In one embodiment, the imaging subsystem is a light-based imaging subsystem. For example, in the embodiment of the system shown in FIG. 1, imaging subsystem 10 includes an illumination subsystem configured to direct light to specimen 14. The illumination subsystem includes at least one light source, e.g., light source 16. The illumination subsystem is configured to direct the light to the specimen at one or more angles of incidence, which may include one or more oblique angles and/or one or more normal angles. As shown in FIG. 1, light from light source 16 is directed through optical element 18 and then lens 20 to beam splitter 21, which directs the light to specimen 14 at a normal angle of incidence.” thus, have sufficient structure or material wherein is any kind of energy source and a detector such as a light source and detector).
“computer subsystem” (Fig. 1, #36, #102, #124. Paragraph [0039]- “This computer subsystem (as well as other computer subsystems described herein) may also be referred to herein as computer system(s). Each of the computer subsystem(s) or system(s) described herein may take various forms, including a personal computer system, image computer, mainframe computer system, workstation, network appliance, Internet appliance, or other device. In general, the term “computer system” may be broadly defined to encompass any device having one or more processors, which executes instructions from a memory medium. The computer subsystem(s) or system(s) may also include any suitable processor known in the art such as a parallel processor. In addition, the computer subsystem(s) or system(s) may include a computer platform with high speed processing and software, either as a standalone or a networked tool.” thus, have sufficient structure or material wherein is any kind of computer system having one or more processors).
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.
Claim(s) 1-6, 8, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Mariyappan et al. (US 20220301133 A1, hereinafter referred to as “Mariyappan”) in view of Kyo et al. (WO 2021074944 A1, hereinafter referred to as “Kyo”).
Regarding claim 1, Mariyappan teaches a system configured to set up care areas for inspection of a specimen, comprising:
an imaging subsystem configured for generating images of a specimen (“The system includes a light source that generates a beam of light, a detector that receives the beam of light reflected from a wafer, and a processor in electronic communication with the detector” Mariyappan, [0017]); and a computer subsystem configured for (Mariyappan, [0017]):
acquiring an area of interest image for the specimen from one or more of the images generated by the imaging subsystem (“Generating care areas from an image is less complex than generating rule-based/pattern-based care areas. Care areas can be generated from rendered image by grouping pixels of similar grey levels in rendered image and/or in brightness or roughness images... For example, care areas or regions of interest can be generated on all pixels with less than 1000 grey level on a rendered image and from 100-400 grey level on a roughness image, though other values are possible” Mariyappan, [0039]);
segmenting the area of interest image based on intensity of pixels in the area of interest image thereby separating the area of interest image into pixels corresponding to one or more specimen structures in the area of interest image having an intensity different than other pixels in the area of interest image (“The rendered image is segmented based on a grey level of the rendered image at 102. A grey level of any pixel on the rendered image can be correlated to the pattern surrounding the pixel. For example, a pixel in a densely patterned region may be darker compared to a sparsely patterned region. Segmenting the grey levels can directly separate regions based on pattern density” Mariyappan, [0036]);
storing information for the one or more care areas for use in inspection of the specimen (“determine care areas based on the segmenting; and send instructions to perform defect inspection in the care areas” Mariyappan, [0017]; “The processor 214 and/or the electronic data storage unit 215 optionally may be in electronic communication with a wafer inspection tool, a wafer metrology tool, or a wafer review tool (not illustrated) to receive additional information or send instructions” Mariyappan, [0062]).
Mariyappan fail to explicitly teach detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures; determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges.
However, Kyo teaches detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures (“a pattern edge extraction method or the like using wavelet transform, the edge of the pattern may be found on the two-dimensional pixel array” Kyo, [0031]); determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges (“The extracted pattern shape of the first reference pattern and the coordinates (coordinates of a cut-out image to be described later) are registered in association with the information of the corresponding swab channel” Kyo, [0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Kyo of having detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures; determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges.
