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 .
Information Disclosure Statement
All Information Disclosure Statements filed on or before 06/04/2026 have been considered.
Drawings
The drawings are objected to because the unlabeled rectangular box(es) shown in the drawings (namely figure 1) should be provided with descriptive text labels. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-13 are rejected under 35 U.S.C. 103 as being unpatentable over Pavani (US 20160123897 A1), Alumot (US 20090148033 A1), and Shabtay (US 20190079022 A1).
With respect to claim 1, Pavani teaches a system comprising a processor (figure 7 element 48) and memory circuitry (figure 7 elements 49 and 50) configured to:
obtain an image of a semiconductor specimen (figure 6 element 28) including:
one or more arrays, each including repetitive structural elements (figure 6 element 28); and
one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements (figure 6 element 28); and
Pavani additionally teaches using correlation to determine sub-areas of the image (“In some embodiments, a focused feature may be modeled and the model shape may be correlated with image of surface. Such a correlation operation creates correlation peaks at the position of features. Correlation peaks may then be distinguished from their background using an intensity threshold value. For each feature, a feature pixel region, comprising a predetermined number of pixels that are surrounding the detected feature pixels, is segmented for estimating feature properties.” Page 8 paragraph 0036 col 1 lines 16-24) and clustering the sub-areas into one or more clusters based on data informative of a distance between the repetitive structural elements in the array (“In block 30, feature pixels are compared with models of feature spread functions. … The match metric determines if the feature corresponding to feature pixels is similar to a previously known feature….Properties of features include information on position, size, shape, and material composition. The position of a feature is either two dimensional or a three dimensional position on the surface. The position is estimated by localizing the position of feature pixels. In some embodiments, position is estimated by interpolating feature pixels and its corresponding feature spread function model, and by shifting the feature spread function relative to the interpolated feature pixels. Each shift is followed by computing the difference between the shifted feature spread function and the interpolated feature pixels at each shift value. The position of shift value generating the least difference is estimated as the position of feature. The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 paragraph 0036 line 24 (col 1) – line 10 (col 2) ), using the one or more clusters to distinguish between one or more first areas of the image corresponding to the one or more arrays (“The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 paragraph 0036 col 2 lines 6-10) and one or more second areas of the image corresponding the one or more regions (“The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 paragraph 0036 col 2 lines 6-10), and outputting data informative of the one or more first areas of the image. (figure 6 element 31).
Pavani does not teach performing a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, generating a correlation matrix or memory being PMC.
Alumot teaches a system comprising a processor (“All the elements in the wafer handling and image acquisition subsystem for both phases are included within the broken-line box generally designated A in FIG. 2; all the elements of the image processor subsystem (both the algorithms and the hardware) for both phases are indicated by the broken-line block B;” Paragraph 0063) and memory circuitry (“…under software control (block 124) from the main controller (8, FIG. 2)…” paragraph 0211) configured to:
obtain an image of a semiconductor specimen (“FIG. 3 more particularly illustrates the wafer handling and image acquisition subsystem 5a (FIG. 2)” paragraph 0066) including:
one or more arrays, each including repetitive structural elements (“As shown in FIG. 9, the wafer W being inspected is formed with a plurality of integrated-circuit dies D1-Dn each including the same pattern. In the Phase I examination, the complete surface of the wafer is scanned by the laser beam 3…” paragraph 0082); and
one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements (“As shown in FIG. 9, the wafer W being inspected is formed with a plurality of integrated-circuit dies D1-Dn each including the same pattern. In the Phase I examination, the complete surface of the wafer is scanned by the laser beam 3…” paragraph 0082); and
wherein the memory is configured to, during run-time scanning of the semiconductor specimen:
perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix (“The Score Calculator 73 computes the score matrix of correlation between the inspected and reference images in all the possible shifts around the current pixel, up to the maximum allowed. It receives three inputs: (a) the inspected image, to define the area around which the correlation is checked; (b) the reference image, to define the range of possible matches within the maximum range of horizontal and vertical shifts; and (c) a control input, from Pixel Characterizer 72, allowing the choice of registration points on the basis of pixel type.” Paragraph 0110 and figure 14a).
