DETAILED ACTION
1. This office action is in response to the amendment filed on 11/20/2025.
2. Claims 2, 9, and 17 are canceled.
3. Claims 1, 3-8, 10-16 and 18 are pending and presented for examination.
Response to Arguments
4. Applicant's arguments filed on 11/20/2025 have been fully considered but they are not persuasive.
In the remarks, the Applicant argues in substance that:
The cited references, Wang, Barnes, Cui, and Slachter either alone or in combination, fail to teach or suggest the amended claim limitations as recited in independent claims 1 and 14.
In response to argument:
a) Examiner respectfully disagrees. First, the Examiner would like to remind the applicant that the rejection is based on the broadest reasonable interpretation of the claims. The Applicant argues on pages 14-15 of the remarks that the cited art does not teach or suggest the limitations “obtaining, via an angular resolution spectrometer, a first momentum space image of the background of the target to be measured; obtaining, via the angular resolution spectrometer, a second momentum space image of the target to be measured when the target is illuminated by incident light; and deriving the dispersion relation pattern of the target to be measured in the momentum space based on the first momentum space image and the second momentum space image.” However, Wang in [0037], [0052], [0057], [0080] discloses the metrology tool 104 may include any type of metrology system known in the art suitable for providing metrology signals associated with metrology targets on a sample. The metrology tool 104 is configured to provide spectroscopic signals indicative of one or more optical properties of a metrology target (e.g., one or more dispersion parameters, and the like) at one or more wavelengths. For example, the metrology tool 104 may include, but is not limited to, a spectrometer, a spectroscopic ellipsometer with one or more angles of illumination. Further, it is noted herein that the metrology tool 104 depicted in FIG. 1C may facilitate multi-angle illumination of the sample 124, and/or more than one metrology illumination source 130 (e.g., coupled to one or more additional detectors 144). In this regard, the metrology tool 104 depicted in FIG. 1D may perform multiple metrology measurements…, one or more optical components may be mounted to a rotatable arm (not shown) pivoting around the sample 124 such that the angle of incidence of the metrology illumination beam 132 on the sample 124 may be controlled by the position of the rotatable arm. The metrology tool 104 may include multiple detectors 144 (e.g., associated with multiple beam paths generated by one or more beamsplitters) to facilitate multiple metrology measurements (e.g., multiple metrology tools) by the metrology tool 104; which corresponds to the claim limitation obtaining, via an angular resolution spectrometer, a first image of the background of the target to be measured within the claim.
Further, Wang in [0006], [0037], [0057], [0080] discloses the system includes a spectroscopic metrology tool to provide a spectroscopic signal indicative of the radiation emanating from a multilayer grating including two or more layers in response to incident illumination. In another illustrative.., the system includes a controller communicatively coupled to the spectroscopic metrology tool. In another illustrative embodiment, the controller generates a model of a multilayer grating including two or more layers, the model including one or more parameters associated with multilayer grating in which the one or more parameters include geometric parameters indicative of a geometry of a test layer of the multilayer grating and one or more dispersion parameters indicative of a dispersion of the test layer. The metrology tool 104 may include any type of metrology system known in the art suitable for providing metrology signals associated with metrology targets on a sample. The metrology tool 104 is configured to provide spectroscopic signals indicative of one or more optical properties of a metrology target (e.g., one or more dispersion parameters, and the like) at one or more wavelengths. For example, the metrology tool 104 may include, but is not limited to, a spectrometer, a spectroscopic ellipsometer with one or more angles of illumination. Further, it is noted herein that the metrology tool 104 depicted in FIG. 1C may facilitate multi-angle illumination of the sample 124, and/or more than one metrology illumination source 130 (e.g., coupled to one or more additional detectors 144). In this regard, the metrology tool 104 depicted in FIG. 1D may perform multiple metrology measurements… one or more optical components may be mounted to a rotatable arm (not shown) pivoting around the sample 124 such that the angle of incidence of the metrology illumination beam 132 on the sample 124 may be controlled by the position of the rotatable arm, which corresponds to the claim limitation obtaining, via the angular resolution spectrometer, a second image of the target to be measured when the target is illuminated by incident light within the claim.
Furthermore, Wang in [0006], [0037] discloses the system includes a spectroscopic metrology tool to provide a spectroscopic signal indicative of the radiation emanating from a multilayer grating including two or more layers in response to incident illumination. In addition, [0065], [0086] discloses the method 200 includes a step 202 of generating a parameterized model of a metrology target including a multilayer grating formed from two or more layers in which the model is parameterized with geometric parameters associated with the multilayer grating and dispersion parameters indicative of a dispersion of a test layer of the two or more layers. A model of step 202 may therefore include a representation of the physical and optical properties of the metrology target. In this regard, the multilayer grating may be a “device-like” metrology target such that the modeled geometric and dispersion parameters of the multilayer grating may correlate to the geometric and dispersion parameters of corresponding device features. Further, parameterization with at least one geometric parameter and at least one dispersion parameter may provide for a variation of the geometric and/or dispersive properties of at least one layer of the multilayer grating in response to variations of fabrication processes (e.g., of a process tool 102, and the like). Further, the inclusion of both geometric parameters and dispersion parameters may facilitate the determination of the dispersion parameters in the presence of dimension-dependent physical effects. The statistical model of step 206 may be trained by generating a design of experiments (DOE) in which spectroscopic signals are generated for a multitude of metrology targets having varied values of the geometric and dispersion parameters within defined ranges (e.g., associated with anticipated process variations). Further, the generated spectroscopic signals associated with each metrology target in the DOE may be analyzed to determine the relationships between aspects of the spectroscopic signals and particular values of the geometric and dispersion parameters. In this regard, the impacts of variations of the geometric and dispersion parameters, in isolation and in combination, on the resulting spectroscopic signals measurable by a metrology tool (e.g., metrology tool 104) may be determined, which corresponds to the claim limitation deriving the dispersion relation pattern of the target to be measured based on the first image and the second image within the claim.
