DETAILED ACTION
This Office action is in response to the amendment and remarks filed on January 27th, 2026. Claims 1-20 are pending.
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 .
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 7 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites “wherein the comparing step is used in semiconductor fabrication recess analysis.” It is unclear what analysis is being claimed, because no analysis process steps are claimed beyond those in the parent claims, and no form of recess analysis is delineated.
Claim Rejections - 35 USC § 103
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 and 8-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2008/0144899 (Varma et al.) in view of US 2002/0047853 (Bartell).
Regarding claim 1, Varma et al. discloses a metrology method comprising the steps of:
using atomic force microscopy (AFM) data of a sample having an array of periodic features to generate a sample image having feature pixels and background pixels (fig. 4, step 110, wherein ‘Moreover, the present teachings may also be used with any image where it is necessary to determine coordinate locations of periodic features, such as DNA microarrays based on fluorescence, atomic force microscopy ("AFM"), and the like.’ P 47);
calculating a periodicity of the features (fig. 4, step 120);
identifying peaks in the periodicity to determine a feature period and a lattice angle (fig. 4, step 125);
constructing a lattice mask template using the feature period and the lattice angle (fig. 4, step 130);
overlaying the image with the lattice mask template (fig. 4, step 135);
performing an alignment calculation (fig. 4, step 140);
applying an offset of the lattice mask template to the image (fig. 4, step 140, wherein ‘The actual locations of the spots in the image are then obtained by adding the shift obtained in step 135 to the spot locations recorded when generating the template (step 140).’ P 44).
Varma et al. does not disclose determining a cost, recalculating the cost after applying the offset, or repeating the applying and recalculating steps. Varma performing alignment based on the brightest autocorrelation peak alone. Bartell discloses a method of aligning a periodic template with a periodic image where an initial alignment calculation is done to determine a cost, then multiple possible offsets are tried and cost recalculated for each to determine a final alignment (‘A combined-rank-score generator combines, for each of the initial and additional grid positions, members of sets of rank scores corresponding to the grid position in order to generate a combined rank score for the grid position. A grid alignment adjuster adjusts the alignment of the grid based on a comparison among the combined rank scores.’ Abstract, where rank scores are scores of a measure of contrast as shown by “The scheme described with respect to the present implementation is generally advantageous because the ranking indicates the grid position for each grid element that provides the greatest degree of contrast.”). It would have been obvious to a person having ordinary skill in the art at the time the application was filed to modify the method of Varma et al. to include the cost calculation and adjustment steps of Bartell to provide a more accurate alignment than can be consistently achieved with single point alignment.
Regarding claim 2, Varma et al. in view of Bartell discloses the method of claim 1, wherein the performing step includes at least one of a) calculating a standard deviation of the background pixels and setting the standard deviation as the cost value, and b) calculating a median of the background pixels and the feature pixels (“may include determining a median of intensities of three or more pixels, or every pixel, within each of the one or more grid elements in the first and second element groups.” P 12), and setting the difference between the medians as a cost value (Bartell, ‘(d) analyzing the data to determine a measure of contrast; and (e) re-associating the data with a second set of locations based, at least in part, on the measure of contrast.’ P 17, obvious to substitute difference of medians for the ranking based on medians alone because this is a direct measure of contrast, unlike intensity (measured as median or mean) which is an indirect measure of contrast, and the purpose is to maximize contrast).
Regarding claim 3, Varma et al. in view of Bartell discloses the method of claim 2, further comprising determining the offset of the lattice that establishes a minimum cost value if the standard deviation is calculated, and the offset of the lattice that establishes a maximum cost value if the difference between medians is calculated (Bartell, ‘The scheme described with respect to the present implementation is generally advantageous because the ranking indicates the grid position for each grid element that provides the greatest degree of contrast.’ P 76).
Regarding claim 4, Varma et al. in view of Bartell discloses the method of claim 1, further comprising extracting data with respect to the features after applying the alignment (‘At this stage, the coordinates of the array elements are well determined and the pixels can be extracted from each spot for further calculation (see FIG. 7).’ P 45).
Regarding claim 5, Varma et al. in view of Bartell discloses the method of claim 4, wherein the data corresponds to at least one of feature characteristic including height, depth, shape, uniformity, variance and slope (AFM images show height or depth).
Regarding claim 6, Varma et al. in view of Bartell discloses the claimed invention except for comparing the at least one feature characteristic to a known model to determine feature quality. Comparing feature characteristics to known models is common in the art. It would have been obvious to a person having ordinary skill in the art at the time the application was filed to modify the method of Varma et al. in view of Bartell to include such a step to determine the presence or absence of the desired test subject.