Wherein having Mariyappan’s system for segmentation of design care areas wherein detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures; determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Kyo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Kyo’s defect inspection allows for high accuracy of comparison when images are superimposed to detect defects. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Kyo et al. (WO 2021074944 A1), Paragraph [0003].
Regarding claim 2, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan fails to explicitly teach wherein acquiring the area of interest image comprises superimposing two or more of the images of the specimen generated by the imaging subsystem and corresponding to an area of interest on the specimen.
However, Kyo teaches wherein acquiring the area of interest image comprises superimposing two or more of the images of the specimen generated by the imaging subsystem and corresponding to an area of interest on the specimen (“detecting a defect by die to die comparison, when a defect size desired to be detected by miniaturization is reduced, when the inspection die image and the reference image are superimposed by die To die comparison” Kyo, [0027]; i.e., the area of interest (e.g., a defect area) is obtained by superimposing two images (e.g., die image and reference image)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Kyo of having wherein acquiring the area of interest image comprises superimposing two or more of the images of the specimen generated by the imaging subsystem and corresponding to an area of interest on the specimen.
Wherein having Mariyappan’s system for segmentation of design care areas wherein acquiring the area of interest image comprises superimposing two or more of the images of the specimen generated by the imaging subsystem and corresponding to an area of interest on the specimen.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Kyo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Kyo’s defect inspection allows for high accuracy of comparison when images are superimposed to detect defects. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Kyo et al. (WO 2021074944 A1), Paragraph [0003].
Regarding claim 3, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan further teaches wherein acquiring the area of interest image comprises merging two or more of the images of the specimen generated with different parameters of the imaging subsystem and corresponding to an area of interest on the specimen (“A reference image can be the same location from an adjacent die, generated by a linear combination of multiple dies” Mariyappan, [0045] wherein the “optical based subsystem 201 may be configured to direct the light to the specimen 202 at different angles of incidence at different times. For example, the optical based subsystem 201 may be configured to alter one or more characteristics of one or more elements of the illumination subsystem such that the light can be directed to the specimen 202 at an angle of incidence that is different than that shown in FIG. 7” Mariyappan, [0050]).
Regarding claim 4, Mariyappan in view of Kyo teach the system of claim 3, Mariyappan further teaches wherein the different parameters comprise different collection channels of the imaging subsystem (“The optical based subsystem 201 further includes one or more detection channels. At least one of the one or more detection channels includes a detector configured to detect light from the specimen 202 due to illumination of the specimen 202 by the subsystem and to generate output responsive to the detected light” Mariyappan, [0056]).
Regarding claim 5, Mariyappan in view of Kyo teach the system of claim 3, Mariyappan further teaches wherein the different parameters are selected to generate the two or more of the images of the specimen with different aspects (“The one or more detection channels may include any suitable detectors known in the art. For example, the detectors may include photo-multiplier tubes (PMTs), charge coupled devices (CCDs), time delay integration (TDI) cameras, and any other suitable detectors known in the art. The detectors may also include non-imaging detectors or imaging detectors. In this manner, if the detectors are non-imaging detectors, each of the detectors may be configured to detect certain characteristics of the scattered light such as intensity but may not be configured to detect such characteristics as a function of position within the imaging plane” Mariyappan, [0060]; See additionally Mariyappan at [0052] “ In another instance, the illumination subsystem may include only one light source (e.g., light source 203 shown in FIG. 7) and light from the light source may be separated into different optical paths (e.g., based on wavelength, polarization, etc.) by one or more optical elements (not shown) of the illumination subsystem. Light in each of the different optical paths may then be directed to the specimen 202. Multiple illumination channels may be configured to direct light to the specimen 202 at the same time or at different times (e.g., when different illumination channels are used to sequentially illuminate the specimen). In another instance, the same illumination channel may be configured to direct light to the specimen 202 with different characteristics at different times”).
