DETAILED ACTION
Response to Arguments
Applicant argues that there is no suggestion or motivation to make the proposed modification with respect to the prior art of David in view of the prior art of Slotboom. Pages 7-8 of Applicant’s Remarks submitted on 04/07/2026. Applicant argues that the intended purpose of David is to teach misalignment between an adjacent layer and Slotboom would render David unsatisfactory for its intended purpose since Slotboom teaches three target layers. Id.
Respectfully, Examiner disagrees. Examiner disagrees with Applicant that the intended purpose of David is to teach misalignment between adjacent layers. Rather David’s intended purpose is to teach new techniques for measuring and/or compensating for process variations to predict yield, optimize testing, and burn-in procedures. As David recites in paras. [0002-0026]: “[t]his disclosure describes new techniques for measuring and/or compensating for process variations in production runs of a semiconductor manufacturing processes, for using these techniques to predict yield at any step of the process, and for optimizing testing and burn-in procedures.” (Emphasis added). What Applicant points to as David’s intended purpose are actually examples related to measurement issues and/or errors that have occurred in the art (see paras. [0055-0060] of David) which David’s teachings rectify with its new techniques for measuring and/or compensating pattern errors. See para. [0064] of David.
As the MPEP§ 2143.01 recites: "[a]lthough statements limiting the function or capability of a prior art device require fair consideration, simplicity of the prior art is rarely a characteristic that weighs against obviousness of a more complicated device with added function." In re Dance, 160 F.3d 1339, 1344, 48 USPQ2d 1635, 1638 (Fed. Cir. 1998)(Emphasis added). In this case, the examples which Applicant points to in the Remarks are examples that David used to explain improvements that the invention teaches with respect to new techniques for measuring and/or compensating for process variations to predict yield and optimizing testing and burn-in procedures. Just because Slotboom teaches three target layers, as Applicant has admitted in the Remarks, does not render David unsatisfactory for its intended purpose since as detailed above, David’s intended purpose is to teach new techniques for measuring and/or compensating for process variations to predict yield, optimize testing, and burn-in procedures.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 04/07/2026 has been entered.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. However, Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 62/546, 509 fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. As MPEP §211.05 details for a claim in a later filed nonprovisional application to be entitled to the benefit of the filing date of the provisional application, the written description and drawing(s) (if any) of the provisional application must adequately support and enable the subject matter of the claim in the later filed nonprovisional application. If a claim in the nonprovisional application is not adequately supported by the written description and drawing(s) (if any) of the provisional application (as in New Railhead), that claim in the nonprovisional application is not entitled to the benefit of the filing date of the provisional application. This is the case Applicant’s provisional application of 62/546,509 since the provisional does not contain a written description of the invention in such full, clear, concise, and exact terms as required under 35 U.S.C 112(a). Thus, the effective filing date to which Applicant is entitled benefit to for the current application 17/554,454 is its PCT filing date of 12/06/2017.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 9-10 and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over David, US 2016/0148850 Al (“David”) in view of Tarshish-Shapir et al US 2014/0136137 A1(“Tarshish”) and in view of SLOTBOOM et al. US 2017 /0090302 Al (“SLOTBOOM”)
Regarding claim 9, David teaches a method comprising:
configuring a multi-layered metrology target to have a plurality, of M target cells(David, para. 0066, see also Table II, “If there are n input variables, then the input
vector size for each target would be 1xn. Therefore, if there are m targets[to have a plurality, of M target cells], there will be an input data matrix of size mxn, with each row of the input data matrix associated with a target[configuring a multi-layered metrology target].” Examiner remarks: Examiner is interpreting the data matrix of size mxn with each row being associated with a target as being multi-layered ),
[wherein the multi-layered metrology target is part of a device portion that is in N>2 of target layers], and wherein M<2N(David, paras. 0065-0066, see also fig. 6 and table II, “If there are n input variables, then the input vector size for each target would be 1xn. Therefore, if there are m targets, there will be an input data matrix of size mxn, with each row of the input data matrix associated with a target. This is a typical training set in matrix format for a machine learning algorithm. An illustration of this matrix is given in Table II[and wherein M<2N]….”),1
measuring, scatterometrically, at least M differential signals from the multi- layered metrology target(David, para. 0070, see also table III, “If the reflectometry data is collected by illuminating the target with unpolarized broadband light and has a detectable wavelength range of 250 nm to 850 nm, then the user could choose to sample that light from 250 nm to 850 nm at 2 nm intervals, to get a total of 301 spectral intensity measurements for that wavelength range…[a]n example of how the input data is associated with a target is shown in Table III[measuring, scatterometrically, at least M differential signals from the multi- layered metrology target].”).
