DETAILED ACTION
This action is responsive to the following communications: Original Application filed on February 9, 2024. All references to this application refer to the U.S. Patent Application Publication No. 2025/0251283 A1.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-25 are pending in this case. Claims 1, 20, and 25 are the independent claims. Claims 1-25 are rejected.
Priority
This application claims the benefit of U.S. Provisional Patent application No. 63/550,503, filed on February 6, 2024.
Claim Interpretation - 35 USC § 101
Claims 1-25 are interpreted as eligible under 35 U.S.C. 101 because while they recite a judicial exception under step 2A, prong 1 (mental processes/observations and judgements), the judicial exception is integrated into a practical application when evaluated under step 2A, prong 2. Additionally, these claims are comparable to the eligible claims 1 and 3 of example 47 of the July 2024 Subject Matter Eligibility examples.
Accordingly, claims 1-25 are eligible.
Examiner’s Note
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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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 1, 2, 7-12, 16, 17, 19-22, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2018/0322623 A1, filed by Memo et al., on May 8, 2018, and published on November 8, 2018 (hereinafter Memo), in view of U.S. Patent Application Publication No. 2009/0242513 A1, filed by Funk et al., on March 31, 2008, and published on October 1, 2009 (hereinafter Funk).
With respect to independent claim 1, Memo discloses a metrology system, comprising:
A controller including one or more processors configured to execute program instructions causing the one or more processors to implement a metrology recipe by: Memo discloses a controller including one or more processors configured to execute instructions to implement metrology recipes (see Memo, Fig. 1A).
Receiving two or more measurement datasets associated with a test feature on a sample from one or more measurement sub-systems operable under two or more measurement configurations, wherein a respective one the two or more measurement datasets is generated with a respective one of the two or more measurement configurations; Memo discloses receiving two or more measurement datasets associated with a sample test feature from one or more measurement sub-systems each generated by respective one of the configurations (see Memo, paragraphs 0056 [using a CNN to classify target objects having defects using summary descriptions], 0103 [describing the process of Fig. 4, in which depth cameras are used to capture depth and color images of the object, compute different views of the object, compute the descriptor of the object from the views and detect defects and output the classification], 0112 [data is integrated from a plurality of different subsystems and measurement datasets to incorporate differing viewpoints], 0115 [conversion of shape to appearance of the object], and 0126 [rendering different views (2D vs 3D) using multiple CNNs]).
Generating two or more intermediate metrology measurements of the test feature using two or more machine learning models, wherein a respective one of the two or more intermediate metrology measurements is generated using at least a portion of a respective one of the two or more measurement datasets as an input to a respective one of the two or more machine learning models; Memo discloses generating intermediate measurement of the test feature using at least two ML models using respective measurement datasets (see Memo, paragraphs 0162-0163 [using the multiple CNNs to process the inputs and render surfaces as features in order to generate intermediate results for use in generating a final result by combining (or comparing) 2D and 3D images]) .
Determining a final metrology measurement of the test feature …based on the two or more intermediate metrology measurements; Memo discloses generating a final metrology measurement based on the intermediate measurements (see Memo, paragraph 0173 [combining the intermediate outputs from the multiple CNNs into a final result]).
Although Memo discloses using weights within the CNNs (see Memo, paragraph 0153 [describing how weights are used to connect neurons between layers of the CNNs]), Memo fails to expressly disclose determining the final measurement using a weighting model.
However, Funk teaches using weighting models to provide varying weights to inputs for computation of outputs (see Funk, paragraph 0099 [one or more weighting models can receive target data and provide dynamic weights to the multi-layer/multi-input/multi-output (MLMIMO) models]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Memo and Funk before him at the time the invention was made, to modify the system of Memo to incorporate a weighting model as taught by Funk. One would have been motivated to make such a combination because this increases measurement accuracy in eliminates damage, as taught by Funk (see Funk, paragraph 0006 [“The invention can provide apparatus and methods of processing a substrate in real-time using Multi-Layer/Multi-Input/Multi-Output (MLMIMO) processing sequences MLMIMO and evaluation libraries to control gate and/or spacer thickness, to control gate and/or spacer uniformity, and to eliminate damage to the transistor structures.”]).