Regarding claim 6, Mariyappan in view of Kyo teach the system of claim 3, Mariyappan further teaches wherein the different parameters comprise first and second parameters, wherein the two or more of the images generated with the different parameters comprise a first image generated with the first parameters and a second image generated with the second parameters, and wherein the first image is more responsive to a first portion of the one or more specimen structures than a second portion of the one or more specimen structures and the second image is responsive to the second portion of the one or more specimen structures (“The one or more detection channels may include any suitable detectors known in the art. For example, the detectors may include photo-multiplier tubes (PMTs), charge coupled devices (CCDs), time delay integration (TDI) cameras, and any other suitable detectors known in the art. The detectors may also include non-imaging detectors or imaging detectors. In this manner, if the detectors are non-imaging detectors, each of the detectors may be configured to detect certain characteristics of the scattered light such as intensity but may not be configured to detect such characteristics as a function of position within the imaging plane. As such, the output that is generated by each of the detectors included in each of the detection channels of the optical based subsystem may be signals or data, but not image signals or image data. In such instances, a processor such as processor 214 may be configured to generate images of the specimen 202 from the non-imaging output of the detectors. However, in other instances, the detectors may be configured as imaging detectors that are configured to generate imaging signals or image data. Therefore, the optical based subsystem may be configured to generate optical images or other optical based output described herein in a number of ways.” Mariyappan, [0060]).
Regarding claim 8, Mariyappan in view of Kyo teach the system of claim 3, Mariyappan further teaches wherein the different parameters are independent of parameters of the imaging subsystem used for the inspection of the specimen (“The model parameters can be iteratively changed by comparing the output of the model to an actual image from the inspection system and minimizing the difference, which iteratively improves the rendering” Mariyappan, [0033]).
Regarding claim 14, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan further teaches wherein the imaging subsystem is further configured for generating the images of the specimen by detecting light scattered from the specimen, and wherein the one or more specimen structures scatter the light more strongly than defects on the specimen (“As shown in FIG. 7, the two detection channels are configured to collect and detect light at different angles of collection. In some instances, both detection channels are configured to detect scattered light, and the detection channels are configured to detect tight that is scattered at different angles from the specimen 202. However, one or more of the detection channels may be configured to detect another type of light from the specimen 202 (e.g., reflected light)” Mariyappan, [0056]; “each of the detection channels included in the optical based subsystem 201 may be configured to detect scattered light. Therefore, the optical based subsystem 201 shown in FIG. 7 may be configured for dark field (DF) output generation for specimens 202. However, the optical based subsystem 201 may also or alternatively include detection channel(s) that are configured for bright field (BF) output generation for specimens 202. In other words, the optical based subsystem 201 may include at least one detection channel that is configured to detect light specularly reflected from the specimen 202” Mariyappan, [0059]).
Regarding claim 15, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan further teaches wherein the computer subsystem is further configured for performing the acquiring, segmenting, detecting, determining, and storing without information for a design for the specimen (“the illumination subsystem may include only one light source (e.g., light source 203 shown in FIG. 7) and light from the light source may be separated into different optical paths (e.g., based on wavelength, polarization, etc.) by one or more optical elements (not shown) of the illumination subsystem. Light in each of the different optical paths may then be directed to the specimen 202. Multiple illumination channels may be configured to direct light to the specimen 202 at the same time or at different times (e.g., when different illumination channels are used to sequentially illuminate the specimen)” Mariyappan, [0052]; i.e., the steps are performed using an illumination subsystem used to illuminate the specimen and capture images for detecting and segmenting; additionally, see Mariyappan [0038], [0039] “Care areas are determined based on the segmenting at 103... Care areas can be generated from rendered image by grouping pixels of similar grey levels in rendered image and/or in brightness or roughness images”).
Regarding claim 16, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan fails to explicitly teach wherein determining the one or more characteristics comprises identifying end points of the one or more specimen structures in the area of interest image based on the detected edges and determining the one or more characteristics based on the identified end points.