Regarding claim 8, Varma et al. in view of Bartell discloses the method of claim 1, wherein the features are 2D-periodic features and identifying peaks in the periodicity step begins at a center of the sample image and continues radially outwardly (‘To achieve this, the distance of the pixel with the maximum pixel value from the center of the cross-correlation image represents the shift between the generated template and the actual spot locations.’).
Regarding claim 9, Varma et al. in view of Bartell discloses the claimed method except for iterating over 2D model types including at least two of square, rectangular, hexagonal, and oblique; and selecting the periodicity of the lattice type that produces the smallest deviation between the model lattice type and the acquired data. However, Varma discloses that the 2D model can have different lattice types (‘While the generated template can also have any shape and/or size depending on the number of elements contained within the well, an exemplary 8X8 grid is shown in FIG. 6.’ P 43). It would have been obvious to a person having ordinary skill in the art at the time the application was filed to include a step of iterating over multiple array types so that data could be analyzed even when the periodic array type was unknown.
Regarding claim 10, Varma et al. in view of Bartell discloses the method of claim 1, wherein the calculating the periodicity step is performed using a Fast Fourier Transform (FFT) algorithm (‘determining grid spacing and rotational characteristics of the image by analyzing the detected coordinate locations of the frequency spectrum peaks with a Fourier transform analysis;’ abstract, while it is not specified whether the fast or discrete Fourier transform is used, it would have been obvious to a person having ordinary skill in the art to use the fast version to save computational resources).
Regarding claim 11, Varma et al. in view of Bartell discloses the claimed invention except for the use of a hexagonal lattice mask template. Hexagonal lattices are well known in the art and it would have been obvious to a person having ordinary skill in the art the time the application was filed to use a hexagonal template if the data being analyzed exhibited hexagonal spacing.
Regarding claim 12, Varma et al. in view of Bartell discloses the claimed invention except for applying an adaptive flattening algorithm to the sample image. Adaptive flattening algorithms are well known in the art and it would have been obvious to a person having ordinary skill in the art at the time the application was filed to modify the method of Varma et al. in view of Bartell to include an adaptive flattening step to remove background curvature.
Regarding claim 13, Varma et al. discloses a metrology method comprising the steps of:
Generating an image of a sample using atomic force microscopy (AFM) data (fig. 4, step 110, wherein ‘Moreover, the present teachings may also be used with any image where it is necessary to determine coordinate locations of periodic features, such as DNA microarrays based on fluorescence, atomic force microscopy ("AFM"), and the like.’ P 47);
calculating a periodicity of features of the image (fig. 4, step 120);
searching for at least one peak in the periodicity (fig. 4, step 120);
obtaining a feature period and a lattice angle (fig. 4, step 125);
constructing a lattice mask template using the feature period and the lattice angle (fig. 4, step 130);
overlaying the image with the lattice mask template (fig. 4, step 135);
performing an alignment calculation (fig. 4, step 140);
applying an offset of the lattice mask template to the image (fig. 4, step 140, wherein ‘The actual locations of the spots in the image are then obtained by adding the shift obtained in step 135 to the spot locations recorded when generating the template (step 140).’ P 44).
Varma et al. does not disclose determining a cost, recalculating the cost after applying the offset, or repeating the applying and recalculating steps. Varma performing alignment based on the brightest autocorrelation peak alone. Bartell discloses a method of aligning a periodic template with a periodic image where an initial alignment calculation is done to determine a cost, then multiple possible offsets are tried and cost recalculated for each to determine a final alignment (‘A combined-rank-score generator combines, for each of the initial and additional grid positions, members of sets of rank scores corresponding to the grid position in order to generate a combined rank score for the grid position. A grid alignment adjuster adjusts the alignment of the grid based on a comparison among the combined rank scores.’ Abstract, where rank scores are scores of a measure of contrast as shown by “The scheme described with respect to the present implementation is generally advantageous because the ranking indicates the grid position for each grid element that provides the greatest degree of contrast.”). It would have been obvious to a person having ordinary skill in the art at the time the application was filed to modify the method of Varma et al. to include the cost calculation and adjustment steps of Bartell to provide a more accurate alignment than can be consistently achieved with single point alignment.
Regarding claim 14, Varma et al. in view of Bartell discloses the metrology method of claim 13, wherein the cost is calculated over an entire area of one unit cell (Bartell, ‘A combined-rank-score generator combines, for each of the initial and additional grid positions, members of sets of rank scores corresponding to the grid position in order to generate a combined rank score for the grid position.’ abstract).