With respect to dependent claim 2, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Memo further teaches the system wherein a respective one of the two or more machine learning models is associated with a single respective one of the two or more measurement datasets.
Memo further teaches that each respective ML model is associated with a single one of the measurement datasets (see Memo, paragraphs 0162-0163, described supra, claim 1).
With respect to dependent claim 7, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Memo further teaches the system wherein at least one of the two or more machine learning models comprises: at least one of a linear model, a neural network model, a polynomial model, a decision tree model, or a random forest model.
Memo further teaches that the ML models are neural networks (e.g., CNNs) (see Memo, paragraphs 0162-0163, described supra, claim 1).
With respect to dependent claim 8, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Funk further teaches the system wherein the weighting model comprises: at least one of an average of the two or more intermediate metrology measurements, a weighted average of the two or more intermediate metrology measurements, or a neural network model.
Funk further teaches the weighting model comprising a MLMIMO model (see Funk, paragraph 0130, describing the different types of MLMIMO refinement procedures for the weighing models).
With respect to dependent claim 9, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Memo further teaches the system wherein the program instructions are further configured to cause the one or more processors to train the two or more machine learning models with training data.
Memo further teaches using training data to train the CNNs (see Memo, paragraph 0156 [describing the training procedure of Fig. 8, in which a training system trains the CNNs based on training data]).
With respect to dependent claim 10, Memo, as modified by Funk, teaches the metrology system of claim 9, as described above.
Memo further teaches the system wherein the training data comprises: at least one of simulated datasets or measurement datasets generated on one or more training samples with known parameters of the test feature.
Memo further teaches the training data comprises datasets generated from training samples with known parameters (see Memo, paragraph 0156, described supra, claim 9).
With respect to dependent claim 11, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Funk further teaches the system wherein the one or more measurement sub-systems comprise: at least one of a spectroscopic ellipsometer, a single-wavelength ellipsometer, an angle-resolved ellipsometer, an angle-resolved reflectometer, a spectroscopic reflectometer, a single-wavelength reflectometer, a Raman metrology tool, a laser dispersion spectroscopic reflectometry tool, a spectroscopic photoreflectance tool, a spectroscopic photoluminescence tool, an x-ray metrology tool, or a particle-based metrology tool.
Funk further teaches the measurement sub-systems can comprise at least one or more of polarizing reflectometry, spectroscopic ellipsometry, reflectometry, or other optical measurement techniques (see Funk, paragraph 0049 [listing different type of measurement subsystems]).
With respect to dependent claim 12, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Funk further teaches the system wherein at least one of the two or more measurement datasets comprises: spectroscopic measurement data.
Funk further teaches that at least one of the measurement datasets comprises spectroscopic measurement data (see Funk, paragraph 0049, described supra, claim 11).
With respect to dependent claim 16, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Funk further teaches the system wherein the final metrology measurement comprises: to at least one of a critical dimension (CD) measurement, a height measurement, an overlay measurement, a film thickness, or a material property.
Funk further teaches the final measurement can include height, overlay, thickness, or material property (see Funk, paragraph 0047 [describing the different measurements performed]).
With respect to dependent claim 17, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Funk further teaches the system wherein the test feature comprises: at least one of a patterned single-layer structure, a patterned multi-layer structure or a film stack.
Funk further teaches the test feature comprising at least a patterned single or multi layer structure (see Funk, paragraph 0021 [describing the physical layer configurations, including single and multi layer structures).
With respect to dependent claim 19, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Funk further teaches the system wherein the test feature is associated with at least one of an etch process, a lithography process, or a deposition process.
Funk further teaches the test feature is associated with etch process (see Funk, abstract [describing the invention being directed to partial or final etch processes]).
