DETAILED ACTION
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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-17 and 26-28 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tian et al., “Transfer Learning for Rapid Extraction of Thickness from Optical Spectra of Semiconductor Thin Films”.
In reference to claim 1, Tian teaches a computer-implemented method for measuring a structure based on a spectrum of the structure (title), the method comprising:
obtaining a first model trained based on simulation data, the first model including a first sub-model and a second sub-model following the first sub- model (abstract, where a pre-trained model is generated as the first model that is pre-trained on a generic simulated dataset. The model includes different sub-models as illustrated in fig. 1 and its caption);
generating a second model such that the second model includes a third sub- model generated from at least a portion of the first sub-model (page 6, last paragraph to page 7, first paragraph, where the second model is generated based on the pre-trained model);
training the second model based on sample spectrum data generated by measuring spectra of sample structures (page 3, paragraph 3 "thickness ML, with this transfer learning workflow, can potentially be implemented to any material class when there are a few literature or experimentally fitted refractive indices of that class (for retraining)" so that the re-training can also be implemented with a few measured spectra); and
estimating, based on the trained second model, the structure from measured spectrum data generated by measuring the spectrum of the structure (thickness estimation in the title & abstract).
In reference to claim 2, Tian teaches wherein the obtaining the first model comprises generating virtual spectra by simulating virtual structures, and wherein the simulation data represents the virtual structures and the virtual spectra (section II.A on the preparation of the simulated datasets).
In reference to claim 3, Tian teaches wherein the obtaining the first model comprises verifying the first model based on an error between output data of the first model and the simulation data (section II.C, where the simulated data set is split in training and validation data).
In reference to claim 4, Tian teaches the second model further comprises a fourth sub-model following the third sub-model, and the training the second model comprises fixing the third sub-model such that the third sub-model is not trained while training the fourth sub-model based on the sample spectrum data (fig. 1 with its caption and section II.D, where in case (2) only the weights of the fully-connected layer are retrained, while the weights of the convolutional layers are frozen).
In reference to claim 5, Tian teaches wherein each of the first sub-model and the third sub-model comprises a convolution network, and each of the second sub-model and the fourth sub-model comprises a fully connected network. (fig. 1 with its caption and section II.D, where in case (2) only the weights of the fully-connected layer are retrained, while the weights of the convolutional layers are frozen).
In reference to claim 6, Tian teaches verifying the second model based on an error between output data of the second model and measured structure data of the sample structures. (abstract in combination with page 3, paragraph 3).
In reference to claim 7, Tian teaches wherein the verifying the second model comprises extracting a first sample and a second sample from the simulation data, obtaining first output data and second output data of the first model, the first output data and the second output data respectively corresponding to the first sample and the second sample, obtaining third output data and fourth output data of the second model, the third output data and the fourth output data respectively corresponding to the first sample and the second sample, and verifying the second model based on a first relationship between the first output data and the second output data and a second relationship between the third output data and the fourth output data (sections II.C and II.D the verification of the first and the second model by comparing different outputs of the first model and the second model with the real values).
In reference to claim 8, Tian teaches wherein the training the second model comprises training the second model such that a loss proportional to an error between the first relationship and the second relationship decreases (sections II.C and II.D the verification of the first and the second model by comparing different outputs of the first model and the second model with the real values).
In reference to claim 9, Tian teaches adjusting at least one sub-process based on the estimated structure; and manufacturing an integrated circuit through a semiconductor process
comprising the adjusted at least one sub-process (abstract & p. 2, par. 3, where the film thickness is controlled as a process parameter).
In reference to claims 10-17 drawn to a system containing all of the same functional limitations as found in claims 1-8, the same rejections apply.
In reference to claim 26, Tian teaches a method for measuring a structure based on a spectrum of the structure (title), the method comprising: obtaining a first model trained based on simulation data, the first model including a first sub-model and a second sub-model following the first sub-model (abstract, where a pre-trained model is generated as the first model that is pre-trained on a generic simulated dataset. The model includes different sub-models as illustrated in fig. 1 and its caption); generating a second model based on the first model (page 6, last paragraph to page 7, first paragraph, where the second model is generated based on the pre-trained model); training the second model based on sample spectrum data generated by measuring spectra of sample structures (page 3, paragraph 3 "thickness ML, with this transfer learning workflow, can potentially be implemented to any material class when there are a few literature or experimentally fitted refractive indices of that class (for retraining)" so that the re-training can also be implemented with a few measured spectra); verifying the trained second model based output data of the first model and output data of the second trained model (sections II.C and II.D the verification of the first and the second model by comparing different outputs of the first model and the second model with the real values) and estimating, based on the trained second model, the structure from measured spectrum data generated by measuring the spectrum of the structure (thickness estimation in the title & abstract).
In reference to claim 27, Tian teaches wherein the verifying the second model comprises extracting a first sample and a second sample from the simulation data, obtaining first output data and second output data of the first model, the first output data and the second output data respectively corresponding to the first sample and the second sample, obtaining third output data and fourth output data of the second model, the third output data and the fourth output data respectively corresponding to the first sample and the second sample, and verifying the second model based on a first relationship between the first output data and the second output data and a second relationship between the third output data and the fourth output data (sections II.C and II.D the verification of the first and the second model by comparing different outputs of the first model and the second model with the real values).
In reference to claim 28, Tian teaches wherein the training the second model comprises training the second model such that a loss proportional to an error between the first relationship and the second relationship decreases (sections II.C and II.D the verification of the first and the second model by comparing different outputs of the first model and the second model with the real values).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON BOWERS whose telephone number is (571)272-1888. The examiner can normally be reached Flex M-F 7am-6pm.
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/B.B/Examiner, Art Unit 2851
/JACK CHIANG/Supervisory Patent Examiner, Art Unit 2851
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