David does not teach: each cell having at least one periodic structure in each of the target layers, and configuring the periodic structures of each of the target cells to be offset with respect to each other by specified offsets; and applying at least one machine learning algorithm to the differential signals and to the specified offsets, to calculate scatterometry overlay (SCOL) metrology parameters by solving a set of M equations that relate the SCOL metrology parameters to the differential signals and to the specified offsets.
However, Tarshish teaches:
each cell having at least one periodic structure in each of the target layers, and configuring the periodic structures of each of the target cells to be offset with respect to each other by specified offsets(Tarshish, paras. 0054-0059, see also figs. 2D-2F, “FIGS. 2D-2F illustrate examples for kernels 90 taken from different targets 80… FIG. 2F illustrates visualizations 130 of Fisher's Kappa metric 110 over wafer 60, separated with respect to inner target elements in the current layer, and to outer target elements in the previous layer[each cell having at least one periodic structure in each of the target layers]…” & Tarshish, paras. 0066-0067, see also figs. 3A and 3B, “FIG. 3A schematically illustrates the target function f(x) as line 81, centered at the center of target 80, from which the center of the ROI 85 is offset by
∆
x
[and configuring the periodic structures of each of the target cells to be offset with respect to each other by specified offsets].”);
and applying at least one machine learning algorithm to the differential signals and to the specified offsets, to calculate scatterometry overlay (SCOL) metrology parameters [from the M measurements of the multi-layered metrology target] by solving a set of M equations that relate the SCOL metrology parameters to the differential signals and to the specified offsets(Tarshish, paras. 0083-0084, see also fig. 5, “[M]ethod 200 may further comprise clustering targets according to the metrics (stage 250) and analyzing the clustering
to indicate production errors (stage 252) and/or directing metrology measurements to target clusters to enhance target similarity… method 200 may be carried out during scatterometry overlay (SCOL) measurements[and applying at least one machine learning algorithm to the differential signals and to the specified offsets, to calculate scatterometry overlay (SCOL) metrology parameters]… target analysis may relate to…target asymmetry measures, ROI parameters, target clustering or any other criterion which is derived from any of the applied metrics…a statistical analysis of a plurality of metrics may be carried out to analyze the targets and to indicate targets which optimize SCOL accuracy (284)[by solving a set of M equations that relate the SCOL metrology parameters to the differential signals and to the specified offsets].”).2
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of David with the teachings of Tarshish the motivation to do so would be to visualize metrology metrics of targeted layers to improve the production accuracy of semiconductor wafers(Tarshish, para. 0055, “Visualization… may be configured to provide a wafer wide overview of metric[s]…with respect to some or all targets…which thus indicates visually the distribution of over-etching on wafer[s]… [v]isualization…may thus be used to correct the etching process and/or to correct the metrology
results to relate differently to correctly and incorrectly etched target.”).
David in view of Tarshish do not teach over N target layers, wherein N is at least three and N
≤
M; wherein the multi-layered metrology target is part of a device portion that is in N>2 of target layers.