Alumot is analogous art in the same field of endeavor as the claimed invention. Almont is directed towards semiconductor image processing (“FIG. 3 more particularly illustrates the wafer handling and image acquisition subsystem 5a (FIG. 2)” paragraph 0066). A person of ordinary skill in the art, before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Alumot with Pavani, by utilizing Alumot’s teachings of a correlation matrix in combination with its (Pavani’s) teaching of correlation corresponding image processing, with the expectation that doing so would lead to improvements in inspection speed and accuracy (“There is therefore an urgent need to inspect patterned semiconductor wafers at relatively high speeds and with a relatively low false alarm rate in order to permit inspection during or immediately after the fabrication of the wafer so as to quickly identify any process producing defects and thereby to enable immediate corrective action to be taken.” Paragraph 0005 and “An object of the present invention is to provide a novel method and apparatus having advantages in the above respects for inspecting the surface of articles for defects.” Paragraph 0006)
Shabtay teaches memory being PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
Shabtay is analogous art in the same field of endeavor as the claimed invention. Shabtay is directed towards examining semiconductors (“The presently disclosed subject matter relates to examining objects (e.g. wafers, reticles, etc.) and more particularly to detecting defects in objects by examining captured images of the objects.” Paragraph 0001). A person of ordinary skill in the art would have found it obvious to combine the teachings of Pavani, Alumot, and Shabtay by utilizing Shabtay’s teachings of PMC memory in place of the prior combined system’s memory with the expectation that doing so would lead to improved processing (“FPEI system 103 comprises a processor and memory circuitry (PMC) 104 operatively connected to a hardware-based input interface 105 and to a hardware-based output interface 106. PMC 104 is configured to provide processing necessary for operating FPEI system 103 as further detailed with reference to the following figures and comprises a processor (not shown separately) and a memory (not shown separately). The processor of PMC 104 can be configured to execute several functional modules in accordance with computer-readable instructions implemented on a non-transitory computer-readable memory comprised in PMC 104. Such functional modules are referred to hereinafter as comprised in PMC 104” paragraph 0034).
With respect to claim 2, Pavani, Alumot, and Shabtay teach the system of claim 1. Pavani teaches wherein the sub-areas of the image correspond to values of the correlation meeting an amplitude criterion (“In some embodiments, a focused feature may be modeled and the model shape may be correlated with image of surface. Such a correlation operation creates correlation peaks at the position of features. Correlation peaks may then be distinguished from their background using an intensity threshold value. For each feature, a feature pixel region, comprising a predetermined number of pixels that are surrounding the detected feature pixels, is segmented for estimating feature properties.” Page 8 paragraph 0036 col 1 lines 16-24), and Alumot discloses a correlation matrix (see figure 14a).
