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 .
Drawings
The drawings are objected to because “μDBO measurements using system 100” disclosed in para 0074 is now in any figures. 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 § 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.
Claim(s) 1, 2, 4-7, 10-12 and 14-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (Wang) (2021/0241449).
Regarding claim 1, Wang discloses a method of compensating for focus deviations on a substrate having a plurality of layers present thereon (para 0006-0016, “correlation between the quality metric and the metrology data”, “a correction to the metrology data based on the correlation” “focus index is determined based on a sample selected from the image that has a relatively higher gradient compared to other locations “), the method comprising: obtaining key performance indicator data for each of a plurality of alignment marks on the substrate using an alignment sensor of a photolithographic apparatus (para 0073, 0075, measurements using an alignment sensor AS or level (height, tilt) measurements, para 0085, to measure or determine one or more properties such as alignment, overlay, line thickness, critical dimension (CD), focus offset); determining a correlation between the key performance indicator data and focus offset data for positions on the substrate (para 0125, a correction to the metrology data collected via a metrology tool involves use of a quality metric. The quality metric is an example of a focus index. A correction model is defined that uses the correlation between the quality metric and the metrology data to determining a correction to the metrology data); and using the correlation and the key performance indicator data, generating, by a hardware computer system (para 0217, may be implemented in hardware, firmware, software…), a focus prediction map for the substrate (para 0145, generating a map of a parameter of the patterning process based on the correction applied to the metrology data, the corrected data may be used to generate a dose map, a focus map…, para 0140, a correction model such as a machine learning model based on the correlation between the quality metric and the metrology data, and the focus map obtained can be considered a prediction focus map).
Regarding claim 2, Wang discloses wherein the positions on the substrate correspond to positions of the plurality of alignment marks (para 0075, detecting alignment marks P1, P2, and using position sensor IF).
Regarding claim 4, Wang discloses wherein the key performance indicator data are signal strength data (para 0125, the quality metric is image data which includes signal strength data).
Regarding claim 5, Wang discloses wherein the correlation is process dependent and layer dependent (para 0125, 0140, correction model is based on metrology data which is dependent on process and layer).
Regarding claim 6, Wang discloses wherein the correlation is expressed as a mathematical function (para 0137, 0140-0145, linear correction model is defined as a mathematical function).
Regarding claim 7, Wang discloses wherein the correlation is expressed as a first order 2D polynomial function (para 0067, Zernike polynomials, para 0142, a correlation between CD and the focus index).
Regarding claim 10, Wang discloses wherein the generating further comprises using level sensor data measured from the substrate (para 0073, using level sensor).
Regarding claim 11, Wang discloses using the focus prediction map to adjust focus for an imaging process of a subsequent layer of the substrate (para 0141, 0140, 0145 applying the trained correction model for a subsequent correction).
Regarding claim 12, Wang discloses a method of compensating for focus deviations on a substrate having a plurality of layers present thereon (para 0145, “by using such substrate height information per pattern transfer, the overlay impact due to a focus of the metrology tool (e.g., SEM) can be observed and accounted for”), the method comprising: obtaining a first layer height map for a first layer of the plurality of layers (para 0145, “a substrate height map obtained, for example, from the levelling sensor of the lithographic apparatus”); measuring, using a level sensor, a second layer height map for a second layer of the plurality of layers, wherein the second layer overlies the first layer (para 0145, “a substrate height map obtained, for example, from the levelling sensor of the lithographic apparatus”); subtracting the first layer height map from the second layer height map to obtain a delta height map for the substrate (para 0145, “difference can be found for the substrate height maps for two pattern transfers”); and using, by a hardware computer system, the delta height map to generate a focus prediction map for the substrate (para 0217, may be implemented in hardware, firmware, software…, para 0145, generating a map of a parameter of the patterning process based on the correction applied to the metrology data, the corrected data may be used to generate a dose map, a focus map…, para 0140, a correction model such as a machine learning model based on the correlation between the quality metric and the metrology data, and the focus map obtained can be considered a prediction focus map).
Regarding claim 14, Wang discloses wherein the first layer height map is obtained using a level sensor (para 0073, 0145).