Examiner relied on Cui to disclose the limitations “the target to be measured in momentum space, and obtaining a first momentum space image, and obtaining a second momentum space image.” Cui in Abstract and [0039] discloses a momentum space spectroscopy measurement system can be used to measure and characterize the optical information of micro-nano photonic materials in momentum space, such as band gap properties, energy band structure, dispersion relations, etc. The system can realize optical measurement of samples in microscopic areas, with a minimum measurement range of up to 1 micron; it can also realize high-resolution measurement in momentum space. The momentum space spectroscopy measurement system can accurately select the measurement area of the sample and can be further used to detect the spatial coherence information at different positions of the sample, which corresponds to the claim limitation the target to be measured in momentum space within the claim.
Further, Cui in [0039], [0044] discloses the momentum space spectroscopy measurement system can be used to measure and characterize the optical information of micro-nano photonic materials in momentum space, such as band gap properties, energy band structure, dispersion relations, etc. The system can realize optical measurement of samples in microscopic areas, with a minimum measurement range of up to 1 micron; it can also realize high-resolution measurement in momentum space. The momentum space spectroscopy measurement system can accurately select the measurement area of the sample and can be further used to detect the spatial coherence information at different positions of the sample. Figure 4 is a structural schematic diagram of a momentum space spectrum measurement system.., wherein ① is an objective lens, ② is a lens La, ③ is a lens Lb, ④ is a spectrometer, ⑤ is a lens Lc0, ⑥ is a lens Ld0, I is a sample, II is a back focal plane of the objective lens, III is a first imaging plane of the sample image, IV is a first imaging plane of the back focal plane, V is a second imaging plane of the sample image, VI is a second imaging plane of the back focal plane. Furthermore, Cui in [0050], [0055] discloses figure 2 shows the measurement results of the optical frequency information of the sample luminescence at different momentum space positions. The system can select different wavelength resolutions in the test according to actual needs.
361 The results in the X-M direction are measured with lower momentum space resolution, while the results in the Γ-M and Γ-X directions are measured with higher momentum space resolution, which corresponds to the claim limitation obtaining a first momentum space image, and obtaining a second momentum space image within the claim. Thus, the combination of Wang, Barnes, Cui, and Slachter meets the scope of broadly claimed limitation as currently presented.
b) In regard to 101 rejection, the Applicant has provided arguments
(see, pages 10-13).
b) In Response, the Examiner respectfully disagrees. Foremost, the decision of the Supreme Court in regard to Alice vs CLS Bank is succinctly discussed as follows. In their decision, Supreme Court has stated that the mere recitation of a generic computer cannot transform a patent-ineligible abstract ideas (such as algorithms) into a patent eligible invention. Because the algorithm was an abstract idea, the claim had to supply a “new and useful" application of the idea in order to be patent eligible (Alice, Page 12). Furthermore, the additional limitations had to be significantly more than a patent upon the ineligible concept itself (Alice, page 7, 15).
Regarding independent Claim 1, we recognize that the limitations “establishing, in accordance with parameters of incident light and a modeled geometric topography of the target to be measured, a simulation dataset associated with a dispersion curve of the target to be measured in momentum space, wherein the modeled geometric topography is characterized by a plurality of critical parameters; training …based prediction model based on the simulation dataset; obtaining,.., a dispersion relation pattern of the target to be measured in momentum space, wherein the dispersion relation pattern at least indicates a dispersion curve associated with the critical dimensions of the target to be measured; deriving the dispersion relation pattern of the target to be measured in the momentum space based on the first momentum space image and the second momentum space image, and extracting, based on the obtained dispersion relation pattern as an input, features related to the dispersion curve from the dispersion relation pattern via the trained prediction model”, as abstract ideas. The abstract idea of claim 1 can be characterized as processes, under their broadest reasonable interpretation, covers mental processes and/or mathematical concepts (See, specification [0060], [0098], [0102]).
Beyond the abstract idea, we next look at additional elements that can be considered to integrate the abstract idea into a practical application. In particular, the claim recites “a processor… a convolutional neural network...obtaining, based on an actual measurement of the target to be measured by incident light, wherein said obtaining of the dispersion relation pattern comprises: obtaining, via an angular resolution spectrometer, a first momentum space image of the background of the target to be measured; obtaining, via the angular resolution spectrometer, a second momentum space image of the target to be measured when the target is illuminated by incident light, and outputting, via the prediction model, an estimated probability density distribution of the at least one critical parameter based on the features.” Regarding the claim limitation, “a processor and outputting, via the prediction model, an estimated probability density distribution of the at least one critical parameter based on the features”, these limitations are recited at a high level of generality (i.e., as a generic computer structures performing a generic computer functions of displaying information) such that they amount no more than mere instructions to apply the exception using a generic computer components. As shown in the prior art, Wang et al. US 2019/0041266 (hereinafter, Wang), (Fig. 1A, 4, [0033]), and Slachter et al. US 11079687 (hereinafter, Slachter), (Fig. 1, 10, 11B, C, F, G), both show that “a processor and outputting, via the prediction model, an estimated probability density distribution of the at least one critical parameter based on the features”, are well-understood and purely conventional in the relevant art and would be routinely used by those of ordinary skill in the art in order to apply the abstract idea(s) and/or activities previously known to the pertinent industry. As such, the claim(s) as a whole does/do not amount to significantly more than the abstract idea itself.
Further, the claim recites “obtaining, based on an actual measurement of the target to be measured by incident light, wherein said obtaining of the dispersion relation pattern comprises: obtaining, via an angular resolution spectrometer, a first momentum space image of the background of the target to be measured; obtaining, via the angular resolution spectrometer, a second momentum space image of the target to be measured when the target is illuminated by incident light” but said limitations are recited at a high level of generality, and are nothing more than data collection activity for gathering parameters using a well-known conventional components and activity previously known in the industry in order to execute an abstract idea, which also does not further limit and integrate the abstract idea in practical application, and as such, do not amount to significantly more than the abstract idea itself. As shown in the prior art, Wang, (Figs. 1A-D), and Cui, ([0016]-[0017]), both show that obtaining, based on an actual measurement of the target to be measured by incident light, and wherein said obtaining of the dispersion relation pattern comprises: obtaining, via an angular resolution spectrometer, a first momentum space image of the background of the target to be measured; obtaining, via the angular resolution spectrometer, a second momentum space image of the target to be measured when the target is illuminated by incident light are well-understood and purely conventional in the relevant art and would be routinely used by those of ordinary skill in the art in order to apply the abstract idea(s) and/or activities previously known to the pertinent industry.