With respect to claim 3, Pavani, Alumot, and Shabtay teach the system of claim 1. Pavani further teaches clustering the sub-areas into one or more first clusters, based on data informative of a distance between the repetitive structural elements in the array along a first axis (“The position of a feature is either two dimensional or a three dimensional position on the surface. The position is estimated by localizing the position of feature pixels. In some embodiments, position is estimated by interpolating feature pixels and its corresponding feature spread function model, and by shifting the feature spread function relative to the interpolated feature pixels. Each shift is followed by computing the difference between the shifted feature spread function and the interpolated feature pixels at each shift value. The position of shift value generating the least difference is estimated as the position of feature.” Page 8 paragraph 0036 col 1 (starting at line 5 from bottom) – col 2 lines 1-6 and “The structural model of feature thus estimated has quantitative dimensions. In some embodiments, each point on the structural model has a three dimensional position coordinate associated with it. In other embodiments, each point on the structural model has a two dimensional position coordinate associated with it.” Page 6 col 1 lines 15-20),
cluster the sub-areas into one or more second clusters, based on data informative of a distance between the repetitive structural elements in the array along a second axis (“The position of a feature is either two dimensional or a three dimensional position on the surface. The position is estimated by localizing the position of feature pixels. In some embodiments, position is estimated by interpolating feature pixels and its corresponding feature spread function model, and by shifting the feature spread function relative to the interpolated feature pixels. Each shift is followed by computing the difference between the shifted feature spread function and the interpolated feature pixels at each shift value. The position of shift value generating the least difference is estimated as the position of feature.” Page 8 paragraph 0036 col 1 (starting at line 5 from bottom) – col 2 lines 1-6 and “The structural model of feature thus estimated has quantitative dimensions. In some embodiments, each point on the structural model has a three dimensional position coordinate associated with it. In other embodiments, each point on the structural model has a two dimensional position coordinate associated with it.” Page 6 col 1 lines 15-20 and “The position of shift value generating the least difference is estimated as the position of feature. The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 col 2 paragraph 0036 lines 5-10),
and use the first and second clusters to distinguish between the one or more first areas of the image corresponding to the one or more arrays and the one or more second areas of the image corresponding to the one or more regions (“The position of shift value generating the least difference is estimated as the position of feature. The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 col 2 paragraph 0036 lines 5-10).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 4, Pavani, Alumot, and Shabtay teaches the system of claim 1. Alumot further teaches wherein the one or more arrays are separated from the one or more regions by one or more borders (“Thus, the misalignment may be detected from the reflected light detector image by computing the cross-correlation between a rectangle of pixels in the inspected image, and the rectangle of pixels in the reference image in all possible misalignments. This information may be used where the score matrix computed in the alignment control circuit does not provide a significant indication of the correct misalignment” paragraph 0089), wherein the memory circuitry is further configured to estimate the one or more first areas of the image including only the at least one or more arrays up to the borders (“The Score Calculator 73 computes the score matrix of correlation between the inspected and reference images in all the possible shifts around the current pixel, up to the maximum allowed. It receives three inputs: (a) the inspected image, to define the area around which the correlation is checked; (b) the reference image, to define the range of possible matches within the maximum range of horizontal and vertical shifts; and (c) a control input, from Pixel Characterizer 72, allowing the choice of registration points on the basis of pixel type.” Paragraph 0110 and figure 14a).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 5, Pavani, Alumot, and Shabtay teaches the system of claim 1. Pavani further teaches wherein the memory circuitry is further configured to apply image processing to the reference image, wherein the image processing attenuates repetitive patterns of the reference image (“In some embodiments, the scattering of surface is modeled by computing a Fourier Transformation of the structural model of surface.” Paragraph 0029 and “In block 24, a filter is designed based on the scattering model of feature and scattering model of surface to achieve a predetermined filter performance metric. It is generally desired that scattered radiation from feature is maximized and scattered radiation from surface is minimized after filtering so as to maximize feature sensitivity.” Paragraph 0030).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 6, Pavani, Alumot, and Shabtay teaches the system of claim 1. Pavani further teaches wherein the memory circuitry is further configured, for each cluster, to:
determine a shape surrounding the one or more clusters (“In block 31, properties of features are estimated. Properties of features include information on position, size, shape, and material composition.” Page 8 col 1 starting at line 8 from the bottom), and
output the polygon as a first area of the image (figure 6 element 31).
Alumot teaches determining a polygon surrounding the one or more clusters (“Thus, the misalignment may be detected from the reflected light detector image by computing the cross-correlation between a rectangle of pixels in the inspected image, and the rectangle of pixels in the reference image in all possible misalignments. This information may be used where the score matrix computed in the alignment control circuit does not provide a significant indication of the correct misalignment” paragraph 0089 )
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 7, Pavani, Alumot, and Shabtay teach the system of claim 1. Pavani further teaches wherein the one or more clusters include only clusters for which a number of sub-areas meets a threshold (“Feature pixels may be classified from their background pixels using an intensity threshold value. To minimize false positives, threshold values are designed to be higher than background pixel values. The value of a threshold may be adaptively chosen depending on pixel intensities in local neighborhood. For example, threshold value in a region with high background is higher than the threshold value in a region with lower background. In some embodiments, a focused feature may be modeled and the model shape may be correlated with image of surface. Such a correlation operation creates correlation peaks at the position of features. Correlation peaks may then be distinguished from their background using an intensity threshold value.” Page 8 col 1 paragraph 0036 lines 9-21).