Independent claim 20, and its respective dependent claims 21 and 22, recite the metrology system of independent claim 1, and its respective dependent claims 2 and 11, further comprising: one or more measurement sub-systems configured to operate under two or more measurement configurations (taught by Memo, see Memo, paragraphs 0056, 0103, 0112, 0115, and 0126, described supra, claim 1). Accordingly, independent claim 20, and its respective dependent claims 21 and 22 are rejected under the same rationales used to reject independent claim 1, and its respective dependent claims 2 and 11, which are incorporated herein.
Independent claim 25 recites a metrology method performed by the system of independent claim 1. Accordingly, independent claim 25 is rejected under the same rationales used to reject independent claim 1, which are incorporated herein.
Claims 3, 4, 23, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Memo, in view of Funk, further in view of U.S. Patent Application Publication No. 2013/0151211 A1, filed by Li on December 9, 2012, and published on June 13, 2013 (hereinafter Li).
With respect to dependent claim 3, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Memo and Funk fail to further teach the system wherein at least one of the two or more machine learning models is associated with a single Mueller matrix element and further associated with a single respective one of the two or more measurement datasets.
However, Li teaches integrating Mueller matrices into metrology systems (see Li, paragraph 0074 [describing how the sample set is used to train ML systems using Mueller matrices]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Memo, Funk, and Li before him at the time the invention was made, to modify the system of Memo, as modified by Funk, to incorporate using Mueller matrices as taught by Li. One would have been motivated to make such a combination because this increases measurement accuracy and flexibility for metrology systems, as taught by Li (see Li, paragraph 0008 [“Thus, there is a need for a library with a reasonable size while maintaining the accuracy of the optical metrology system for determining profile parameters of the sample structure. Furthermore, there is a need for an optical metrology system that has the flexibility to handle metrology applications with different system calibration parameters, different ray tracing techniques, different beam propagation parameters, different diffraction metrology signal parameters (metrology signal parameters) while maintaining reasonable response times for integrated or standalone metrology applications.”]).
With respect to dependent claim 4, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Memo and Funk fail to further teach the system wherein at least one of the two or more machine learning models is associated with a linear combination of two or more Mueller matrix elements and further associated with a single respective one of the two or more measurement datasets.
However, Li teaches integrating linear combinations of Mueller matrix elements into metrology systems (see Li, paragraph 0074, described supra, claim 3).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Memo, Funk, and Li before him at the time the invention was made, to modify the system of Memo, as modified by Funk, to incorporate using linear combinations of Mueller matrix elements as taught by Li. One would have been motivated to make such a combination because this increases measurement accuracy and flexibility for metrology systems, as taught by Li (see Li, paragraph 0008, described supra, claim 3).
Dependent claims 23 and 24, recite the metrology system of dependent claims 3 and 4, further comprising: one or more measurement sub-systems configured to operate under two or more measurement configurations (taught by Memo, see Memo, paragraphs 0056, 0103, 0112, 0115, and 0126, described supra, claim 1). Accordingly, dependent claims 23 and 24 are rejected under the same rationales used to reject dependent claims 3 and 4, which are incorporated herein.
Claims 5 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Memo, in view of Funk, further in view of U.S. Patent Application Publication No. 2014/0316730 A1, filed by Shchegrov et al., on April 14, 2014, and published on October 23, 2014 (hereinafter Shchegrov).
With respect to dependent claim 5, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Memo and Funk fail to further teaches the system wherein the program instructions are further configured to cause the one or more processors to implement the metrology recipe by extracting two or more principal component sets from the two or more measurement datasets, wherein a respective one of principal component sets corresponds to a subset of a respective one of the two or more measurement datasets.
However, Shchegrov teaches extracting features for use in PCA for metrology (see Shchegrov, paragraph 0057 [describing the extraction of features for PCA]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Memo, Funk, and Shchegrov before him at the time the invention was made, to modify the system of Memo, as modified by Funk, to incorporate using PCA as taught by Shchegrov. One would have been motivated to make such a combination because this increases measurement accuracy and flexibility for metrology systems, as taught Shchegrov Li (see Shchegrov, paragraph 0010 [“Future metrology applications present challenges for metrology due to increasingly small resolution requirements, multi-parameter correlation, increasingly complex geometric structures, and increasing use of opaque materials. Thus, methods and systems for improved measurements are desired.”]).