Furthermore, The claim also recites “a convolutional neural network”. However the “convolutional neural network” is recited at a high level of generality, and merely amounts to the use of computer technology as a tool to apply the abstract idea (see MPEP 2106.05(f)) and/or the use of “convolutional neural network” to perform the predictions, that are otherwise abstract, is merely an attempt at limiting the abstract to a particular field of use (See MPEP 2106.05(h)).
Accordingly, these additional elements do not integrate the abstract idea into a practical application because these elements do not impose any meaningful limits on practicing the abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the combination of these additional elements, when considered individually and as an ordered combination, do not amount to “significantly more” than the identified abstract idea. In addition, the claim recites “determining critical parameter of a target to be measured” that is recited at a high level of generality without providing specific type of target and specific practical applications performed in association with the target. As it is well known to one ordinary skill in the art, the target may cover vast categories of targes applied in different fields, such as, materials science, medicine, mechanical, and/or semiconductors, etc. Applicant encouraged to amend the claim that reflects specific type of target and specific practical applications performed in association with the target in order to advance prosecution. As claimed today, the claim is not patent eligible. Therefore, the 101 rejection is maintained.
Claim Rejections - 35 USC § 101
5. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
6. Claims 1, 3-8, 10-16 and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The representative claim 1 recites:
A metrology method for determining critical dimensions of a target to be measured, the method being implemented by a processor, the method comprising:
establishing, in accordance with parameters of incident light and a modeled geometric topography of the target to be measured, a simulation dataset associated with a dispersion curve of the target to be measured in momentum space, wherein the modeled geometric topography is characterized by a plurality of critical parameters;
training a convolutional neural-network-based prediction model based on the simulation dataset;
obtaining, based on an actual measurement of the target to be measured by incident light, a dispersion relation pattern of the target to be measured in momentum space, wherein the dispersion relation pattern at least indicates a dispersion curve associated with the critical dimensions of the target to be measured; wherein said obtaining of the dispersion relation pattern comprises:
obtaining, via an angular resolution spectrometer, a first momentum space image of the background of the target to be measured;
obtaining, via the angular resolution spectrometer, a second momentum space image of the target to be measured when the target is illuminated by incident light; and
deriving the dispersion relation pattern of the target to be measured in the momentum space based on the first momentum space image and the second momentum space image;
extracting, based on the obtained dispersion relation pattern as an input, features related to the dispersion curve from the dispersion relation pattern via the trained prediction model and
outputting, via the prediction model, an estimated probability density distribution of the at least one critical parameter based on the features.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category (process).
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and/or mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because the additional limitations in the claim are only: “a processor…a convolutional neural-network, … obtaining, based on an actual measurement of the target to be measured by incident light, obtaining, via an angular resolution spectrometer, a first momentum space image of the background of the target to be measured; obtaining, via the angular resolution spectrometer, a second momentum space image of the target to be measured when the target is illuminated by incident light… and outputting, via the prediction model, an estimated probability density distribution of the at least one critical parameter based on the features.” The limitations “a processor and outputting, via the prediction model, an estimated probability density distribution of the at least one critical parameter based on the features” are recited at a high level of generality (i.e., as a generic computer structures performing a generic computer functions of displaying information) such that they amount no more than mere instructions to apply the exception using a generic computer components.” Further, the limitation “a convolutional neural network” is recited at a high level of generality, and merely amounts to the use of computer technology as a tool to apply the abstract idea (see MPEP 2106.05(f)) and/or the use of “convolutional neural network” to perform the predictions, that are otherwise abstract, is merely an attempt at limiting the abstract to a particular field of use (See MPEP 2106.05(h)).
Furthermore, the limitations “obtaining, based on an actual measurement of the target to be measured by incident light, obtaining, via an angular resolution spectrometer, a first momentum space image of the background of the target to be measured; obtaining, via the angular resolution spectrometer, a second momentum space image of the target to be measured when the target is illuminated by incident light” are recited at a high level of generality (i.e., gathering data using a sensor) such that it amounts no more than mere instructions to apply the exception using a generic sensor.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as noted above, the additional limitations recited at a high level of generality (i.e., as a generic computer components processing and/or outputting information, and a generic sensor gathering data). Further, the additional elements are conventional in the art, as evidenced by the art of record (see, Wang (Figs. 1A-D, 4, and [0033]), and Slachter (Fig. 1, 10, 11B, C, F, G)), and/or Cui, ([0016]-[0017]). Therefore, claim 1 is directed to an abstract idea without significantly more.
The claim is not patent eligible.
Dependent claims 3-8, 10-12, and 15, add further details of the identified abstract idea. The claims are not patent eligible.
Independent claim 14, the claim is rejected with the same rationale as in claim 1 as explained above.
Dependent claims 13, 16, and 18, the claims are rejected with the same rationale as in claim 1 as explained above.
Claim Rejections - 35 USC § 103
8. In the event the determination of the status of the application as subject to AlA 35 U.S.C. 102 and 103 (or as subject to pre-AlA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 of this title, 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.
9. Claims 1, 5, 6, 8, 10-16, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al. US 2019/0041266 (hereinafter, Wang), in view of Barnes et al. “Contrasting Conventional and Machine Learning Approaches
to Optical Critical Dimension Measurements”, March 2020 (hereinafter, Barnes), in view of Cui et al. CN108519155A (hereinafter, Cui), in further view of Slachter et al. US 11079687 (hereinafter, Slachter).