With respect to claim 8, Pavani, Alumot, and Shabtay teaches the system of claim 2. Pavani further teaches wherein the memory circuitry is further configured to obtain data informative of the amplitude criterion in a setup phase prior to run-time examination of the semiconductor specimen (“In block 27, imaging settings are configured. In some embodiments, the imaging module is set so that a focused image of surface is detected by the image sensor.” Paragraph 0036 and “In other embodiments, the distance between image sensor 5 and imaging module 4A is tuned to focus scattered radiation 3 on image sensor 5. A focused image maximizes intensity of image pixels of sensor 5.” Paragraph 0023).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 9, Pavani, Alumot, and Shabtay teaches the system of claim 1. Alumot further teaches wherein the memory is further configured to generate the corrected image (“The method used for generating these images can be further understood using FIG. 49” paragraph 0298) such that a position of the sub-areas in the corrected image and data informative of an expected position of the repetitive structural elements in the array meet a proximity criterion (“Thus, as shown in the flow chart of FIG. 49, if the feature is determined to be a corner, the system computes the actual location (x', y') as shown in block 432; then computes the intensity I(k,t,D) for each detector D.sub.1-D.sub.8 (block 434); and then assigns the correct intensity in the right location for each detector (block 436)” paragraph 0299 and “On the other hand, if the feature is determined not to be a corner (i.e., a curve), a check is made to determine the kind of curve. Thus, if "k" is not a straight line as shown in FIG. 42 (block 436), a computation is made of the intensity (block 440), and of the edge points of the segment (block 442); and then the correct intensity is assigned to the correct location (block 444). On the other hand, if the feature is determined to be a curve (block 438), a computation is made of the intensity (block 446), and then the correct intensity is assigned in the correct location for each detector (block 448)” paragraph 0300).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 10, Pavani, Alumot, and Shabtay teaches the system of claim 1. Alumot further teaches wherein the memory circuitry is further configured to:
perform a correlation analysis between pixel intensity of the one or more first areas of the image and pixel intensity of a second reference image informative of at least one of the repetitive structural elements (“According to a further feature of the invention, the first examining phase is effected by generating a first flow of N streams of data representing the pixels of different images of the inspected pattern unit; generating a second flow of N streams of data representing the pixels of different images of the reference pattern unit; and comparing the data of the first flow with the data of the second flow to provide an indication of the suspected locations of the inspected pattern unit having a high probability of a defect.” Paragraph 0014 and “The correlation matrices computed for different registration points are summed, and the minimal value in the matrix corresponds to the correct misalignment.” Paragraph 121), to obtain a second correlation matrix (see figure 14a),
determine given sub-areas of the one or more first areas of the image corresponding to values of the second correlation matrix meeting an amplitude criterion (“In some embodiments, a focused feature may be modeled and the model shape may be correlated with image of surface. Such a correlation operation creates correlation peaks at the position of features. Correlation peaks may then be distinguished from their background using an intensity threshold value. For each feature, a feature pixel region, comprising a predetermined number of pixels that are surrounding the detected feature pixels, is segmented for estimating feature properties.” Page 8 paragraph 0036 col 1 lines 16-24),
determine a map of deformation between the one or more first areas of the image and the array (see figure 4 score matrix and alignment process), based at least on a position of the given sub-areas in the one or more first areas of the image and data informative of an expected position of the repetitive structural elements in the array (“The Score Calculator 73, as described earlier with reference to FIG. 14, computes the score matrix of correlation between the inspected and reference images in all the possible shifts around the current pixel, up to the maximum allowed (plus or minus vertical and horizontal ranges). This unit includes the following circuits: delays 73a, 73b, to correct the timing of the arrival of the inspected and reference images, respectively, to that of the arrival of the Registration Point flags from the pixel characterizer 72; Neighborhood Normalizers 73c, 73d, to normalize the pixels in the neighborhood of the current pixel; Absolute Difference Calculator 73e, which finds the absolute difference between the inspected image in the vicinity of the current pixel as against all the possible matches in the reference image within the maximum range of shifts in the vertical and horizontal axes, and computes the score matrix for these matches; and Score Matrix accumulator 73f which sums and stores all the score matrices which are accumulated during the scanning of a number of successive rows, before transmitting them to the Alignment Computer 62 (FIG. 12) for computation of the best match.” Paragraph 122), and