Memo, as modified by Funk and Shchegrov further teach wherein generating the two or more intermediate metrology measurements of the test feature using two or more machine learning models comprises:
Generating the two or more intermediate metrology measurements of the test feature using the two or more principal component sets as inputs to the two or more machine learning models, wherein a respective one of the two or more principal component sets is provided as an input to the respective one of the two or more machine learning models; Shchegrov further teaches generating the intermediate measurements using PCA (see Shchegrov, paragraph 0057, described supra).
With respect to dependent claim 6, Memo, as modified by Funk and Shchegrov, teaches the metrology system of claim 5, as described above.
Shchegrov further teaches the system wherein the two or more principal component sets are generated using at least one of a principal component analysis or a fast Fourier Transform.
Shchegrov further teaches using FFT techniques (see Shchegrov, paragraphs 0134 [FFT is used]).
Claims 13-15, 18, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Memo, in view of Funk, further in view of U.S. Patent Application Publication No. 2022/0307990 A1, filed by Robinson et al., on June 4, 2021, and published on September 29, 2022 (hereinafter Robinson).
With respect to dependent claim 13, Memo, as modified by Ref2, teaches the metrology system of claim 1, as described above.
Memo and Funk fail to further teach the system wherein the two or more measurement configurations comprise: two or more illumination angles.
However, Robinson teaches using two or more illumination angles (see Robinson, paragraph 0051 [describing the different types of measurement configurations, including multiple illumination angles (altitude or azimuth or polar, different wavelengths or polarizations, etc.)]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Memo, Funk, and Robinson before him at the time the invention was made, to modify the system of Memo, as modified by Funk, to incorporate using multiple different types of measurement configurations as taught by Robinson. One would have been motivated to make such a combination because this increases measurement accuracy for metrology systems, as taught by Robinson (see Robinson, paragraph 0003 [“However, typical screening techniques for identifying die to be removed from the supply chain such as electrical testing all or part of a die may have insufficient throughput. It is therefore desirable to provide systems and methods for efficient screening.”]).
With respect to dependent claim 14, Memo, as modified by Funk and Robinson, teaches the metrology system of claim 13, as described above.
Robinson further teaches the system wherein the two or more illumination angles comprise: two or more altitude illumination angles.
Robinson further teaches using two or more altitude illumination angles (see Robinson, paragraph 0051, described supra, claim 13).
With respect to dependent claim 15, Memo, as modified by Funk and Robinson, teaches the metrology system of claim 13, as described above.
Robinson further teaches the system wherein the two or more illumination angles comprise: two or more azimuth illumination angles.
Robinson further teaches using two or more azimuth illumination angles (see Robinson, paragraph 0051, described supra, claim 13).
With respect to dependent claim 18, Memo, as modified by Funk, teaches the metrology system of claim 1, as described above.
Memo and Funk fail to further teach the system wherein the test feature comprises: two or more sub-features, wherein the final metrology measurement includes measurements of the two or more sub-features.
However, Robinson teaches processing two or more sub-features (see Robinson, paragraph 0027 [describing the features and sub-features measured]).
Accordingly, it would have been obvious to one of ordinary skill in the art, having the teachings of Memo, Funk, and Robinson before him at the time the invention was made, to modify the system of Memo, as modified by Funk, to incorporate measurement of multiple features and sub-features as taught by Robinson. One would have been motivated to make such a combination because this increases measurement accuracy for metrology systems, as taught by Robinson (see Robinson, paragraph 0003, described supra, claim 13).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure. See PTO-892.
It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ERIC J. BYCER whose telephone number is (571) 270-3741. The Examiner can normally be reached Monday - Thursday 9am-6pm, and alternate Fridays 9am-5pm.
Examiner interviews are available via a variety of formats. See MPEP § 713.01. To schedule an interview, Applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/InterviewPractice.
If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, MATT ELL can be reached on (571) 270-3264. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ERIC J. BYCER/
Primary Examiner
Art Unit 2141
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