However, Kyo teaches wherein determining the one or more characteristics comprises identifying end points of the one or more specimen structures in the area of interest image based on the detected edges and determining the one or more characteristics based on the identified end points (“the edge of the pattern on the two-dimensional pixel array by a pattern edge extraction method using the wavelet transform or the like... The pattern shape and coordinates of the extracted first reference pattern (coordinates of the cutout image described later) are registered in association with the information of the corresponding swath channel” Kyo, [0031]-[0032]; Additionally, see Kyo [0034] Fig. 6A “In these enlarged views, the first reference pattern is shown as a pattern 502 present in the segmented image 601. Similarly, the pattern 402'is shown as a pattern 502'at a position offset from the center of the segmented image 602. The pattern indicated by the dotted line indicates the first reference pattern 502 registered in the recipe” i.e., the segmented structure contains coordinates, e.g., as end points, of a specimen).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Kyo of having wherein determining the one or more characteristics comprises identifying end points of the one or more specimen structures in the area of interest image based on the detected edges and determining the one or more characteristics based on the identified end points.
Wherein having Mariyappan’s system for segmentation of design care areas wherein determining the one or more characteristics comprises identifying end points of the one or more specimen structures in the area of interest image based on the detected edges and determining the one or more characteristics based on the identified end points.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Kyo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Kyo’s defect inspection allows for high accuracy of comparison when images are superimposed to detect defects. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Kyo et al. (WO 2021074944 A1), Paragraph [0003].
Regarding claim 17, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan further teaches wherein the imaging subsystem is a light-based imaging subsystem (“The system includes a light source that generates a beam of light, a detector that receives the beam of light reflected from a wafer” Mariyappan, [0017]).
Regarding claim 18, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan further teaches wherein the imaging subsystem is an electron-based imaging subsystem (“the embodiments disclosed herein can be used with an electron beam system (e.g., a scanning electron microscope), a focused ion beam (FIB) system, a helium ion microscopy (HIM) system, or a secondary ion mass spectroscopy (SIMS) system.” Mariyappan, [0073]).
Regarding claim 19, Mariyappan teaches a non-transitory computer-readable medium, storing program instructions executable on a computer system for performing a computer-implemented method for setting up care areas for inspection of a specimen, wherein the computer-implemented method comprises (“non-transitory computer-readable medium storing program instructions” Mariyappan, [0071]):
acquiring an area of interest image for a specimen from one or more images of the specimen generated by an imaging subsystem (“Generating care areas from an image is less complex than generating rule-based/pattern-based care areas. Care areas can be generated from rendered image by grouping pixels of similar grey levels in rendered image and/or in brightness or roughness images... For example, care areas or regions of interest can be generated on all pixels with less than 1000 grey level on a rendered image and from 100-400 grey level on a roughness image, though other values are possible” Mariyappan, [0039]);
segmenting the area of interest image based on intensity of pixels in the area of interest image thereby separating the area of interest image into pixels corresponding to one or more specimen structures in the area of interest image having an intensity different than other pixels in the area of interest image (“The rendered image is segmented based on a grey level of the rendered image at 102. A grey level of any pixel on the rendered image can be correlated to the pattern surrounding the pixel. For example, a pixel in a densely patterned region may be darker compared to a sparsely patterned region. Segmenting the grey levels can directly separate regions based on pattern density” Mariyappan, [0036]);and
storing information for the one or more care areas for use in inspection of the specimen (“determine care areas based on the segmenting; and send instructions to perform defect inspection in the care areas” Mariyappan, [0017]; “The processor 214 and/or the electronic data storage unit 215 optionally may be in electronic communication with a wafer inspection tool, a wafer metrology tool, or a wafer review tool (not illustrated) to receive additional information or send instructions” Mariyappan, [0062]).
However, Kyo teaches detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures (“a pattern edge extraction method or the like using wavelet transform, the edge of the pattern may be found on the two-dimensional pixel array” Kyo, [0031]); determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges (“The extracted pattern shape of the first reference pattern and the coordinates (coordinates of a cut-out image to be described later) are registered in association with the information of the corresponding swab channel” Kyo, [0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a non-transitory computer-readable medium, storing program instructions executable on a computer system for performing a computer-implemented method for setting up care areas for inspection of a specimen, with the teachings of Kyo of having detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures; determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges.