generate a corrected image based on the map of deformation (“The spanner uses the feature data and the model data in order to generate eight high-resolution scattering images.” Paragraph 0297 and “The method used for generating these images can be further understood using FIG. 49.” Paragraph 0298 ).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 11, Pavani, Alumot, and Shabtay teach the system of claim 10. Pavani teaches wherein the memory circuitry is further configured to obtain the reference image informative of at least one of the repetitive structural elements and to select only a subset of the reference image as the second reference image (“In block 17, images of feature are acquired. Each image of feature comprises information from multiple points of the feature. The images may be captured using a variety of techniques including scanning electron microscope, atomic force microscope, near field optical microscope, and optical microscope. In some embodiments, images may be captured with multiple illumination angles. In other embodiments, images may be captures with multiple views. In block 19, a structural model for feature is generated by extracting predetermined properties from acquired images of feature.” Page 3 paragraph 0028 lines 7-16).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 12, Pavani, Alumot, and Shabtay teach the system of claim 10. Pavani further teaches wherein the memory circuitry is further configured to:
determine deformation DFcentral between a position of the given sub-areas in the one or more first areas of the image and data informative of an expected position of the repetitive structural elements in the array (“The match metric determines if the feature corresponding to feature pixels is similar to a previously known feature. In some embodiments, a variety of scaled, rotated, and transformed feature spread functions are used for comparison. In some embodiments, previously known features used for calculating feature spread function comprises defects, including: particles, process induced defect, scratch, residue, crystal originated pit, and bumps.” Page 8 col 1 lines 35-42), and
determine a map of deformation between the one or more first areas of the image and the array of the semiconductor specimen (“In some embodiments, the match metric is computed by calculating the difference between feature pixels and a feature spread function model.” Page 8 col. 1 starting at line 23 from the bottom), based on an interpolation method applied at least to DFcentral (“The position is estimated by localizing the position of feature pixels. In some embodiments, position is estimated by interpolating feature pixels and its corresponding feature spread function model, and by shifting the feature spread function relative to the interpolated feature pixels. Each shift is followed by computing the difference between the shifted feature spread function and the interpolated feature pixels at each shift value. The position of shift value generating the least difference is estimated as the position of feature.” Page 8 col 1 starting at line 4 from the bottom to col 2 lines 1-6).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
With respect to claim 13, Pavani, Alumot, and Shabtay teach the system of claim 10. Alumot teaches wherein the memory circuitry is further configured to generate the corrected image (“The method used for generating these images can be further understood using FIG. 49” paragraph 0298) such that a position of the given sub-areas in the corrected image and data informative of an expected position of the repetitive structural elements in the array meet a proximity criterion (“Thus, as shown in the flow chart of FIG. 49, if the feature is determined to be a corner, the system computes the actual location (x', y') as shown in block 432; then computes the intensity I(k,t,D) for each detector D.sub.1-D.sub.8 (block 434); and then assigns the correct intensity in the right location for each detector (block 436)” paragraph 0299 and “On the other hand, if the feature is determined not to be a corner (i.e., a curve), a check is made to determine the kind of curve. Thus, if "k" is not a straight line as shown in FIG. 42 (block 436), a computation is made of the intensity (block 440), and of the edge points of the segment (block 442); and then the correct intensity is assigned to the correct location (block 444). On the other hand, if the feature is determined to be a curve (block 438), a computation is made of the intensity (block 446), and then the correct intensity is assigned in the correct location for each detector (block 448)” paragraph 0300).