However, Slotboom teaches:
over N target layers, wherein N is at least three and N
≤
M(SLOTBOOM, para. 0193, see also fig. 22(A)“Further, as shown in shown in FIG. 22(A), the sub-target 1908 may be in only 1 layer at a time. That is, different than, e.g., sub-target 1904 which has periodic structures in layers 3 and 2 or sub-target 1906 which has periodic structures in layers 3 and 1[over N target layers, wherein N is at least three and N
≤
M].”);
wherein the multi-layered metrology target is part of a device portion that is in N>2 of target layers (SLOTBOOM, para. 0193, see also fig. 22(A)“Further, as shown in shown in FIG. 22(A), the sub-target 1908 may be in only 1 layer at a time. That is, different than, e.g., sub-target 1904 which has periodic structures in layers 3 and 2 or sub-target 1906 which has periodic structures in layers 3 and 1[wherein the multi-layered metrology target is part of a device portion that is in N>2 of target layers]....”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of David in view of Tarshish with the teachings of SLOTBOOM the motivation to do so would be to improve the lithographic process through the use of a measuring parameter that can later be used for correction purposes(SLOTBOOM, paras. 0005-0006, “It is desirable to provide a method and apparatus
for metrology using a target, in which throughput, flexibility and/or accuracy can be improved. Furthermore, although not limited to this, it would be of great advantage, if this could be applied to small target structures that can be read out with a dark-field image-based technique...there is provided a method of measuring a parameter of a lithographic process, the method
comprising: illuminating a diffraction measurement target on a substrate with radiation, the measurement target comprising at least a first sub-target, at least a second sub-target
and at least third sub-target, wherein the first, second and third sub-targets each comprise a periodic structure... and detecting radiation scattered by the at least two sub-targets to obtain for that target a measurement representing the different parameters of the lithographic process”).
Regarding claim 10, David in view of Tarshish and in view of SLOTBOOM teaches the method of claim 9, wherein the applying at least one machine learning algorithm is further configured to extract overlay information from the M<2N cells(David, paras. 0065-0066, see also fig. 6 and table II, “Referring to FIG. 6, a flow chart illustrates a method 600 for creating and deploying a model to evaluate a semiconductor manufacturing process in order to correct for errors in a lithographic process, such as overlay errors and CD errors[and wherein the applying at least one machine learning algorithm is further configured to extract overlay information]… [i]f there are n input variables, then the input vector size for each target would be 1xn. Therefore, if there are m targets, there will be an input data matrix of size mxn, with each row of the input data matrix associated with a target. This is a typical training set in matrix format for a machine learning algorithm. An illustration of this matrix is given in Table II[from the M<2N cells]….”).
Regarding claim 12, David in view of Tarshish and in view of SLOTBOOM teaches the method of claim 9, wherein the at least one machine learning algorithm is derived during setup and/or training and applied in runtime(David, paras. 0082-0086, see also fig. 6 and fig. 8, “In step 612, the data is then fed into the algorithm for training[wherein the at least one machine learning algorithm is derived during setup and/or training]… FIG. 8 illustrates use of the model. In step 802, specified input data is collected, e.g., as an input vector, then fed into the model in step 804…[f]or each input vector of size 1xn fed into the algorithmic model, a score will be generated in step 806[and applied in runtime].”).3
Regarding claim 13, David in view of Tarshish and in view of SLOTBOOM teaches the method of claim 9, wherein the multi-layered metrology target comprises a single cell per target(David, paras. 0065-0066, see also fig. 6 and table II, “If there are n input variables, then the input vector size for each target would be 1xn[comprises a single cell per target]. Therefore, if there are m targets, there will be an input data matrix of size mxn, with each row of the input data matrix associated with a target. This is a typical training set in matrix format for a machine learning algorithm. An illustration of this matrix is given in Table II….” Examiner remarks: Examiner is interpreting the data matrix of size mxn with each row being associated with a target as being multi-layered),
and wherein the at least one machine learning algorithm is further configured to enable model-free on-the-fly optical overlay measurements of the single cell(Tarshish, para. 0071, “[O]nce the pitch of a SCOL target grating is known, one can use a wavelength which is only slightly smaller than the pitch to obtain an image which retains the periodic structure… [s]uch measurement is applicable during the calibration or on the fly to remove or replace defective targets[to enable model-free on-the-fly optical overlay measurements of the single cell].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of David with the above teachings of Tarshish for the same rationale stated at Claim 9.