Wherein having Mariyappan’s system for segmentation of design care areas wherein detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures; determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Kyo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Kyo’s defect inspection allows for high accuracy of comparison when images are superimposed to detect defects. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Kyo et al. (WO 2021074944 A1), Paragraph [0003].
Regarding claim 20, Mariyappan teaches a computer-implemented method for setting up care areas for inspection of a specimen, comprising (“computer-implemented method” Mariyappan, [0071]):
acquiring an area of interest image for a specimen from one or more images of the specimen generated by an imaging subsystem (“Generating care areas from an image is less complex than generating rule-based/pattern-based care areas. Care areas can be generated from rendered image by grouping pixels of similar grey levels in rendered image and/or in brightness or roughness images... For example, care areas or regions of interest can be generated on all pixels with less than 1000 grey level on a rendered image and from 100-400 grey level on a roughness image, though other values are possible” Mariyappan, [0039]);
segmenting the area of interest image based on intensity of pixels in the area of interest image thereby separating the area of interest image into pixels corresponding to one or more specimen structures in the area of interest image having an intensity different than other pixels in the area of interest image (“The rendered image is segmented based on a grey level of the rendered image at 102. A grey level of any pixel on the rendered image can be correlated to the pattern surrounding the pixel. For example, a pixel in a densely patterned region may be darker compared to a sparsely patterned region. Segmenting the grey levels can directly separate regions based on pattern density” Mariyappan, [0036]); and
storing information for the one or more care areas for use in inspection of the specimen, wherein the acquiring, segmenting, detecting, determining, and storing steps are performed by a computer subsystem coupled to the imaging subsystem (“determine care areas based on the segmenting; and send instructions to perform defect inspection in the care areas” Mariyappan, [0017]; “The processor 214 and/or the electronic data storage unit 215 optionally may be in electronic communication with a wafer inspection tool, a wafer metrology tool, or a wafer review tool (not illustrated) to receive additional information or send instructions” Mariyappan, [0062]).
However, Kyo teaches detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures (“a pattern edge extraction method or the like using wavelet transform, the edge of the pattern may be found on the two-dimensional pixel array” Kyo, [0031]); determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges (“The extracted pattern shape of the first reference pattern and the coordinates (coordinates of a cut-out image to be described later) are registered in association with the information of the corresponding swab channel” Kyo, [0032]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a computer-implemented method for setting up care areas for inspection of a specimen, with the teachings of Kyo of having detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures; determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges.
Wherein having Mariyappan’s system for segmentation of design care areas wherein detecting edges of the one or more specimen structures in the area of interest image based on the pixels corresponding to the one or more specimen structures; determining one or more characteristics of one or more care areas in the images of the specimen based on the detected edges.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Kyo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Kyo’s defect inspection allows for high accuracy of comparison when images are superimposed to detect defects. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Kyo et al. (WO 2021074944 A1), Paragraph [0003].
Claim(s) 7 and 10-13 are rejected under 35 U.S.C. 103 as being unpatentable over Mariyappan et al. in view of Kyo et al. and in further view of Luo et al. (US 20160104600 A1, hereinafter referred to as “Luo”).
Regarding claim 7, Mariyappan in view of Kyo teach the system of claim 3, Mariyappan in view of Kyo fail to explicitly teach wherein said merging produces a merged image more similar to a design for the one or more specimen structures than either of the two or more of the images.
However, Luo teaches wherein said merging produces a merged image more similar to a design for the one or more specimen structures than either of the two or more of the images (“a set of rules to identify array structures based on common patterns of structures may be defined and encoded in software... During defect detection, given an image, the computer subsystem(s) may apply the predetermined rules to the output of the inspection subsystem. Since the array structures repeat, an averaged image of multiple repeats can be calculated to reduce the noise on the image” Luo, [0049]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Luo of having wherein said merging produces a merged image more similar to a design for the one or more specimen structures than either of the two or more of the images.