Regarding claim 15, Varma et al. in view of Bartell discloses the claimed method except for a step of downsampling the image for faster calculation of the cost. Downsampling is well known in the art and it would have been obvious to a person having ordinary skill in the art at the time the application was filed to modify the method of Varma et al. in view of Bartell to include a step of downsampling so that the data set is smaller and therefore computationally less expensive.
Regarding claim 16, Varma et al. in view of Bartell discloses the metrology method of claim 13, wherein the searching for at least one peak in periodicity step begins from a center of the image and continues radially outwardly (‘To achieve this, the distance of the pixel with the maximum pixel value from the center of the cross-correlation image represents the shift between the generated template and the actual spot locations.’).
Regarding claim 17, Varma et al. in view of Bartell discloses the metrology method of claim 13, wherein the calculating the periodicity step is performed using a Fast Fourier Transform (FFT) algorithm (‘determining grid spacing and rotational characteristics of the image by analyzing the detected coordinate locations of the frequency spectrum peaks with a Fourier transform analysis;’ abstract, while it is not specified whether the fast or discrete Fourier transform is used, it would have been obvious to a person having ordinary skill in the art to use the fast version to save computational resources).
Regarding claim 18, Varma et al. discloses an AFM for collecting data of a sample AFM comprising:
a probe that interacts with a surface of the sample (“atomic force microscopy ("AFM"),” P 47);
a controller that controls the probe-sample interaction and collect atomic force microscopy (AFM) data of a sample having an array of periodic features (“atomic force microscopy ("AFM"),” P 47); and
wherein the controller:
uses the AFM data to generate a sample image having feature pixels and background pixels (fig. 4, step 110, wherein ‘Moreover, the present teachings may also be used with any image where it is necessary to determine coordinate locations of periodic features, such as DNA microarrays based on fluorescence, atomic force microscopy ("AFM"), and the like.’ P 47);
calculates a periodicity of the features (fig. 4, step 120);
identifies peaks in the periodicity to determine a feature period and a lattice angle (fig. 4, step 125);
constructs a lattice mask template using the feature period and the lattice angle (fig. 4, step 130);
overlays the image with the lattice mask template (fig. 4, step 135);
performs an alignment calculation (fig. 4, element 140);
applying an offset of the lattice mask template to the image (fig. 4, step 140, wherein ‘The actual locations of the spots in the image are then obtained by adding the shift obtained in step 135 to the spot locations recorded when generating the template (step 140).’ P 44).
Varma et al. does not disclose the controller determining a cost, recalculating the cost after applying the offset, or repeating the applying and recalculating steps. Varma performing alignment based on the brightest autocorrelation peak alone. Bartell discloses a method of aligning a periodic template with a periodic image where an initial alignment calculation is done to determine a cost, then multiple possible offsets are tried and cost recalculated for each to determine a final alignment (‘A combined-rank-score generator combines, for each of the initial and additional grid positions, members of sets of rank scores corresponding to the grid position in order to generate a combined rank score for the grid position. A grid alignment adjuster adjusts the alignment of the grid based on a comparison among the combined rank scores.’ abstract). It would have been obvious to a person having ordinary skill in the art at the time the application was filed to modify the method of Varma et al. to include the cost calculation and adjustment steps of Bartell to provide a more accurate alignment than can be consistently achieved with single point alignment.
Regarding claim 19, Varma et al. in view of Bartell discloses the AFM of claim 18, wherein the controller performs the alignment step by at least one of a) calculating a standard deviation of the background pixels and setting the standard deviation as the cost value, and b) calculating a median of the background pixels and the feature pixels (“may include determining a median of intensities of three or more pixels, or every pixel, within each of the one or more grid elements in the first and second element groups.” P 12), and setting the median as a cost value (Bartell, ‘(d) analyzing the data to determine a measure of contrast; and (e) re-associating the data with a second set of locations based, at least in part, on the measure of contrast.’ P 17, where median and especially the difference of medians is a contrast value).
Regarding claim 20, Varma et al. in view of Bartell discloses the AFM of claim 19, wherein the controller further determine the offset of the lattice that establishes a minimum cost value if the standard deviation is calculated, and the offset of the lattice that establishes a maximum cost value if the median is calculated (Bartell, ‘The scheme described with respect to the present implementation is generally advantageous because the ranking indicates the grid position for each grid element that provides the greatest degree of contrast.’ P 76).