Regarding claim 14, David in view of Tarshish and in view of SLOTBOOM teaches the method of claim 9, wherein the SCOL metrology parameters are overlays between the N layers(Tarshish, paras. 0060-0061, see also figs. 2H, 2I and 2J, “FIG. 2H illustrates kernels 90 of two target elements (e.g. belonging to different layers, namely the upper current layer and the lower previous layer)… FIGS. 2I and 2J illustrate kernels 90 from SCOL targets…FIG. 2I illustrates kernel 90A of a correctly produced SCOL target, and kernel 90B of an incorrectly produced SCOL target[wherein the SCOL metrology parameters are overlays between the N layers]….”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of David with the above teachings of Tarshish for the same rationale stated at Claim 9.
Regarding claim 15, David in view of Tarshish and in view of SLOTBOOM teaches the method of claim 9, wherein the application of the at least one machine learning algorithm to calculate the SCOL metrology parameters is carried out sequentially for consecutive layers(Tarshish, paras. 0083-0086, see also fig. 5, “[M]ethod 200 may further comprise clustering targets according to the metrics (stage 250) and analyzing the clustering to indicate production errors (stage 252) and/or directing metrology measurements to target clusters to enhance target similarity… method 200 may be carried out during scatterometry overlay (SCOL) measurements[wherein the application of the at least one machine learning algorithm to calculate the SCOL metrology]… [f]or example, method 200 may comprise increasing signal accuracy by judiciously selecting ROI's to include correct target parts (stage 325), possibly under consideration of different target layers[parameters is carried out sequentially for consecutive layers].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of David with the above teachings of Tarshish for the same rationale stated at Claim 9.
Regarding claim 16, David in view of Tarshish and in view of SLOTBOOM teaches the method of claim 9, wherein the application of the at least one machine learning algorithm to calculate the SCOL metrology parameters is carried out simultaneously for the layers, by carrying out the measuring at a pupil plane with respect to the target and using measurements of a plurality of pixel positions at the pupil plane(Tarshish, paras. 0071-0073, “[T]he methods and systems may be applied to pupil or spectral images as well as to field images. For example, once the pitch of a SCOL target grating is known, one can use a wavelength which is only slightly smaller than the pitch to obtain an image which retains the periodic structure. Measuring, for example by Fourier transform or by Fisher's kappa test, etc., the periodicity of the obtained image yields an indicator of the quality of the target[to calculate the SCOL metrology parameters is carried out simultaneously for the layers, by carrying out the measuring at a pupil plane with respect to the target and using measurements of a plurality of pixel positions at the pupil plane]… target characterization module 140 ( e.g., via analysis unit 120) is further arranged to cluster targets 80 according to metrics 110, analyze the clustering to indicate production errors and/or to direct metrology measurements to target clusters to enhance target similarity[wherein the application of the at least one machine learning algorithm].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of David with the above teachings of Tarshish for the same rationale stated at Claim 9.
Regarding claim 17, David in view of Tarshish and in view of SLOTBOOM teaches the method of claim 9, carried out at least partially by at least one computer processor(Tarshish, para. 0079, “Deriving 210, calculating 220 and/or analyzing 240, as well as any of the following stage may be carried out by at least one computer processor[carried out at least partially by at least one computer processor].”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of David with the above teachings of Tarshish for the same rationale stated at Claim 9.
Regarding claim 18, Tarshish teaches a computer program product comprising a non-transitory computer readable storage medium having computer readable program embodied therewith(Tarshish, para. 0079, “Deriving 210, calculating 220 and/or analyzing 240, as well as any of the following stage may be carried out by at least one computer processor) and for all other claim limitations they are rejected on the same basis as claim 9 since they are analogous claims.
Referring to independent claim 19, it is rejected on the same basis as
independent claim 18 since they are analogous claims.
Referring to independent claim 20, it is rejected on the same basis as
independent claim 9 since they are analogous claims.
