Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 02/27/2026 have been fully considered but they are not fully persuasive.
With respect to the rejection of claims under 35 USC 101 in view of the claim amendments, the applicant’s arguments are persuasive and the rejection has been dropped.
With respect to the rejection of claims under 35 USC 112, some of the amendments and arguments are persuasive and the rejections have been withdrawn. However, some of the rejections are not overcome.
With respect to the limitation regarding “output data output from the autoencoder” lacks antecedent basis. The applicant’s arguments that “output data” is a plural data noun that does not require an introduction is not persuasive. Since there is no output data previously disclosed, it is unclear if “outputting data” is part of the steps and although it is clear that the output data is from the autoencoder, it is not clear how it is related to inputting the preset spectrum and the normal spectra. Clarification is still required.
With respect to the limitation “a plurality of preset spectra data acquired in the past” the applicant argues that the origin of the preset spectra data is not relevant to the current claims. That may be so and the claims do not have to disclose where they come from, but that also cannot serve to limit the claims since the origin of data is not structural or limiting on the method steps.
With respect to the applicant’s arguments regarding the rejection of claims 102 as anticipated by Jiang, the arguments are not persuasive. In light of the claim amendments, the rejection was revised as below.
The applicant argues on page 19 that Jiang has a different intention, that Jiang fails to detect abnormal conditions in calibration data. However, detecting abnormal calibrations condition is not in the claim language. The applicant argues that Jiang fails to disclose creating the preset spectrum data before polishing of the workpiece. However, polishing of the workpiece is not part of the claimed system or method and whether a step is performed before or after the claimed limitations is irrelevant to the current claim set.
With respect to applicant’s arguments regarding Yang, the arguments are persuasive. The examiner agrees that Yang does not disclose thin film optical measurements nor tie the processing to that field. However, Yang does provide the state of the art for large data analysis. Therefore, Yang provides teachings for the data analysis of Jiang. The combination does disclose all the claimed limitations as recited in the rejection.
For these reasons, the rejection is as follows in response to the claim amendments.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim 1, 2, 4, and 9-16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 discloses “before polishing of the workpiece”, however “polishing of the workpiece” is never claimed as a step in the method. The method cannot be limited to steps that may or may not be taken outside of the claims boundaries. It is unclear if the claim is infringed upon if the workpiece is never polished. Additionally, the specification discloses in P.0001 that the workpiece is actually polished several times with each new layer. So it is unclear if “before polishing of the workpiece” means before any polishing is ever performed or if it just means before any final polishing. Clarification is required.
Claim 1 recites the limitation "output data output from the autoencoder" in line 6. There is insufficient antecedent basis for this limitation in the claim. There is no method step that includes outputting data from the autoencoder and no suggestion as to how that output might relate to the preset spectrum data and the normal preset spectra data.
With respect to claim 2 and 4, the limitation “a plurality of preset spectra data acquired in the past” is lacking antecedent basis. There is no method step for acquiring the preset spectra data in the past. It is unclear when or by what means this preset spectra data is acquired and how it relates to the rest of the claimed limitations.
With respect to claims 9-16, the claims disclose “an optical film-thickness measuring apparatus” however fail to actually disclose measuring a film thickness of a workpiece. In line 5, “a processing system configured to determine the film thickness of the workpiece” is configured to perform steps however fails to actually result in determining the film thickness. Clarification is required.
With respect to claims 9-16, the limitation “output data from the autoencoder” lacks antecedent basis. Data is input to the autoencoder, but there is no limitation regarding outputting data. Correction is required.
The balance of claims that are dependent upon the listed claims above fail to correct the deficiencies noted above.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim 1 and 4 are rejected under 35 U.S.C. 103 as unpatentable over
Jiang et al. U.S. Patent #9,915,522.
With respect to claim 1 and 4, Jiang et al. discloses optimized spatial metrology comprising:
Determining reference spectrum data which is one of a plurality of preset spectra data acquired in the past (Col.5, l 20-25, reference spectrum data = initial model of the measurement process, “which is one of a plurality of preset spectra data acquired in the past” is not limiting such that actions occurring in the past outside of the current claim cannot limit the claim)
Generating a latest preset spectrum data obtained by an optical film-thickness measuring system for use in polishing the workpiece, the latest preset spectrum data obtained before polishing the workpiece (Col.5, l 15-17)
Configuring an optical film thickness measuring system for use in polishing with the preset spectrum data before polishing of the workpiece (Col.19, l 44-56, wherein the first fabrication cluster and metrology system can be before or after the second fabrication cluster, wherein fabrication for semiconductor workpieces include polishing, Figure 2, step 202, measured spectral information = preset spectrum data)
Inputting the preset spectrum data to a trained model constructed by machine learning using training data including a plurality of normal preset spectra data to evaluate whether the optical film-thickness measuring system is operating under normal calibration conditions (Figure 2, step 208, trained model = model of the diffracting structure, Col.20, l 29-34, normal calibration conditions = yes model is a good fit)
Calculating a difference between output data output from the autoencoder and the preset spectrum data representing a deviation of the optical film thickness measuring system from the normal calibration conditions (Figure 2, step 208, a deviation of the optical film thickness measuring system from the normal calibration conditions means the measured spectral information cannot match up as a good fit to the model)
Determining that there is an abnormality in the preset spectrum data when the difference is larger than a threshold value, where the preset spectrum data is used for optical film-thickness measurement during polishing when the abnormality is not determined (Col.5, l 26-50, larger than a threshold value = not a good fit = opposite of termination condition where the difference between the previous model parameters and the current model parameters is less than a threshold value, abnormality = error significant enough to require additional iterations of blocks 206-210, preset spectrum data is used when abnormality is not determined = Is the model a good fit -< yes, then determine a characteristic of the diffracting structure = optical film-thickness measurement)
However, Jiang fails to specifically disclose an autoencoder.