Wherein having Mariyappan’s system for segmentation of design care areas wherein said merging produces a merged image more similar to a design for the one or more specimen structures than either of the two or more of the images.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Luo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Luo’s defect inspection uses structural information to detect defects on semiconductor specimen which enhances the segmentation of the array areas in images. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Luo et al. (US 20160104600 A1), Paragraph [0006 and 0010].
Regarding claim 10, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan in view of Kyo fail to explicitly teach wherein said segmenting comprises projection based segmentation.
However, Luo teaches wherein said segmenting comprises projection based segmentation (“The computer subsystem(s) may then analyze the image intensities, projection, variance, and symmetry and determine which rules should be applied to identify array structures for this type of array areas on the wafer” Luo, [0049]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Luo of having wherein said segmenting comprises projection based segmentation.
Wherein having Mariyappan’s system for segmentation of design care areas wherein said segmenting comprises projection based segmentation.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Luo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Luo’s defect inspection uses structural information to detect defects on semiconductor specimen which enhances the segmentation of the array areas in images. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Luo et al. (US 20160104600 A1), Paragraph [0006 and 0010].
Regarding claim 11, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan in view of Kyo fail to explicitly teach wherein said segmenting comprises applying a segmentation method to the area of interest image, displaying results of the segmentation method to a user, receiving from the user a cutline threshold for said separating, and performing said separating with the cutline threshold.
However, Luo teaches wherein said segmenting comprises applying a segmentation method to the area of interest image, displaying results of the segmentation method to a user, receiving from the user a cutline threshold for said separating, and performing said separating with the cutline threshold (“a set of rules to identify array structures based on common patterns of structures may be defined and encoded in software. A user may input structure information based on the image of array structures. The computer subsystem(s) may then analyze the image intensities, projection, variance, and symmetry and determine which rules should be applied to identify array structures for this type of array areas on the wafer. These steps may be performed for one type of array structure and then repeated for any other types of array structures” Luo, [0049]; i.e., the structure information includes that of image intensity to function as a threshold).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Luo of having wherein said segmenting comprises applying a segmentation method to the area of interest image, displaying results of the segmentation method to a user, receiving from the user a cutline threshold for said separating, and performing said separating with the cutline threshold.
Wherein having Mariyappan’s system for segmentation of design care areas wherein said segmenting comprises applying a segmentation method to the area of interest image, displaying results of the segmentation method to a user, receiving from the user a cutline threshold for said separating, and performing said separating with the cutline threshold.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Luo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Luo’s defect inspection uses structural information to detect defects on semiconductor specimen which enhances the segmentation of the array areas in images. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Luo et al. (US 20160104600 A1), Paragraph [0006 and 0010].
Regarding claim 12, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan in view of Kyo fail to explicitly teach wherein detecting the edges comprises applying a binary mask to the segmented area of interest image to generate a binary image of the one or more specimen structures and detecting the edges of the one or more specimen structures in the binary image.
However, Luo teaches wherein detecting the edges comprises applying a binary mask to the segmented area of interest image to generate a binary image of the one or more specimen structures and detecting the edges of the one or more specimen structures in the binary image (“The structural information may be represented in a number of different ways. For example, the structural information can be transferred and represented in terms of gray levels or spatial relationship of gray levels or binary masks” Luo, [0050]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Luo of having wherein detecting the edges comprises applying a binary mask to the segmented area of interest image to generate a binary image of the one or more specimen structures and detecting the edges of the one or more specimen structures in the binary image.
Wherein having Mariyappan’s system for segmentation of design care areas wherein detecting the edges comprises applying a binary mask to the segmented area of interest image to generate a binary image of the one or more specimen structures and detecting the edges of the one or more specimen structures in the binary image.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Luo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Luo’s defect inspection uses structural information to detect defects on semiconductor specimen which enhances the segmentation of the array areas in images. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Luo et al. (US 20160104600 A1), Paragraph [0006 and 0010].