Claim 11 are rejected under 35 U.S.C. 103 as being unpatentable over David, US 2016/0148850 Al (“David”) in view of Tarshish-Shapir et al US 2014/0136137 A1(“Tarshish”) and in view of SLOTBOOM et al. US 2017 /0090302 Al (“SLOTBOOM”) and further in view of Chen et al., US 8883024 B2(“Chen”).
Regarding claim 11, David in view of Tarshish and in view of SLOTBOOM teaches the method of claim 9, further comprising training the at least one machine learning algorithm on target designs of the multi-layered metrology target [which are based on metrology simulations], to match a behavior of the target designs to a specified device patterns behavior(David, paras. 0105-0106, “Algorithms can also be applied to the processing and manufacturing of 3D-NAND, or vertical NAND memory structures. To form vertical NAND (3-D NAND) structures, semiconductor manufacturers use alternating layers of oxide and nitride or oxide and conductor layers. These stacks can be a very thick, such as 2 um high, and are continuing to scale thicker. To address the stress issues, algorithms can use as inputs the process parameters (e.g., gas flows, temperature, process cycle times) of the blanket deposition of these films, as well as the in-situ and inline metrologies (including broadband light metrologies) used to measure these film stacks[training the at least one machine learning algorithm on target designs of the multi-layered metrology target]. Without explicitly having to apply any physical modeling, correlations can be found between yield/inspection/stress tests and the inputs mentioned above to immediately identify problems[to match a behavior of the target designs to a specified device patterns behavior]….” ).4
David in view of Tarshish and in view of SLOTBOOM do not teach metrology simulations.
However, Chen teaches:
which are based on metrology simulations(Chen, col. 42, lines 11-19, “In 1120, first simulation data can be determined for the first (VUV-EED
f
)-related procedure using a first Multi-Input/Multi-Output (MIMO) model for the first (VUV-EED
f
)-related procedure[which are based on metrology simulations]. The first MIMO model can include a first number (
N
a
) of first Controlled Variables (
C
V
1
a
,
C
V
2
a
,
…
,
C
V
N
a
), a first number (
M
a
)) of first Manipulated Variables (
M
V
1
a
,
M
V
2
a
,
…
M
V
M
a
) , and a first number (
L
a
)) of first Disturbance Variables (
D
V
1
a
,
D
V
2
a
,
…
,
D
V
L
a
), where (
L
a
,
M
a
, and
N
a
) are integers greater than one.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of David in view of Tarshish and in view of SLOTBOOM and further in view of Chen the motivation to do so would be to use simulation data to help create gate structures on the substrate level of semiconductors(Chen, col. 14, lines 34-56, “A controller can receive real time data from a MIMO optimizer/model…to update subsystem, processing element, process, recipe, profile, image, pattern, simulation, sequence data, and/or model data… the controllers can process messages and extract new data in real-time. When new data is available, the new data can be used in real-time to update a model and/or procedure currently being used for the substrate and/or lot.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 20170256465 A1(details a method of determining overlay of a given patterning process in which multiple overlays can be in different directions for a plurality of layers)
H. -F. Kuo and A. Faricha, "Artificial Neural Network for Diffraction Based Overlay Measurement," in IEEE Access, vol. 4, pp. 7479-7486, 2016(details the use of an artificial neural network for diffraction based overlay measurements)
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ADAM C STANDKE whose telephone number is (571)270-1806. The examiner can normally be reached Gen. M-F 9-9PM EST.
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/Adam C Standke/
Primary Examiner
Art Unit 2129
1 Examiner Remarks: The claim limitations that are not in bold and contained with square brackets i.e., [ ] are claim limitations not taught by David.
2 Examiner Remarks: The claim limitations that are not in bold and contained with square brackets i.e., [ ] are claim limitations taught by David.
3 According to the broadest reasonable interpretation (BRI), the use of alternative language amounts to the claim requiring one or more elements but not all.
4 Examiner Remarks: The claim limitations that non-bolded and contained with brackets are claim limitations that are not taught by the prior arts of David and Tarshish
Prosecution Insights