As noted by the applicant in the response dated 02/27/2026 and evidenced by the supporting references, autoencoders are well known in the art for analyzing large sums of data. Jiang discloses using machine learning to generate a model. It would have been obvious to one of ordinary skill in the art at the time of the invention to use an autoencoder to automatically generate the model as being more efficient and hands off, saving time and money.
Claims 1, 2, 6, and 7, are rejected under 35 U.S.C. 103 as unpatentable over Jiang et al. U.S. Patent #9,915,522 in view of Yang et al. “Spectral Classification and Particular Spectra Identification Based on Data Mining”.
With respect to claims 2, 6, and 7, Jiang discloses all of the limitations as applied to claim 1 above. However, Jiang fails to disclose producing training data for the autoencoder, …
Yang discloses spectral classification comprising:
Creating the preset spectrum data before polishing of the workpiece (Page 923, paragraph 3.1, “preset spectrum” = standard MK spectra and training samples on Page 929)
Inputting the preset spectrum data to a trained model constructed by machine learning using training data including a plurality of normal preset spectra data (Page 929, 1st column, trained model = SVM model, preset spectrum data = training samples)
Calculating a difference between output data output from the model and the preset spectrum data (Page 919, second column, “difference between predicted value and actual value”)
Determining that there is an abnormality in the preset spectrum data when the difference is larger than a threshold value (Page 919, second column, threshold = unit step)
2, 6- Classifying a plurality of preset spectra data acquired in the past into groups according to algorithm clustering (Page 921, second column, K-means, partitioning n observations in to k clusters, classifying = clusters)
2- Producing the training data including a plurality of normal preset spectra data belonging to one of the groups (Page 921, first column, where KNN is “supervised learning algorithm” for classification tasks, training sample = preset spectra data)
2- And performing the machine learning using the training data to construct the autoencoder which is the trained model (Page 921, first column, wherein training data = training samples, trained model = KNN)
6- Determining reference spectrum data which is one of a plurality of preset spectra data acquired in the past (Page 932, first column, reference spectrum data = “spectrographic calibration from various observation equipment”)
7- Normalizing the plurality of preset spectra data acquired in the past to create a plurality of normalized preset spectra data and normalizing the latest preset spectrum data to create a latest normalized preset spectrum data (Page 928, first column, “the template spectrum is normalized to the SDSS spectrum”, Page 930, first column, “fluxes at each wavelength of the observed spectra and continuum-subtracted spectra are first normalized as the feature vectors based on which a graph is used to model the manifold structures among spectral data”)
It would have been obvious to one of ordinary skill in the art at the time of the invention to apply the calculations of Yang to the measuring system of Jiang since the steps of Yang are well known steps for trained models and analyzing large amounts of data. It is within ordinary skill to consider reference data and reanalyze it for errors to ensure that any measurements resulting from it are as accurate as possible. The steps of normalizing, clustering, different comparisons are all normal steps in data analysis and do not amount to an inventive step.
With respect to claim 9, 10, 12, 14, and 15, Jiang in view of Yang discloses all of the limitations as applied to claims 1, 2, 4, 6, and 7 as disclosed above. Jiang discloses an optimized spatial modeling comprising:
A light source configured to emit light (Figure 1, light source 106)
An optical sensor head configured to irradiate the workpiece with the light emitted by the light source and receive reflected light from the workpiece (Figure 1, sensor head = optical metrology system 100 including light 106 and detector 112, Col.1, l 36-40)
A processing system configured to determine the film thickness of the workpiece based on spectrum measurement data (Col.1, l 49-53, Col.1, l 23-28, wherein height of grating = film thickness)
However, Jiang fails to disclose the programming specifics for the trained model and spectrum comparisons.
Yang discloses:
Input the preset spectrum data to a trained model constructed by machine learning using training data including a plurality of normal preset spectra data (Page 929, 1st column, trained model = SVM model, preset spectrum data = training samples)
Calculate a difference between output data output from the autoencoder and the preset spectrum data (Page 919, second column, “difference between predicted value and actual value”)
Determine that there is an abnormality in the preset spectrum data when the difference is larger than a threshold value (Page 919, second column, threshold = unit step)
It would have been obvious to one of ordinary skill in the art at the time of the invention to apply the spectroscopic analysis and processing of Yang with the optical film spatial modeling of Jiang since both references are interested in efficiently analyzing large amounts of spectroscopic data reflected from an object of interest. The optical sensor head of Jiang provides a constant source of light and location for detection for any surface to be measured. Measuring the thickness of Jiang using the analysis and trained models of Yang allows for a more complete analysis of whatever is being looked at, that is able to consider high amounts of data in a small time frame.
With respect to claim 5 and 13, Jiang in view of Yang discloses all of the limitations as applied to claim 1, 4, 12 above. In addition, Yang discloses:
The difference is a Euclidean distance (Page 918, bottom of second column through top of Page 919)
With respect to claims 3, 8, 11, and 16, Jiang in view of Yang fails to disclose the preset spectrum data is one of base intensity data containing reference intensity, dark level data containing background intensity, and light monitoring data containing intensity of light from a light source.
It would have been obvious to one of ordinary skill in the art at the time of the invention that any preset spectrum data collected would inherently or at least ordinarily contain any base intensity data present in the system, any dark level data occurring beyond the system’s lighting, and the actual measured intensity of the irradiated light. These are components of any measured light that unless dramatic steps are taken to remove, are at least initially present in data from all measured light.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA CAROLE BRYANT whose telephone number is (571)272-9787. The examiner can normally be reached M-F, 12-4 pm.
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/REBECCA C BRYANT/Primary Examiner, Art Unit 2877