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 Amendment
This action is made Non-Final because the rejections herein are directed to subject matter previously indicated as allowable but which has been deemed, on further review, to be obvious in view of the prior art.
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 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsov (US20170287751A1) in view of Abdi (Abdi, Hervé. "Partial least square regression (PLS regression)." Encyclopedia for research methods for the social sciences 6.4 (2003): 792-795.), further 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 (Figure 8: 402 to 404, ¶100),
generate an input data set by performing preprocessing on the spectrum data (¶79),
reducing a dimension of the spectrum data (¶77, measurement data is dimensionally reduced),
select data that is related to predictions of a structure of the target device (¶77-82, data is selected and a model trained to predict structure of the target device and therefore the data is related to the predictions of a structure of the target device),
train a model based on the input data set such that the model is configured to predict the structure of the target device (¶80-82), wherein the preprocessing includes
selecting a function based on the spectrum data (¶79, selecting an FFT or any other function can be said to be based on the spectrum data because it is used on the spectrum data).
Kuznetsov does not teach generating a linear combination variable giving a maximum covariance between the spectrum data and the structure of the target device.
Abdi teaches generating a linear combination variable giving a maximum covariance between the data and the dependent variable (§5, “For PLS regression this amounts to finding to sets of weights w and c in order to create (respectively) a linear combination of the columns of X and Y such that their covariance is maximum”, where Y is a dependent variable on X, see §3).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kuznetsov to utilize partial least squares in order to accurately predict the dependent variable, device structure, from the independent variable, the spectrum data, which would result in generating a linear combination variable giving a maximum covariance between the spectrum data and the structure of the target device.
While Kuznetsov discloses selecting a basis function to analyze/process spectrum data (see above), Kuznetsov does not disclose selecting a basis function based on the spectrum data and separating the spectrum data based on the basis function.
Torrence teaches selecting a basis function based on the spectrum data and separating the spectrum data based on the basis function (see Table 1 and §3e, which discusses selecting a wavelet function based on the data, i.e. selecting a basis function based on the spectrum data, and §7, steps 1-7, where a wavelet function is constructed at each scale, i.e. separating spectrum data based on the basis function) 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 Kuznetsov to include selecting a basis function based on the spectrum data and separating the spectrum data based on the basis function in order to implement a wavelet transform in Kuznetsov thereby providing an accurate, in depth spectrum analysis.
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).
Claim(s) 5-6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsov (US20170287751A1) in view of Abdi (Abdi, Hervé. "Partial least square regression (PLS regression)." Encyclopedia for research methods for the social sciences 6.4 (2003): 792-795.), further 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 Cilimkovic (Cilimkovic, Mirza. "Neural networks and back propagation algorithm." Institute of Technology Blanchardstown, Blanchardstown Road North Dublin 15.1 (2015): 18.)
Regarding claim 5, Kuznetsov as modified teaches all of the limitations of claim 1, wherein the model includes a neural network (¶80), but does not teach wherein the model includes a first sub model and a second sub model, the first sub model trained based on previous data, and the second sub model being trained based on the input data set, the first sub model includes initial weight data, and the second sub model includes retrained weight data resulting from retraining the initial weight data, based on the input data set.
Cilimkovic, in describing implementation of neural networks, teaches wherein the model includes a first sub model and a second sub model, the first sub model trained based on previous data, and the second sub model being trained based on the input data set, the first sub model includes initial weight data, and the second sub model includes retrained weight data resulting from retraining the initial weight data, based on the input data set (see §2.5, “Running the network consist of a forward pass and a backward pass. In the forward pass outputs are calculated and compared with desired outputs [the first sub model trained based on previous data…the second sub model being trained based on the input data set]. Error from desired and actual output are calculated. In the backward pass this error is used to alter the weights in the network [the first sub model includes initial weight data and the second sub model includes retrained weight data resulting from retraining the initial weight data, based on the input data set] in order to reduce the size of the error. Forward and backward pass are repeated [a first sub model and a second sub model] until the error is low enough (users usually set the value of accepted error)”).
To clarify, a first iteration can be considered a first sub model based on previous data and a second iteration can be considered a second sub model based on the input dataset.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to train the neural network as taught by Cilimkovic in order to provide an accurate neural network in Kuznetsov, thereby providing “wherein the model includes a first sub model and a second sub model, the first sub model trained based on previous data, and the second sub model being trained based on the input data set, the first sub model includes initial weight data, and the second sub model includes retrained weight data resulting from retraining the initial weight data, based on the input data set”.
Regarding claim 6, Kuznetsov as modified teaches all of the limitations of claim 5, and Cilimkovic further teaches wherein the model further includes a loss function that optimizes the initial weight data, and the loss function generates first gradient data and second gradient data, the first gradient data based on the previous data, the second gradient data based on the input data set, and the loss function updates the initial weight data based on the first gradient data and the second gradient data (see §2.5, “Running the network consist of a forward pass and a backward pass. In the forward pass outputs are calculated and compared with desired outputs. Error from desired and actual output are calculated [a loss function that optimizes the initial weight data]. In the backward pass this error is used to alter the weights in the network [the loss function generates first gradient data and second gradient data, the first gradient data based on the previous data, the second gradient data based on the input data set] in order to reduce the size of the error. Forward and backward pass are repeated [the loss function updates the initial weight data based on the first gradient data and the second gradient data] until the error is low enough (users usually set the value of accepted error)”).
Kuznetsov as modified and Cilimkovic are combineable for the same reasons as claim 5.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kuznetsov (US20170287751A1) in view of Abdi (Abdi, Hervé. "Partial least square regression (PLS regression)." Encyclopedia for research methods for the social sciences 6.4 (2003): 792-795.), further 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 Abdi (Abdi, Hervé. "Partial least square regression (PLS regression)." Encyclopedia for research methods for the social sciences 6.4 (2003): 792-795.), further 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.
Response to Arguments
Applicant’s arguments filed 09/09/2026 have been considered.
Applicant’s arguments are moot in view of the new grounds of rejection herein.
Conclusion
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/SCHYLER S SANKS/ Primary Examiner, Art Unit 2129
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