Regarding claim 15, Wang discloses wherein the first layer is a top most layer that includes alignment or overlay metrology marks, and the second layer is a layer that is to be exposed in a subsequent exposure operation (para 0073, discloses alignment and level measurements, para 0145, discloses a focus map, a height map, an overlay map and taking a difference between height maps for two pattern transfers and converting it to an overlay value. Inherently, in order to expose the second transfer on the second layer, an alignment mark or an overlay mark is on the first layer).
Regarding claim 16, Wang discloses using the focus prediction map to adjust focus for an imaging process of a subsequent layer of the substrate (para 0141, 0145).
Regarding claim 17, Wang discloses a non-transitory computer-readable medium having instructions therein, the instructions, when executed by at least one processor, are configured to cause the at least one processor to cause performance of at least the method of claim 12 (para 0027, 0218 and claim 15).
Regarding claim 18, Wang discloses wherein the instructions are further configured to cause the at least one processor to use the focus prediction map to adjust focus for an imaging process of a subsequent layer of the substrate (para 0151-0155 discloses processor for carrying out the disclosed method which would include the using the focus prediction map and adjusting focus for an imaging process disclosed in para 0140, 0141 0145).
Regarding claim 19, Wang discloses a non-transitory computer-readable medium having instructions therein, the instructions, when executed by at least one processor, are configured to cause the at least one processor to cause performance of at least the method of claim 1 (para 0027, 0218 and claim 15).
Regarding claim 20, Wang discloses wherein the instructions are further configured to cause the at least one processor to use the focus prediction map to adjust focus for an imaging process of a subsequent layer of the substrate (para 0151-0155 discloses processor for carrying out the disclosed method which would include the using the focus prediction map and adjusting focus for an imaging process disclosed in para 0140, 0141 0145).
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 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Wang).
Regarding claim 3, Wang discloses the claimed invention as discussed above. Although Want does not explicitly disclose wherein the positions on the substrate correspond to positions defined by a focus model for the substrate, Wang discloses in para 0125, the focus index is an example of the quality metric and since a correction model uses the correlation between the quality metric and the metrology data to determine a correction to the metrology data, the correction model can be the focus model for the substrate. Therefore, it would have been obvious to one of ordinary skill in the art to have the positions of the substrate correspond to positions defined by a focus model for the substrate to provide correction to the metrology data as taught by Wang.
Regarding claim 13, although Wang does not disclose wherein the obtaining a first layer height map comprises reading stored height map data for the first layer, Wang discloses obtaining a substrate height map from the levelling sensor of the lithographic apparatus. Therefore, it would have been obvious to one of ordinary skill in the art to store the obtained height map in a memory for adjusting the focus during the exposure of the first layer.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. (Wang) in view of Lee et al. (Lee) (2011/0317163).
Regarding claim 9, Wang does not disclose wherein the key performance indicator data is obtained using a plurality of wavelengths of alignment sensor radiation for each of the plurality of alignment marks. Lee discloses detecting alignment mark using two different wavelengths (para 0048). Therefore, it would have been obvious to one of ordinary skill in the art to use a plurality of wavelength of alignment sensor radiation for each of the plurality of alignment marks in order to produce different diffraction image as taught by Lee in para 0049.
Allowable Subject Matter
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
None of the prior art of record teaches or discloses prior to the determining a correlation, removing at least a portion of intrafield focus data from the key performance indicator data to produce smoothed interfield focus data, wherein the using the correlation and the key performance indicator data comprises using the smoothed interfield focus data.
Wang discloses in para 0140, training a correction model such as a machine learning model based on the correlation between the quality metric and the metrology data and the key performance indicator or the quality metric as a focus index. However, Wang does not disclose removing at least a portion of intrafield focus data and producing smoothed interfield focus data.
Van Den Brink et al. (2022/0365450) discloses obtaining a plurality of values of a parameter relating to focus, determining an intra-field component and removing the intra-field component to determining scribe lane focus error contribution. Van Den Brink et al. does not disclose producing smoothed inter-field focus data, wherein the using the correlation and the key performance indicator data comprising using the smoothed inter-field focus data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Queens et al. (2019/0094713) discloses predicting focus information based on a functional relationship between local height deviation and focus information (abstract, para 0085-0090).
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/PETER B KIM/ Primary Examiner, Art Unit 2882 December 27, 2025