10. Regarding claim 1, Wang discloses a metrology method for determining critical dimensions of a target to be measured, the method being implemented by a processor, the method comprising:
[determining], in accordance with parameters of incident light and a modeled geometric topography of the target to be measured, a simulation dataset associated with a dispersion curve of the target to be measured, wherein the modeled geometric topography is characterized by a plurality of critical dimensions ([0026], [0029], Fig. 2 and associate text: a statistical relationship between spectroscopic signals of the metrology target and values of the modeled dispersion parameters of the test layer may be determined through simulations of spectroscopic signals of many modeled metrology targets including multilayer gratings with varying parameters (e.g., varying geometric and dispersion parameters), calculating the modeled dispersion parameters for each simulation, and using a statistical model to determine statistical relationships between particular features of the spectroscopic signals measurable with the spectroscopic metrology tool and the modeled dispersion parameters of the test layer…determining a metrology metric proportional to the bandgap of the test layer based on the values of the dispersion parameters determined using the statistical model. In cases where the statistical model provides values of dispersion parameters related to the bandgap, the bandgap or a metric proportional to the bandgap must be extracted from the values of the dispersion parameters…a metrology metric proportional to the bandgap includes an integral of the dispersion curve in an exponentially-varying spectral region associated with an absorption edge (e.g., a transitional optical absorption)… a dispersion curve including an Urbach tail is reconstructed using an exponential form for the Urbach tail region to facilitate the determination of the transitional optical absorption integral… [Further], [0065]-[0077], [0087]: the method 200 includes a step 202 of generating a parameterized model of a metrology target including a multilayer grating formed from two or more layers in which the model is parameterized with geometric parameters associated with the multilayer grating and dispersion parameters indicative of a dispersion of a test layer of the two or more layers. A model of step 202 may therefore include a representation of the physical and optical properties of the metrology target. In this regard, the multilayer grating may be a “device-like” metrology target such that the modeled geometric and dispersion parameters of the multilayer grating may correlate to the geometric and dispersion parameters of corresponding device features. Further, parameterization with at least one geometric parameter and at least one dispersion parameter may provide for a variation of the geometric and/or dispersive properties of at least one layer of the multilayer grating in response to variations of fabrication processes (e.g., of a process tool 102, and the like). Further, the inclusion of both geometric parameters and dispersion parameters may facilitate the determination of the dispersion parameters in the presence of dimension-dependent physical effects…Referring generally to FIGS. 3B and 3C, geometric characteristics of the multilayer grating 316 such as, but not limited to the size of the patterned features 318 (e.g., the height 322, the middle critical dimension 326, the top critical dimension 324, the lateral critical dimension 334, and the like) or the shape of the of the patterned features 318 (e.g., a difference between the top critical dimension 324 and the bottom critical dimension 328, and the like) may impact the optical characteristics (e.g., dispersion characteristics) of any of the layers including, but not limited to the insulating layer 306 (e.g., a test layer for which the bandgap may be proportional to device performance));
a neural-network based prediction model ([0026], [0082], [0084], [0086], [0097]: a statistical relationship between spectroscopic signals of the metrology target and values of the modeled dispersion parameters of the test layer may be determined through simulations of spectroscopic signals of many modeled metrology targets including multilayer gratings with varying parameters (e.g., varying geometric and dispersion parameters), calculating the modeled dispersion parameters for each simulation,…the method 200 includes a step 206 of determining values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance. In step 206, the spectroscopic signal for an uncharacterized metrology target generated in step 204 is analyzed to determine values of the geometric and dispersion parameters associated with the model of step 202. The spectroscopic signal may be analyzed by data fitting and optimization techniques including,…machine-learning algorithms such as neural networks,…step 206 includes training a statistical model to determine the relationships between particular values of the one or more parameters (e.g., the geometric and dispersion parameters) and particular aspects of the spectroscopic signal of the modeled multilayer grating. For example, a statistical model include any model suitable for generating statistical relationships between aspects of a measured spectroscopic signal and particular values of geometric and dispersion parameters such that the values of the geometric and dispersion parameters of an uncharacterized metrology target may be predicted using the statistical relationships);
obtaining, based on an actual measurement of the target to be measured by incident light, a dispersion relation pattern of the target to be measured, wherein the dispersion relation pattern at least indicates a dispersion curve associated with the critical dimensions of the target to be measured ([0029], Fig. 2, and associate text: determining a metrology metric proportional to the bandgap of the test layer based on the values of the dispersion parameters determined using the statistical model. In cases where the statistical model provides values of dispersion parameters related to the bandgap, the bandgap or a metric proportional to the bandgap must be extracted from the values of the dispersion parameters…, a metrology metric proportional to the bandgap includes an integral of the dispersion curve in an exponentially-varying spectral region associated with an absorption edge (e.g., a transitional optical absorption). In this regard, the transitional optical absorption may provide a measure of the bandgap that is robust to defects that may not impact the electrical properties of interest (e.g., leakage current),...a dispersion curve including an Urbach tail is reconstructed using an exponential form for the Urbach tail region to facilitate the determination of the transitional optical absorption integral… [Further], [0067], [0079]-[0081]: The multilayer stack 302 may be patterned into a 2D or a 3D structure including features (e.g., periodic or aperiodic features) in one or more directions along the surface of the multilayer stack 302…, the patterned features 318 may be characterized by a height 322 along the Z direction, and one or more critical dimensions defined at selected heights (e.g., a top critical dimension 324 defined as a width at a top of the patterned features 318, a middle critical dimension 326 defined as a width at a mid-level height of the patterned features 318, and a bottom critical dimension 328 defined as a width of the patterned features 318 at a surface of the multilayer grating 316)…. [Furthermore], [0090]-[0098], Fig. 4 and associate text: provides statistical relationships between aspects of measurable spectroscopic signals and other dispersion parameters related to the bandgap (e.g., a dispersion curve, and the like)…FIG. 4 is a plot 402 of a dispersion curve of the imaginary part of the relative permittivity (ϵ2) near the bandgap of a layer in a multilayer grating,…a metrology metric proportional to the bandgap of a test layer may be determined at least in part by reconstructing the dispersion curve (e.g., generated in step 206) with one or more functional forms such as, but not limited to, lines, polynomials, piecewise polynomials, or exponential functions. For example, a metrology metric proportional to the bandgap of a test layer may be determined at least in part by reconstructing the dispersion curve with a generic form of the Urbach tail as an exponential function of photon energy in the transitional energy region. Accordingly, the bandgap may be extracted from the reconstructed dispersion curve using any method such as, but not limited to, evaluating an integral of a dispersion curve of the test layer over the transitional energy region); and