Regarding claim 13, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan in view of Kyo fail to explicitly teach wherein the one or more specimen structures are sources of systematic noise in the images of the specimen generated for the inspection of the specimen.
However, Luo teaches wherein the one or more specimen structures are sources of systematic noise in the images of the specimen generated for the inspection of the specimen (“noise statistics may be calculated on a region basis, i.e., a multi-structure basis. Therefore, unlike the currently used methods, the embodiments described herein allow defect detection sensitivity to adapt to noise for different structures separately. For each structure, segmentation can be used for further dividing images into meaningful areas. If the number of nuisance defects is relatively large on certain structures or segments, the user can set a lower sensitivity for those structures or segments to suppress nuisance defects.” Luo, [0063]; see additionally Luo, [0066] “applying the defect detection method(s) includes determining one or more characteristics of noise for the first segment based on only the output in the first segment and determining one or more parameters of the one or more defect detection methods based on the determined one or more characteristics of the noise. In one such example, for areas where noise is relatively high, lower sensitivities may be applied. On the other hand, where noise is relatively low, higher sensitivities may be applied. The sensitivity used for detecting defects in different structures having one or more different characteristics may be determined during setup of the inspection for the specimen”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Luo of having wherein the one or more specimen structures are sources of systematic noise in the images of the specimen generated for the inspection of the specimen.
Wherein having Mariyappan’s system for segmentation of design care areas wherein the one or more specimen structures are sources of systematic noise in the images of the specimen generated for the inspection of the specimen.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Luo are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Luo’s defect inspection uses structural information to detect defects on semiconductor specimen which enhances the segmentation of the array areas in images. Please see Mariyappan et al. (US 20220301133 A1), Paragraph [0012 and 0013] and Luo et al. (US 20160104600 A1), Paragraph [0006 and 0010].
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Mariyappan et al. in view of Kyo et al. and in further view of Zhang et al. (US 20130188859 A1, hereinafter referred to as “Zhang”).
Regarding claim 9, Mariyappan in view of Kyo teach the system of claim 1, Mariyappan in view of Kyo fail to explicitly teach wherein said segmenting comprises median based segmentation.
However, Zhang teaches wherein said segmenting comprises median based segmentation (“the entire range of median intensity values is from 0 to 255, one of the first segments may include median intensity values from 0 to 100 and another of the first segments may include median intensity values from 101 to 255. In this manner, one of the first segments corresponds to darker pixels in the image, and the other of the first segments corresponds to brighter pixels in the image” Zhang, [0024]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made to combine the teachings of Mariyappan of having a system configured to set up care areas for inspection of a specimen, with the teachings of Zhang of having wherein said segmenting comprises median based segmentation.
Wherein having Mariyappan’s system for segmentation of design care areas wherein said segmenting comprises median based segmentation.
The motivation behind the modification would have been to obtain a system for segmentation of design care areas that enhances the defects in wafers for inspection, since both Mariyappan and Zhang are defect inspection systems for detecting a defect in semiconductor wafers. Wherein Mariyappan system for segmentation of design care areas that improves segmentation where patterns have different noise based on surrounding patterns, while Zhang’s system for segmenting pixels on a wafer enhances segment boundaries that underlie physical structure pattern(s) on the wafer. Please see Mariyappan et al. (US 20220301133 A1), paragraph [0012 and 0013] and Zhang et al. (US 20130188859 A1), paragraph [0009 and 0010].
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Brankner et al. (US 20040121496 A1) – uses CAD coordinates to navigate through the reticle for the wafer in order to locate reticle defects corresponding to a detected wafer defect.
Hiroi et al. (US 20220301136 A1) – determine a defect candidate position even when design data cannot be obtained by setting a reference die or chip over a wafer and setting a reference pattern; correcting a position error from another die to be inspected using the reference pattern and performing defect detection.
Karlinsky et al. (US 20220067523 A1) – training a DNN for examination of semiconductor specimen related to detection and/or classification of defects during fabrication.
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/EMMANUEL SILVA-AVINA/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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