Shabtay teaches the PMC (“One aspect of the disclosed subject matter relates to an examination system comprising: a defect detection system comprising a processing and memory circuitry (PMC) and configured to receive inspection data comprising at least one inspection image informative of potential defects of an object” paragraph 0011).
Claims 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Pavani in view of Alumot.
With respect to claim 14, Pavani teaches a non-transitory computer readable medium tangibly embodying a program of instructions (figure 7 elements 49 and 50) that, when executed by one or more computers (figure 7 element 47), cause the one or more computers to perform:
obtaining an image of a semiconductor specimen (figure 6 element 28) including:
one or more arrays, each including repetitive structural elements (figure 6 element 28); and
one or more regions, each region at least partially surrounding a corresponding
array and including features different from the repetitive structural elements (figure 6 element 28); and
using the correlation to determine sub-areas of the image (“In some embodiments, a focused feature may be modeled and the model shape may be correlated with image of surface. Such a correlation operation creates correlation peaks at the position of features. Correlation peaks may then be distinguished from their background using an intensity threshold value. For each feature, a feature pixel region, comprising a predetermined number of pixels that are surrounding the detected feature pixels, is segmented for estimating feature properties.” Page 8 paragraph 0036 col 1 lines 16-24),
clustering the sub-areas into one or more clusters, based on data informative of a distance between the repetitive structural elements in the array (“In block 30, feature pixels are compared with models of feature spread functions. … The match metric determines if the feature corresponding to feature pixels is similar to a previously known feature….Properties of features include information on position, size, shape, and material composition. The position of a feature is either two dimensional or a three dimensional position on the surface. The position is estimated by localizing the position of feature pixels. In some embodiments, position is estimated by interpolating feature pixels and its corresponding feature spread function model, and by shifting the feature spread function relative to the interpolated feature pixels. Each shift is followed by computing the difference between the shifted feature spread function and the interpolated feature pixels at each shift value. The position of shift value generating the least difference is estimated as the position of feature. The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 paragraph 0036 line 24 (col 1) – line 10 (col 2) ),
using the one or more clusters to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions (“The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 paragraph 0036 col 2 lines 6-10) and one or more second areas of the image corresponding the one or more regions (“The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 paragraph 0036 col 2 lines 6-10), and outputting data informative of the one or more first areas of the image (figure 6 element 31).
Pavani does not teach performing a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, generating a correlation matrix.
Alumot teaches a non-transitory computer readable medium tangibly embodying a program of instructions (“…under software control (block 124) from the main controller (8, FIG. 2)…” paragraph 0211) configured to cause one or more computers to:
obtain an image of a semiconductor specimen (“FIG. 3 more particularly illustrates the wafer handling and image acquisition subsystem 5a (FIG. 2)” paragraph 0066) including:
one or more arrays, each including repetitive structural elements (“As shown in FIG. 9, the wafer W being inspected is formed with a plurality of integrated-circuit dies D1-Dn each including the same pattern. In the Phase I examination, the complete surface of the wafer is scanned by the laser beam 3…” paragraph 0082); and
one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements (“As shown in FIG. 9, the wafer W being inspected is formed with a plurality of integrated-circuit dies D1-Dn each including the same pattern. In the Phase I examination, the complete surface of the wafer is scanned by the laser beam 3…” paragraph 0082); and
perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix (“The Score Calculator 73 computes the score matrix of correlation between the inspected and reference images in all the possible shifts around the current pixel, up to the maximum allowed. It receives three inputs: (a) the inspected image, to define the area around which the correlation is checked; (b) the reference image, to define the range of possible matches within the maximum range of horizontal and vertical shifts; and (c) a control input, from Pixel Characterizer 72, allowing the choice of registration points on the basis of pixel type.” Paragraph 0110 and figure 14a).