wherein said obtaining of the dispersion relation pattern comprises:
obtaining, via an angular resolution spectrometer, a first image of the background of the target to be measured ([0037], [0052], [0057], [0080]: the metrology tool 104 may include any type of metrology system known in the art suitable for providing metrology signals associated with metrology targets on a sample…, the metrology tool 104 is configured to provide spectroscopic signals indicative of one or more optical properties of a metrology target (e.g., one or more dispersion parameters, and the like) at one or more wavelengths. For example, the metrology tool 104 may include, but is not limited to, a spectrometer, a spectroscopic ellipsometer with one or more angles of illumination,… Further, it is noted herein that the metrology tool 104 depicted in FIG. 1C may facilitate multi-angle illumination of the sample 124, and/or more than one metrology illumination source 130 (e.g., coupled to one or more additional detectors 144). In this regard, the metrology tool 104 depicted in FIG. 1D may perform multiple metrology measurements…, one or more optical components may be mounted to a rotatable arm (not shown) pivoting around the sample 124 such that the angle of incidence of the metrology illumination beam 132 on the sample 124 may be controlled by the position of the rotatable arm. … the metrology tool 104 may include multiple detectors 144 (e.g., associated with multiple beam paths generated by one or more beamsplitters) to facilitate multiple metrology measurements (e.g., multiple metrology tools) by the metrology tool 104);
obtaining, via the angular resolution spectrometer, a second image of the target to be measured when the target is illuminated by incident light ([0006], [0037], [0057], [0080]: the system includes a spectroscopic metrology tool to provide a spectroscopic signal indicative of the radiation emanating from a multilayer grating including two or more layers in response to incident illumination. In another illustrative.., the system includes a controller communicatively coupled to the spectroscopic metrology tool. In another illustrative embodiment, the controller generates a model of a multilayer grating including two or more layers, the model including one or more parameters associated with multilayer grating in which the one or more parameters include geometric parameters indicative of a geometry of a test layer of the multilayer grating and one or more dispersion parameters indicative of a dispersion of the test layer….The metrology tool 104 may include any type of metrology system known in the art suitable for providing metrology signals associated with metrology targets on a sample… the metrology tool 104 is configured to provide spectroscopic signals indicative of one or more optical properties of a metrology target (e.g., one or more dispersion parameters, and the like) at one or more wavelengths. For example, the metrology tool 104 may include, but is not limited to, a spectrometer, a spectroscopic ellipsometer with one or more angles of illumination… Further, it is noted herein that the metrology tool 104 depicted in FIG. 1C may facilitate multi-angle illumination of the sample 124, and/or more than one metrology illumination source 130 (e.g., coupled to one or more additional detectors 144). In this regard, the metrology tool 104 depicted in FIG. 1D may perform multiple metrology measurements. … one or more optical components may be mounted to a rotatable arm (not shown) pivoting around the sample 124 such that the angle of incidence of the metrology illumination beam 132 on the sample 124 may be controlled by the position of the rotatable arm;
deriving the dispersion relation pattern of the target to be measured based on the first image and the second image ([0006], [0037]: the system includes a spectroscopic metrology tool to provide a spectroscopic signal indicative of the radiation emanating from a multilayer grating including two or more layers in response to incident illumination…[Further], [0065], [0086]: the method 200 includes a step 202 of generating a parameterized model of a metrology target including a multilayer grating formed from two or more layers in which the model is parameterized with geometric parameters associated with the multilayer grating and dispersion parameters indicative of a dispersion of a test layer of the two or more layers. A model of step 202 may therefore include a representation of the physical and optical properties of the metrology target. In this regard, the multilayer grating may be a “device-like” metrology target such that the modeled geometric and dispersion parameters of the multilayer grating may correlate to the geometric and dispersion parameters of corresponding device features. Further, parameterization with at least one geometric parameter and at least one dispersion parameter may provide for a variation of the geometric and/or dispersive properties of at least one layer of the multilayer grating in response to variations of fabrication processes (e.g., of a process tool 102, and the like). Further, the inclusion of both geometric parameters and dispersion parameters may facilitate the determination of the dispersion parameters in the presence of dimension-dependent physical effects…..The statistical model of step 206 may be trained by generating a design of experiments (DOE) in which spectroscopic signals are generated for a multitude of metrology targets having varied values of the geometric and dispersion parameters within defined ranges (e.g., associated with anticipated process variations). Further, the generated spectroscopic signals associated with each metrology target in the DOE may be analyzed to determine the relationships between aspects of the spectroscopic signals and particular values of the geometric and dispersion parameters. In this regard, the impacts of variations of the geometric and dispersion parameters, in isolation and in combination, on the resulting spectroscopic signals measurable by a metrology tool (e.g., metrology tool 104) may be determined);
extracting, based on the obtained dispersion relation pattern as an input, features related to the dispersion curve from the dispersion relation pattern via the prediction model ([0081]-[0086], Fig. 2 and associate text: the method 200 includes a step 206 of determining values of the one or more parameters of the modeled multilayer grating providing a simulated spectroscopic signal corresponding to the measured spectroscopic signal within a selected tolerance…The spectroscopic signal may be analyzed by data fitting and optimization techniques including,… machine-learning algorithms such as neural networks…,step 206 includes training a statistical model to determine the relationships between particular values of the one or more parameters (e.g., the geometric and dispersion parameters) and particular aspects of the spectroscopic signal of the modeled multilayer grating. For example, a statistical model include any model suitable for generating statistical relationships between aspects of a measured spectroscopic signal and particular values of geometric and dispersion parameters such that the values of the geometric and dispersion parameters of an uncharacterized metrology target may be predicted using the statistical relationships…It is recognized herein that a statistical model may provide accurate relationships between measured spectroscopic signals from a spectroscopic metrology tool (e.g., metrology tool 104) and modeled geometric and dispersion parameters of nearly any type of metrology target including, but not limited to, targets for which physical and optical properties are linked by dimension-dependent physical effects and targets for which defect states in fabricated layers may impact the corresponding optical or electrical properties…[Further], [0090]-[0091]: step 206 provides statistical relationships between aspects of measurable spectroscopic signals and other dispersion parameters related to the bandgap (e.g., a dispersion curve, and the like). Accordingly, step 208 may include calculating the bandgap of the test layer based on the values of the dispersion parameters generated in step 206.)