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 .
Response to Amendments
Acknowledgment is made of the amendment filed 04/27/2026, in which: claims 1, 2, 5, and 6 are amended; and the rejection of the claims are traversed. Claims 1-6 are currently pending an Office action on the merits as follows.
Response to Arguments
Applicant’s arguments with respect to claims 1-6 have been fully considered but are moot in view of the new grounds of rejection.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 2, 5, and 6 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Okuyama et al, US 20220139788.
Regarding claim 1, Okuyama discloses : An evaluation method that evaluates a difference between a target shape and a cross-sectional shape of an electron microscopic image, comprising: a first step of measuring a characteristic dimension of the cross-sectional shape(Scanning electron microscope measures cross-sectional shape [0062]); after the first step, a second step of creating a template of the target shape in the cross- sectional shape obtained from the characteristic dimension of the cross-sectional shape(Fig. 18, template created from image of fig. 16 [0107]); and a third step of comparing a difference between the template and the cross-sectional shape using a normalized metric(pixel information from dimension measurements put through neural network to output a label number to which each pixel is normalized [0068] with #113 comparing actual value to goal value [0090]), wherein the template of the target shape in the cross-sectional shape created in the second step includes a cross-sectional box region and a cross-sectional shoulder region within the cross- sectional box region(Fig. 13, output measurements of cross-sectional image to include background, mask, and substrate of cross sectional shape [0099]).
Regarding claim 2, Okayama discloses : The evaluation method according to claim 1, wherein the characteristic dimension of the cross-sectional shape includes a maximum width of the cross-sectional shape and a maximum depth of the cross-sectional shape, and the template is a rectangle formed based on the maximum width and the maximum depth(Fig. 10, two coordinates of bounding box given to include mask upper portion and trench bottom portion [0097] Fig. 11 shows an example from </object> to </object> of x and y maximum values of bounding box).
Regarding claim 5, Okuyama discloses : A search method using machine learning to search for an etching condition that brings a processing result by a plasma etching apparatus to a target shape, comprising: a first step of measuring a characteristic dimension of a cross-sectional shape(Scanning electron microscope measures cross-sectional shape [0062]. Machine learning used as object detection model [0072]); after the first step, a second step of creating a template of the target shape in the cross- sectional shape obtained from the characteristic dimension of the cross-sectional shape(Fig. 18, template created from image of fig. 16 [0107]); and a third step of comparing a difference between the template and the cross-sectional shape using a normalized metric(pixel information from dimension measurements put through neural network to output a label number to which each pixel is normalized [0068] with #113 comparing actual value to goal value [0090]); the method using, as one of objective variables, an evaluation result of an electron microscopic image obtained by execution of the third step wherein the template of the target shape in the cross-sectional shape created in the second step includes across-sectional box region and across-sectional shoulder region within the cross- sectional box region(Fig. 13, output measurements of cross-sectional image to include background, mask, and substrate of cross sectional shape [0099]).
Regarding claim 6, Okuyama discloses : A search system using machine learning to search for an etching condition that brings a processing result by a plasma etching apparatus to a target shape, the system comprising: a control unit configured to perform a first step of measuring a characteristic dimension of a cross-sectional shape(Scanning electron microscope measures cross-sectional shape process by #111 [0062]. Machine learning used as object detection model [0072]); after the first step, a second step of creating a template of the target shape in the cross-sectional shape obtained from the characteristic dimension of the cross-sectional shape(Fig. 18, template created from image of fig. 16 [0107]); and a third step of comparing a difference between the template and the cross- sectional shape using a normalized metric, the system using, as one of objective variables, an evaluation result of a microscopic image obtained by execution of the third step(pixel information from dimension measurements put through neural network to output a label number to which each pixel is normalized [0068] with #113 comparing actual value to goal value [0090]), wherein the template of the target shape in the cross-sectional shape created in the second step includes across-sectional box region and across-sectional shoulder region within the cross-sectional box region(Fig. 13, output measurements of cross-sectional image to include background, mask, and substrate of cross sectional shape [0099]).
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) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Okuyama et al, US 20220139788 in view of Murakami et al, US 20130322737.
Regarding claim 3, Okuyama discloses : The evaluation method according to claim 2.
Okuyama does not disclose : wherein a difference in area between the rectangle and the cross-sectional shape is used as the metric, and the difference comprises an area number which is determined by subtracting a region of the cross-sectional shoulder region from the cross-sectional box region, divided by the region of the cross-sectional box region.
However, in the same field of endeavor, Murakami teaches : wherein a difference in area between the rectangle and the cross-sectional shape is used as the metric(Evaluation value #F3 calculated by the difference of background and interconnection part [0100])., and the difference comprises an area number which is determined by subtracting a region of the cross-sectional shoulder region from the cross-sectional box region, divided by the region of the cross-sectional box region(Formula #f3 to include min of background and interconnection region divide by area [0100]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to apply the teachings of Murakami to the cross-sectional image of Okuyama’s background, mask, and substrate in a bounding box by subtracting the background from the maximum dimensions of the boundary box and dividing the value by the maximum dimension of the boundary box to get an area of the substrate and mask.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Okuyama et al, US 20220139788 in view of Rucklidge et al, US 5999653.
Regarding claim 4, Okuyama discloses : The evaluation method according to claim 2.
Okuyama does not disclose : wherein a shortest distance among distances from a corner of the rectangle to the cross-sectional shape is used as the metric.
However, in the same field of endeavor, Rucklidge teaches : wherein a shortest distance among distances from a corner of the rectangle to the cross-sectional shape is used as the metric(Hausdorff distance between two set used to determine if a pattern and an image matches [Col. 2, line 26-57]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date to apply the teachings of Rucklidge to Okuyama to use the Hausdorff distance to provide reliable indicators of a match between pattern and image [Col. 2, line 26-57].
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.
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/D.T./Examiner, Art Unit 2897 /CHAD M DICKE/Supervisory Patent Examiner, Art Unit 2897
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