Alumot is analogous art in the same field of endeavor as the claimed invention. Almont is directed towards semiconductor image processing (“FIG. 3 more particularly illustrates the wafer handling and image acquisition subsystem 5a (FIG. 2)” paragraph 0066). A person of ordinary skill in the art, before the effective filing date of the claimed invention would have found it obvious to combine the teachings of Alumot with Pavani, by utilizing Alumot’s teachings of a correlation matrix in combination with its (Pavani’s) teaching of correlation corresponding image processing, with the expectation that doing so would lead to improvements in inspection speed and accuracy (“There is therefore an urgent need to inspect patterned semiconductor wafers at relatively high speeds and with a relatively low false alarm rate in order to permit inspection during or immediately after the fabrication of the wafer so as to quickly identify any process producing defects and thereby to enable immediate corrective action to be taken.” Paragraph 0005 and “An object of the present invention is to provide a novel method and apparatus having advantages in the above respects for inspecting the surface of articles for defects.” Paragraph 0006).
With respect to claim 15, Pavani and Alumot teach the non-transitory computer readable medium of claim 14. Pavati further teaches clustering the sub-areas (“In block 30, feature pixels are compared with models of feature spread functions. … The match metric determines if the feature corresponding to feature pixels is similar to a previously known feature….Properties of features include information on position, size, shape, and material composition. The position of a feature is either two dimensional or a three dimensional position on the surface. The position is estimated by localizing the position of feature pixels. In some embodiments, position is estimated by interpolating feature pixels and its corresponding feature spread function model, and by shifting the feature spread function relative to the interpolated feature pixels. Each shift is followed by computing the difference between the shifted feature spread function and the interpolated feature pixels at each shift value. The position of shift value generating the least difference is estimated as the position of feature. The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 paragraph 0036 line 24 (col 1) – line 10 (col 2) ), using the one or more clusters (“The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 paragraph 0036 col 2 lines 6-10) and outputting the data (figure 6 element 31).
Alumot further teaches wherein performing the correlation analysis is performed during run-time scanning of the semiconductor specimen (“The Score Calculator 73 computes the score matrix of correlation between the inspected and reference images in all the possible shifts around the current pixel, up to the maximum allowed. It receives three inputs: (a) the inspected image, to define the area around which the correlation is checked; (b) the reference image, to define the range of possible matches within the maximum range of horizontal and vertical shifts; and (c) a control input, from Pixel Characterizer 72, allowing the choice of registration points on the basis of pixel type.” Paragraph 0110 and figure 14a).
With respect to claim 16, Pavani and Alumot teach the non-transitory computer readable medium of claim 14. Pavani further teaches wherein the sub-areas of the image correspond to values of the correlation matrix meeting an amplitude criterion (“In some embodiments, a focused feature may be modeled and the model shape may be correlated with image of surface. Such a correlation operation creates correlation peaks at the position of features. Correlation peaks may then be distinguished from their background using an intensity threshold value. For each feature, a feature pixel region, comprising a predetermined number of pixels that are surrounding the detected feature pixels, is segmented for estimating feature properties.” Page 8 paragraph 0036 col 1 lines 16-24), and Alumot discloses a correlation matrix (see figure 14a).