… It is recognized herein that the dispersion function of many thin dielectric films may include tail regions near an absorption peak that may drastically impact the determination of the bandgap…[Furthermore], [0094]-[0098]: FIG. 4 is a plot 402 of a dispersion curve of the imaginary part of the relative permittivity (ϵ2) near the bandgap of a layer in a multilayer grating,.. the transitional energy region is defined by bounding photon energies Es and Ee (or equivalently, wavelengths) such that ϵ2 varies exponentially in the transitional energy region…a metrology metric proportional to the bandgap of a test layer may be determined at least in part by reconstructing the dispersion curve (e.g., generated in step 206) with one or more functional forms such as, but not limited to, lines, polynomials, piecewise polynomials, or exponential functions. For example, a metrology metric proportional to the bandgap of a test layer may be determined at least in part by reconstructing the dispersion curve with a generic form of the Urbach tail as an exponential function of photon energy in the transitional energy region. Accordingly, the bandgap may be extracted from the reconstructed dispersion curve using any method such as, but not limited to, evaluating an integral of a dispersion curve of the test layer over the transitional energy region….Further, the method 200 may include predicting the performance of a fabricated device feature based on any of the one or more determined parameters (e.g. the geometric and/or the dispersion parameters) determined in step 206. For instance, geometric parameters such as, but not limited to, critical dimensions, sidewall angles, lateral and horizontal dimensions may be utilized in conjunction with the metrology metric determined in step 208 to further predict the eventual performance of a fabricated device feature);
the prediction model [includes] an estimated probability density distribution of the at least one critical parameter ([0067], [0084]-[0086], Fig. 2).
Wang does not disclose:
establishing a simulation dataset, training a convolutional neural network based prediction model based on the simulation dataset; the target to be measured in momentum space; obtaining a first momentum space image, and obtaining a second momentum space image; and outputting an estimated probability density distribution of the at least one critical parameter based on the features.
However, Barnes discloses:
establishing a simulation dataset, training a neural network based prediction model based on the simulation dataset (Abstract, page 4, section 3.3, page 5, section 3.4, and pages 6-8, section 4, Training and Validation data: two ML approaches, a data-driven surrogate model for nonlinear regression using radial basis functions (RBF) and multiple-output Gaussian process regression (GPR) that indirectly applies the simulated intensity data…. As can be inferred from the small markers in Fig. 3, the simulation data library is not equally distributed throughout the simulation domain. In each realization, the validation set is indexed randomly while the nTP training data are selected through draws of random values for the parameters, normally distributed about the center of the simulation domain). Barnes discloses different types of machine learning as disclosed above. Further, Wang discloses machine-learning algorithms such as neural networks or support-vector machines (SVM), dimensionality-reduction algorithms (e.g., principal component analysis (PCA), independent component analysis (ICA), local-linear embedding (LLE), and the like). Wang in view of Barnes does not disclose a convolutional neural-network. However, a convolutional neural-network would have been obvious to one ordinary skill in the art based on the teaching of Wang and Barnes as disclosed above.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang to use the establishing a simulation dataset, training a convolutional neural network based prediction model based on the simulation dataset as taught by Barnes. The motivation for doing so would have been in order to determine the critical dimensions of a target accurately (Barnes, Abstract, page 1).
Wang in view of Barnes does not disclose:
the target to be measured in momentum space; obtaining a first momentum space image, and obtaining a second momentum space image; and outputting an estimated probability density distribution of the at least one critical parameter based on the features.
However, Cui discloses:
the target to be measured in momentum space (Abstract, [0039]: momentum space spectroscopy measurement system can be used to measure and characterize the optical information of micro-nano photonic materials in momentum space, such as band gap properties, energy band structure, dispersion relations, etc. The system can realize optical measurement of samples in microscopic areas, with a minimum measurement range of up to 1 micron; it can also realize high-resolution measurement in momentum space…The momentum space spectroscopy measurement system can accurately select the measurement area of the sample and can be further used to detect the spatial coherence information at different positions of the sample);
obtaining a first momentum space image, and obtaining a second momentum space image ([0039], [0044]: the momentum space spectroscopy measurement system can be used to measure and characterize the optical information of micro-nano photonic materials in momentum space, such as band gap properties, energy band structure, dispersion relations, etc. The system can realize optical measurement of samples in microscopic areas, with a minimum measurement range of up to 1 micron; it can also realize high-resolution measurement in momentum space…The momentum space spectroscopy measurement system can accurately select the measurement area of the sample and can be further used to detect the spatial coherence information at different positions of the sample…Figure 4 is a structural schematic diagram of a momentum space spectrum measurement system.., wherein ① is an objective lens, ② is a lens La, ③ is a lens Lb, ④ is a spectrometer, ⑤ is a lens Lc0, ⑥ is a lens Ld0, I is a sample, II is a back focal plane of the objective lens, III is a first imaging plane of the sample image, IV is a first imaging plane of the back focal plane, V is a second imaging plane of the sample image, VI is a second imaging plane of the back focal plane...[Further] [0050], [0055]: Figure 2 shows the measurement results of the optical frequency information of the sample luminescence at different momentum space positions…The system can select different wavelength resolutions in the test according to actual needs.
361 The results in the X-M direction are measured with lower momentum space resolution, while the results in theΓ-M and Γ-X directions are measured with higher momentum space resolution).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang in view of Barnes to use the target to be measured in momentum space; obtaining a first momentum space image, and obtaining a second momentum space image as taught by Cui. The motivation for doing so would have been in order to accurately select the measurement area of the sample and can be further used to detect the spatial coherence information at different positions of the sample (Cui, [0039]).