Response to Arguments
Applicant’s arguments, with respect to the U.S.C. 112(b) rejections based on the meaning “cost” have been fully considered and are persuasive. Applicant notes that cost has a special meaning in the art of machine learning, which examiner has verified with outside sources. Examiner’s specialty is nanoscale imaging and analysis techniques, such as AFM, rather than machine learning, so she was aware of this special definition. The rejection of claims 1-20 under U.S.C. 112(b) has been withdrawn.
Applicant's remaining arguments have been fully considered but they are not persuasive.
With regard to the rejection of claim 7 as indefinite, applicant argues that claim 7 highlights a particular type of measurement that the preferred embodiments may be employed to make.
That is precisely the problem – the limitation only states what is being measured (a recess fabricated into a semiconductor, presumably), while the claim is to performing an analysis on the image generated from that measurement (“comparing step used in … analysis”). Without knowing the steps of the analysis process, it is impossible to determine whether said analysis is performed. Examiner will refer applicant to MPEP 2173.05(q), which states “Attempts to claim a process without setting forth any steps involved in the process generally raises an issue of indefiniteness under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.”
With respect to the 103 rejections, applicant argues that Varma does not disclose iterative offsets, cost recalculation, or repetition to minimize/maximize cost over a range.
Examiner does not rely on Varma to teach those features. Examiner relies on Bartell to teach those features.
Also with respect to the 103 rejections, applicant argues that Bartell differs in using intensity ranking to calculate a cost, rather than a “cost measure like standard deviation or median difference.”
Examiner has cited passages in Bartell that specify the cost measure as a level of contrast.
Applicant argues that Bartell calculates the medians to identify bright/dim pixel groups initially, not as an iterative mask offset cost.
Bartell does disclose using medians to identify bright/dim pixel groups, but this is just one step in the process. The bright/dim pixel groups are identified specifically so that the difference between the brightest and dimmest pixel groups (the difference of medians) can be maximized (“The common objective of the present implementation and alternative approaches is to identify the grid position that provides an overall combination of brightest bright and dimmest dim grid elements as compared to any other grid position.”) through iterative offset cost calculations (“One or more additional positions of the grid are determined so that each additional position is offset from the initial position and from other additional positions. … For each of the initial and additional grid positions, the members of the sets of rank scores corresponding to the grid position are combined to generate a combined rank score for the grid position.”).
Applicant argues that Bartell’s offsets are small pixel shifts that lack lattice construction from periodicity peaks/angles, AFM, or cost optimization via standard deviation/median over a selected area.
Examiner does not see how the size of the pixel shifts relates to any claimed feature. The image of Bartell is periodic (checkerboard pattern), examiner is not sure what features of the image, beyond periodicity, applicant feels make up an image with “lattice construction from periodicity peaks/angles” or how those features would relate to any claim limitations. AFM is an image creation technique, and examiner has relied on Bartell only to show image analysis. Main art Varma teaches the limitation to using AFM to generate an image. Bartell does teach cost optimization via median over a selected area, as shown in the rejection above.
Applicant argues that the combination of Varma and Bartell would not yield the claimed invention but a rank-based shift, rather than iterative recalculation of cost.
The ranking process of Bartell includes an iterative recalculation of cost. Ranking involves creating an ordered list, in this case an ordered listed of costs for different offsets. In order to rank the costs at different offsets, the costs at each offset must of course be calculated.
Applicant argues that that the cited references teach away from one another because there would be no reason to modify Varma’s less computationally intensive method with Bartell’s exhaustive ranking, as Varma already achieves alignment insensitive to defects/background.
Teaching away requires a showing that a person having ordinary skill in the art would believe the combination would cease to function or become unsatisfactory for its intended purpose, not just that there is no reason to modify. If applicant is arguing simply that there is no rationale motivating the combination, examiner has provided a rationale which applicant has not specifically attacked.
Applicant goes on to say “In the end, Bartell addresses low resolution edge data in microarray scanning (1 [0042]), unable to meet the semiconductor fabrication needs provided by AFM's nanoscale topography. The claimed exhaustive unit-cell search (e.g., 1.2 period) for cost ensures efficient sub-nm precision for semiconductor recess determination, unachievable by the cited references.”
This argument does not specifically point out what limitation or limitations applicant believes is missing from the references. To the extent that the unit-cell search is claimed (claimed in claim 14 only), examiner has cited a passage of Bartell that teaches this. The rest of the quoted portion of the remarks discuss effects of the invention, rather than claimed features.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZA W OSENBAUGH-STEWART whose telephone number is (571)270-5782. The examiner can normally be reached 10am - 6pm Pacific Time M-F.
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/ELIZA W OSENBAUGH-STEWART/Primary Examiner, Art Unit 2881
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