With respect to claim 17, Pavani and Alumot teach the non-transitory computer readable medium of claim 14. Pavani further teaches it comprising instructions that, when executed by the one or more computers, cause the one or more computers (figure 7 element 47) to perform:
clustering the sub-areas into one or more first clusters, based on data informative of a distance between the repetitive structural elements in the array along a first axis (“The position of a feature is either two dimensional or a three dimensional position on the surface. The position is estimated by localizing the position of feature pixels. In some embodiments, position is estimated by interpolating feature pixels and its corresponding feature spread function model, and by shifting the feature spread function relative to the interpolated feature pixels. Each shift is followed by computing the difference between the shifted feature spread function and the interpolated feature pixels at each shift value. The position of shift value generating the least difference is estimated as the position of feature.” Page 8 paragraph 0036 col 1 (starting at line 5 from bottom) – col 2 lines 1-6 and “The structural model of feature thus estimated has quantitative dimensions. In some embodiments, each point on the structural model has a three dimensional position coordinate associated with it. In other embodiments, each point on the structural model has a two dimensional position coordinate associated with it.” Page 6 col 1 lines 15-20),
clustering the sub-areas into one or more second clusters, based on data informative of a distance between the repetitive structural elements in the array along a second axis (“The position of a feature is either two dimensional or a three dimensional position on the surface. The position is estimated by localizing the position of feature pixels. In some embodiments, position is estimated by interpolating feature pixels and its corresponding feature spread function model, and by shifting the feature spread function relative to the interpolated feature pixels. Each shift is followed by computing the difference between the shifted feature spread function and the interpolated feature pixels at each shift value. The position of shift value generating the least difference is estimated as the position of feature.” Page 8 paragraph 0036 col 1 (starting at line 5 from bottom) – col 2 lines 1-6 and “The structural model of feature thus estimated has quantitative dimensions. In some embodiments, each point on the structural model has a three dimensional position coordinate associated with it. In other embodiments, each point on the structural model has a two dimensional position coordinate associated with it.” Page 6 col 1 lines 15-20 and “The position of shift value generating the least difference is estimated as the position of feature. The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 col 2 paragraph 0036 lines 5-10), and using the first and second clusters to distinguish between the one or more first areas of the image corresponding to the one or more arrays and the one or more second areas of the image corresponding to the one or more regions (“The position of shift value generating the least difference is estimated as the position of feature. The size and shape of feature is estimated as the size and shape of the feature spread function model that produces the closest match (as determined by least difference or strongest correlation peak) to the feature pixels.” Page 8 col 2 paragraph 0036 lines 5-10).
With respect to claim 18, Pavani and Alumot teach the non-transitory computer readable medium of claim 14. Alumot further teaches wherein the one or more arrays are separated from the one or more regions by one or more borders (“Thus, the misalignment may be detected from the reflected light detector image by computing the cross-correlation between a rectangle of pixels in the inspected image, and the rectangle of pixels in the reference image in all possible misalignments. This information may be used where the score matrix computed in the alignment control circuit does not provide a significant indication of the correct misalignment” paragraph 0089), wherein the non-transitory computer readable medium comprises instructions that, when executed by the one or more computers, cause the one or more computers to estimate the one or more first areas of the image including only the at least one or more arrays up to the borders (“The Score Calculator 73 computes the score matrix of correlation between the inspected and reference images in all the possible shifts around the current pixel, up to the maximum allowed. It receives three inputs: (a) the inspected image, to define the area around which the correlation is checked; (b) the reference image, to define the range of possible matches within the maximum range of horizontal and vertical shifts; and (c) a control input, from Pixel Characterizer 72, allowing the choice of registration points on the basis of pixel type.” Paragraph 0110 and figure 14a).
With respect to claim 19, Pavani and Alumot teach the non-transitory computer readable medium of claim 14. Pavani further teaches it comprising instructions that, when executed by the one or more computers, cause the one or more computers (figure 7 element 47) to apply image processing to the reference image, wherein the image processing attenuates repetitive patterns of the reference image (“In some embodiments, the scattering of surface is modeled by computing a Fourier Transformation of the structural model of surface.” Paragraph 0029 and “In block 24, a filter is designed based on the scattering model of feature and scattering model of surface to achieve a predetermined filter performance metric. It is generally desired that scattered radiation from feature is maximized and scattered radiation from surface is minimized after filtering so as to maximize feature sensitivity.” Paragraph 0030).
With respect to claim 20, Pavani and Alumot teach all limitations in consideration of claim 14, due to the substantial similarities of claim 14 and claim 20 with claim 20 being directed to the method implemented by the computer of claim 14.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Mai (US 20040057633 A1) discloses performing image processing operations using a correlation matrix and reference image, affecting the pixel intensity with regards to a comparison operation
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/REBECCA COLETTE WILLIAMS/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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