Wang in view of Barnes in view of Cui does not disclose:
outputting an estimated probability density distribution of the at least one critical parameter based on the fea33tures.
However, Slachter discloses:
outputting an estimated probability density distribution of the at least one critical parameter based on the features (column 25, line 22-column 26 lines 29, column 51, lines 23-38, Fig. 10, 11B, C, F, G).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang in view of Barnes in view of Cui to use outputting an estimated probability density distribution of the at least one critical parameter based on the features as taught by Slachter. The motivation for doing so would have been in order to analyze the critical dimension efficiently (Slachter, column 13, lines 53-59).
11. Regarding claims 14 and 18, the claims are rejected with the same rationale as in claim 1.
12. Regarding claim 5, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 1 as disclosed above.
Wang further discloses wherein obtaining the simulation dataset includes obtaining the simulation dataset by altering at least one of: incidence angle of incident light; wavelength of incident light; polarization of incident light; and critical dimensions of the modeled geometric topography ([0040], [0056], [0089]-[0090], [0100]).
13. Regarding claim 6, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 1, as disclosed above.
Wang further discloses noises related to light intensity into at least a part of the simulation dataset; and training the prediction model based on the simulation dataset ([0077], [0082], [0084]-[0087], [0093], [0097], Fig. 2). See also Barnes (pages 7-9).
Wang in view of Cui in view of Slachter does not disclose:
adding noises related to light intensity into at least a part of the simulation dataset, to obtain an enhanced simulation dataset with robustness to light intensity; and training the prediction model based on the enhanced simulation dataset.
However, Barnes discloses:
adding noises related to light intensity into at least a part of the simulation dataset, to obtain an enhanced simulation dataset with robustness to light intensity (Abstract, and pages 7-9: section 4).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang in in view of Cui in view of Slachter to use adding noises related to light intensity into at least a part of the simulation dataset, to obtain an enhanced simulation dataset with robustness to light intensity as taught by Barnes. The motivation for doing so would have been in order to determine the critical dimensions of a target accurately (Barnes, Abstract, page 1).
14. Regarding claim 8, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 1, as disclosed above.
Wang further discloses wherein the angular resolution spectrometer has a measurement angle selected from a range [of] degrees and a measuring wavelength selected from a near-infrared band from 900 nm to 1700 nm, or a visible light band from 360 nm to 900 nm, or an ultraviolet band from 200 nm to 360 nm ([0037], [0047], [0049], [0079]-[0080], Fig. 2: the metrology tool 104 is configured to provide spectroscopic signals indicative of one or more optical properties of a metrology target (e.g., one or more dispersion parameters, and the like) at one or more wavelengths. For example, the metrology tool 104 may include, but is not limited to, a spectrometer, a spectroscopic ellipsometer with one or more angles of illumination, a spectroscopic ellipsometer for measuring Mueller matrix elements (e.g., using rotating compensators), … an angle-resolved reflectometer (e.g., a beam-profile reflectometer), an imaging system, a pupil imaging system, a spectral imaging system, or a scatterometer…, the metrology tool 104 includes an image-based metrology tool to measure metrology data based on the generation of one or more images of a sample…., the metrology tool 104 includes a scatterometry-based metrology system to measure metrology data based on the scattering (reflection, diffraction, diffuse scattering, and the like) of light from the sample… the metrology illumination source 130 is a separate illumination source configured to generate a separate metrology illumination beam 132. The metrology illumination beam 132 may include one or more selected wavelengths of light including, but not limited to, ultraviolet (UV) radiation, visible radiation, or infrared (IR) radiation). Wang discloses an angle-resolved reflectometer measurement angle selected from a range degrees as disclosed above. Wang in view of Barnes in view of Cui does not disclose measurement angle selected from -60 to 60 degrees range degrees. However, selecting measurement angle from -60 to 60 degrees range degrees would have been obvious to one ordinary skill in the art based on the teaching of Wang, Barnes, and Cui as disclosed above.
Wang in view of Barnes in view of Slachter does not disclose:
the target to be measured in momentum space.
However, Cui discloses:
the target to be measured in momentum space (Abstract, [0039])
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang in view of Barnes in view of Slachter to use the target to be measured in momentum space as taught by Cui. The motivation for doing so would have been in order to accurately select the measurement area of the sample and can be further used to detect the spatial coherence information at different positions of the sample (Cui, [0039]).
15. Regarding claim 10, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 1, as disclosed above.
Wang further discloses wherein both the dispersion curve and the dispersion relation pattern are defined by a first coordinate and a second coordinate, wherein the first coordinate denotes energy or wavelength and the second coordinate denotes [permittivity] ([0037], [0065]-[0066], [0079]-[0081], [0094], Figs. 2 and 4). Further, Cui discloses momentum space spectroscopy measurement system, and the system can select different measurement area sizes during testing according to actual needs. Wang in view of Barnes in view of Cui in view of Slachter does not disclose wherein the first coordinate denotes energy or wavelength and the second coordinate denotes angle or momentum. However, wherein the first coordinate denotes energy or wavelength and the second coordinate denotes angle or momentum would have been obvious to one ordinary skill in the art based on the teaching of Wang in view of Barnes in view of Cui in view of Slachter as disclosed above.
16. Regarding claim 11, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 1, as disclosed above.
Wang further discloses wherein obtaining the simulation dataset includes: establishing the simulation dataset based on at least one of Rigorous Coupled Wave Analysis (RCWA) algorithm, Finite Difference Time Domain (FDTD), Finite Element Method (FEM) and Boundary Element Method (BEM) ([0087] ).
17. Regarding claim 12, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 11, as disclosed above.
Wang further discloses correcting the simulation dataset via at least one of a numerical aperture correction and an angular resolution correction for an objective lens for measurement ([0032], [0040], [0056], [0087], [0099]-[0100]).
