Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/31/2026 has been entered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-4 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsov (US20170287751A1) in view of Torrence (Torrence, Christopher; Campo, Gilbert P., A Practical Guide to Wavelet Analysis, Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado).
Regarding claim 1, Kuznetsov teaches a device structure simulation apparatus (Figure 8, see components below) comprising:
a memory storing a device structure simulation program; and a processor configured to execute the device structure simulation program stored in the memory, such that, by executing the device structure simulation program, the device structure simulation apparatus is configured to (Figure 8, ¶108)
receive spectrum data of a target device, the spectrum data being generated by a measuring device that is configured to emit polarized visible light to the target device and measure a reflected spectrum signal (Figure 8: 402 to 404, ¶100, 150-2000nm includes visible light),
generate an input data set by performing preprocessing on the spectrum data (¶79), and
train a model based on the input data set such that the model is configured to predict a structure of the target device (¶80-82), wherein the preprocessing includes
selecting a certain basis function based on the spectrum data (¶80, at least FFT involves the selection of basis functions), and separating the spectrum data into sets of certain basis functions (¶80, at least FFT involves the selection of basis functions and the separation of data into sets based on the basis functions), and
the model includes at least one sub model (¶80, each node of a neural network can be a sub-model).
While Kuznetsov discloses selecting a basis function to analyze/process spectrum data (see above), Kuznetsov does not disclose wherein the preprocessing is a noise removal preprocessing which includes selecting, from a plurality of basis functions, a basis function based on characteristics of the spectrum data, wherein the plurality of basis functions correspond to a plurality of characteristics of the spectrum data, respectively, and applying a transform algorithm that uses the selected basis function on the spectrum data to generate noise reduced spectrum data based on removing an optical measurement noise component in the reflected spectrum signal.
Torrence teaches wherein the preprocessing is a noise removal preprocessing (§6(a)) which includes selecting, from a plurality of basis functions, a basis function based on characteristics of the spectrum data (see §3, section “e”, and Table 1), wherein the plurality of basis functions correspond to a plurality of characteristics of the spectrum data (see section “e” of §3, where basis functions of Table 1 are discussed), respectively, and applying a transform algorithm that uses the selected basis function on the spectrum data to generate noise reduced spectrum data (see §7, steps 1-7) which allows for analyzing localized variations and how modes vary in time (§1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the preprocessing of Kuznetsov such that the preprocessing is a noise removal preprocessing which includes selecting, from a plurality of basis functions, a basis function based on characteristics of the spectrum data, wherein the plurality of basis functions correspond to a plurality of characteristics of the spectrum data, respectively, and applying a transform algorithm that uses the selected basis function on the spectrum data to generate noise reduced spectrum data based on removing an optical measurement noise component in the reflected spectrum signal in order to implement a wavelet transform in Kuznetsov thereby providing an accurate, in depth spectrum analysis.
To clarify, the modification would result in noise reduction “based on removing an optical measurement noise component in the reflected spectrum signal” because noise is reduced and the measurement in Kuznetsov is a reflected optical spectrum signal (see Figure 8) and therefore the noise can be considered optical measurement noise.
Regarding claim 2, Kuznetsov teaches all of the limitations of claim 1, wherein
the input data set includes simulation data and measurement data (¶61), and the model includes a first sub model and a second sub model (¶80, first and second nodes of a neural network can be considered sub-models), the first sub model trained based on the simulation data, and the second sub model trained based on the measurement data (¶80 and ¶61, each node of the neural network is trained on both datasets because the whole neural network is trained on the entire dataset which is composed of both datasets).
Regarding claim 3, Kuznetsov teaches all of the limitations of claim 2, wherein the device structure simulation apparatus is further configured to
generate device structure data through a simulation (¶61), based on the measurement data (¶61), and generate device spectrum data based on the device structure data, wherein the simulation data includes the device structure data and the device spectrum data (¶61 – simulated process data and simulated spectra are generated).
Regarding claim 4, Kuznetsov teaches all of the limitations of claim 2, wherein the device structure simulation apparatus is further configured
to train the first sub model based on the simulation data. and train the second sub model based on the first sub model and the measurement data (¶80, in the case of a neural network, the second sub model can be considered a neuron downstream from a neuron considered to be the first sub model, in which case it is trained both on the input data and the results of the preceding neuron).
Regarding claim 9, Kuznetsov teaches all of the limitations of claim 1, wherein
the device structure simulation apparatus is further configured to reduce a dimension of the spectrum data. and select data that is related to predictions of the structure of the target device (¶128, PCA reduces dimension).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsov (US20170287751A1) in view of Torrence (Torrence, Christopher; Campo, Gilbert P., A Practical Guide to Wavelet Analysis, Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado), further in view of David (US20120096006A1).
Regarding claim 8, Kuznetsov teaches all of the limitations of claim 1, but does not teach wherein the device structure simulation apparatus is further configured to separate the spectrum data into a high-frequency region and a low-frequency region, and process noise of the high-frequency region.
David teaches separating the spectrum data into a high-frequency region and a low-frequency region, and process noise of the high-frequency region (¶149) to reduce noise (¶149).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to separate the spectrum data into a high-frequency region and a low-frequency region, and process noise of the high-frequency region in Kuznetsov in order to reduce noise.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsov (US20170287751A1) in view of Torrence (Torrence, Christopher; Campo, Gilbert P., A Practical Guide to Wavelet Analysis, Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado), further in view of Reghunathan (US20180350979A1).
Regarding claim 11, Kuznetsov teaches all of the limitations of claim 1, but does not teach wherein the prediction of the structure of the target device includes at least one selected from a thickness, height, length, or boundary surface curvature of a sub element of the target device, and the sub element includes at least one selected from a source, gate, drain, and channel of a transistor.
However, Kuznetsov discloses that the structure prediction is within the field of semiconductor metrology (¶3) and Reghunathan, also in the field of semiconductor metrology, discloses that being able to accurately predict transistor channel length reduces the likelihood of misalignment errors (¶120).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Kuznetsov to the measurement/predicted measurement of transistor channel length in order to reduce the likelihood of misalignment errors in transistor fabrication.
Allowable Subject Matter
Claims 5-7 and 10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 5, the prior art does not establish anticipation or a prima facie case of obviousness for the particular manner in which the first and second sub-models are trained.
Regarding claim 10, the prior art does not disclose or render obvious the generation of the claimed linear combination variable which gives the claimed maximum covariance.
Response to Arguments
Applicant’s arguments filed 03/24/2026 have been considered.
As noted in Applicant’s remarks, the previously cited portions of Kuznetsov and Torrence were not considered to disclose the amended subject matter. However, as noted herein, Kuznetsov does disclose the usage of polarized visible light and combined with the noise reduction via wavelet analysis of Torrence, the claimed invention would have been obvious to one of ordinary skill in the art because the result of removing an optical measurement noise component in the reflected spectrum signal would be achieved.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCHYLER S SANKS whose telephone number is (571)272-6125. The examiner can normally be reached 06:30 - 15:30 Central Time, M-F.
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/SCHYLER S SANKS/Primary Examiner, Art Unit 2129
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