18. Regarding claim 13, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 11, as disclosed above.
Wang further discloses the neural network (i.e., machine-learning algorithms such as neural networks) ([0082], [0084]: the spectroscopic signal may be analyzed by data fitting and optimization techniques including, but not limited to, libraries, fast-reduced-order models, regression, machine-learning algorithms such as neural networks…[Further], Metrology data from the metrology tool may be utilized in the semiconductor manufacturing process for example to feed-forward, feed-backward and/or feed-sideways corrections to the process (e.g., a lithography step, an etch step, and the like) to provide a complete process-control solution (see, ([0040]). Further, Wang discloses different types of machine learning. Wang in view of Barnes in view of Cui in view of Slachter does not disclose a convolutional neural network. However, wherein the neural network includes a convolutional neural network would have been obvious to one ordinary skill in the art based on the teaching of Wang in view of Barnes in view of Cui in view of Slachter as disclosed above.
19. Regarding claim 15, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 14, as disclosed above.
Wang further discloses wherein the sample dataset is a simulation dataset [determined] on the basis of parameters of incident light and a modeled geometric topography of the target to be measured, wherein the modeled geometric topography is characterized by a plurality of critical dimensions of a target to be measured ([0026], [0047], [0057], [0067], [0080], [0087], Figs. 1B-1C, 2). Further, Barnes discloses a simulation dataset established on the basis of parameters (Abstract, and pages 6-8).
20. Regarding claim 16, Wang discloses a metrology system, comprising:
an angular spectrometer configured to generate, based on an actual measurement of the target to be measured by incident light, a dispersion relation pattern of a target to be measured in momentum space, where the dispersion relation pattern at least indicates a dispersion curve related to critical dimensions of the target to be measured ([0029], [0067], [0079]-[0080], [0090]-[0096], Figs. 2, 4, and associate text); and
a computing device having a processor configured to operatively execute the metrology method according to claim 1 (The rest of the claim is rejected with the same rationale as in claim 1).
21. Claims 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Wang, in view of Barnes, in view of Cui, in view of Slachter, in further view of Chuang et al. US 20190285407 (hereinafter, Chuang).
22. Regarding claim 3, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 1, wherein obtaining, based on the actual measurements of the target to be measured by incident light, the dispersion relation pattern of the target to be measured in momentum space as disclosed above.
Wang further discloses measuring the target to be measured in practice with at least one of polarized light and polarized light, to obtain at least one of the corresponding polarized and polarized dispersion relation patterns of the target to be measured ([0030], [0052], [0056], [0080], Fig. 2).
Wang in view of Barnes in view of Slachter does not disclose:
the target to be measured in momentum space and at least one of s-polarized light and p-polarized light.
However, Cui discloses:
the target to be measured in momentum space (Abstract, [0039])
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang in view of Barnes, in view of Slachter to use the target to be measured in momentum space as taught by Cui. The motivation for doing so would have been in order to accurately select the measurement area of the sample and can be further used to detect the spatial coherence information at different positions of the sample (Cui, [0039]).
Wang in view of Barnes in view of Cui in view of Slachter does not disclose:
at least one of s-polarized light and p-polarized light.
However, Chuang discloses:
at least one of s-polarized light and p-polarized light ([0036], [0079], [0120], [0130]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang in view of Barnes in view of Cui in view of Slachter to use at least one of s-polarized light and p-polarized light as taught by Chuang. The motivation for doing so would have been in order to isolate information about polarized light and select a specific polarization of light (Chuang, [0120], [0130]).
23. Regarding claim 4, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 3, wherein extracting features related to the dispersion curve from the dispersion relation pattern via the trained prediction model, to determine an estimated value associated with at least one critical dimension of the target to be measured as disclosed above.
Wang further discloses obtaining the polarized dispersion relation patterns and outputting them to the prediction model, to acquire the estimated value associated with at least one critical dimension of the target to be measured ([0030], [0052], [0056], [0080]-[0086], [0090]-[0096], Figs. 2, 4).
Wang in view of Barnes in view of Cui in view of Slachter does not disclose:
obtaining both the s-polarized and p-polarized dispersion.
However, Chuang discloses:
obtaining both the s-polarized and p-polarized dispersion ([0036], [0079], [0120], [0130]).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang in view of Barnes in view of Cui in view of Slachter to use obtaining both the s-polarized and p-polarized dispersion as taught by Chuang. The motivation for doing so would have been in order to isolate information about polarized light and select a specific polarization of light (Chuang, [0120], [0130]).
24. Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Wang, in view of Barnes, in view of Cui, in further view of Gao et al. CN 111207677 B (hereinafter, Gao).
25. Regarding claim 7, Wang in view of Barnes in view of Cui in view of Slachter disclose the metrology method according to claim 6, as disclosed above.
Wang in view of Barnes in view of Cui in view of Slachter does not disclose:
wherein the noises related to light intensity includes one or more of a low-frequency disturbance, a Gaussian noise, a Perlin noise or a Gaussian function type disturbance.
However, Gao discloses:
wherein the noises related to light intensity includes one or more of a low-frequency disturbance, a Gaussian noise, a Perlin noise or a Gaussian function type disturbance (pages 5 and 9: gaussian noise is added into the light intensity image to simulate the noise in the actually acquired image).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the system of Wang in view of Barnes in view of Cui in view of Slachter to use wherein the noises related to light intensity includes one or more of a low-frequency disturbance, a Gaussian noise, a Perlin noise or a Gaussian function type disturbance as taught by Gao. The motivation for doing so would have been in order to improve the accuracy of the predictive model (Gao, page 7).
Conclusion
26. Examiner has cited particular columns and line numbers, and/or paragraphs, and/or pages in the references applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings of the art and are applied to specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested from the applicant in preparing responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. In the case of amending the claimed invention, Applicant is respectfully requested to indicate the portion(s) of the specification which dictate(s) the structure on for proper interpretation and also to verify and ascertain the metes and bounds of the claimed invention.
27. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
28. Any inquiry concerning this communication or earlier communications from the examiner should be directed to EYOB HAGOS whose telephone number is (571)272-3508. The examiner can normally be reached on 8:30-5:30PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Shelby Turner can be reached on 571-272-6334. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Eyob Hagos/
Primary Examiner, Art Unit 2857