DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is responsive to pending claims 1-5 filed 3/23/2323.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application CN202210390710.4 filed in China on 4/14/2022.
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim(s) 1-5 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement.
Claim 1 recite inputting conductor arrangements into a “FasterCap” tool in order to generate XGBoost labels”. But the Specifications do not provide sufficient written description for these elements, and hence, a one of ordinary skill in the art, being unable to understand the structure of these tools, can reasonably conclude that the inventor had possession of the claimed invention.
The remaining claims are rejected for failing to cure the deficiency of the parent.
Claim(s) 1-5 are rejected under 35 U.S.C. 112(a) as failing to comply with the enablement requirement.
Claim 1 recite inputting conductor arrangements into a FasterCap tool in order to generate XGBoost labels”. But the Specifications do not provide sufficient written description for this process. For example, a person of ordinary skill in the art would not know how to obtain data from a FasterCap tool in order to generate XGBoost labels, nor to perform training on the XGBoost model via the input and the label dataset based on the Specifications, for example, as presented in 0029-30.
Claim 1 recites “an adaptive window extraction and gridding method” for characterizing an arbitrary conductor arrangement. However, the Specifications, for example, at 0012-13, 0030, do not provide sufficient description to enable this process. A person of ordinary skill in the art would not know how to perform this adaptive windowing of the conductors as presently described.
The remaining claims are rejected for failing to cure the deficiency of the parent.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim(s) 1-5 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
In claim 1 ¶3, “large number” is relative terminology, see MPEP 2173.05(b). The Specifications do not the reader with an understanding of what this number range could be.
The remaining claims are rejected for failing to cure the deficiency of the parent.
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.
Claim(s) 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Yang ("CNN-Cap: Effective convolutional neural network based capacitance models for full-chip parasitic extraction", published 7/14/2021) in view of Raichura ("Efficient CNN‐XGBoost technique for classification of power transformer internal faults against various abnormal conditions", published 2021) in view of FastFieldSolvers ("FasterCap", published 5/15/2021).
For claim 1, Yang discloses: an energy-efficient capacitance extraction method based on machine learning (fig.8 gives overview of the method, the method being energy efficient relative to traditional pattern matching (§1¶4) of field solvers (p.8 col.2 ¶2-3)), comprising following steps of:
a data set preparation stage (§III.D: “Dataset Generation and Other Discussion” (p.6)): randomly generating enough input samples with different conductor arrangements under different technological standards (§III.D ¶2: random samples, with §IV¶1 contemplating 55 and 15nm technology standards), and inputting the input samples into a field solver tool after the input samples are subjected to data preprocessing, and taking field solver output data as machine learning model labels (§III.D ¶1: using a field solver for preparing training data for the CNN of fig.9); meanwhile, with a two-dimensional cross-sectional structure regarded as an image, characterizing an arbitrary arrangement mode of any number of conductors as a respective two-dimensional matrix (§III.B: “Grid-Based Data Representation” (p.4), fig.8: generating a 2-dimensional matrix representation of a 2D cross section, such as those described in §III.D) by using an adaptive window extraction and gridding method (§III.D ¶3), thereby obtaining input of machine learning model from the input samples randomly generated (fig.8, §IV: providing input to a machine learning model during training and experiments);
machine learning model training: combining machine learning model input and the machine learning model labels into a data set (§III.D: the dataset is generated based on structures and labels), performing training with a large number of data sets respectively to obtain two machine learning models of self-capacitance and coupling capacitance (fig.8 gives overview separate total and coupling capacitance models, with total capacitance constituting self-capacitance, i.e., the amount of capacitance experienced by the conductor as self without particular consideration of coupling effects, see p.2 col.1 ¶3 (“A grid-based …”) );
problem solving: taking a two-dimensional cross-sectional structure of a chip whose capacitance is to be extracted as an input of a capacitance extractor according to the adaptive window extraction and gridding method, and obtaining a self-capacitance of a main conductor and a coupling capacitance between the main conductor and an adjacent conductor at an output end of the capacitance extractor, thereby realizing parasitic capacitance extraction of a full chip (§IV gives overview of experimental results, see tables 1-3, of various full chip structures).
Yang does not disclose: wherein the field solver is FasterCap, wherein the machine learning model is XGBoost.
Raichura discloses: wherein the machine learning model is XGBoost (fig.1, §2-2.1: using XGBoost as a classifier for 2D vector data).
It would have been obvious before the effective It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Yang by incorporating the XGBoost technique of Raichura. Both concern the art of electronic system analysis, and the incorporation would have, according to Raichura, allowed use of an outstanding classifier for electronic systems analysis (§2 ¶2).
Yang modified by Raichura does not disclose, wherein the field solver is FasterCap.
FastFieldSolvers discloses: wherein the field solver is FasterCap (p.1).
It would have been obvious before the effective It would have been obvious before the effective filing date to a person of ordinary skill in the art to modify the method of Yang modified by Raichura by incorporating the FasterCap technique of FastFieldSolvers. Both concern the art of capacitance extraction, and the incorporation would have, according to FastFieldSolvers, provided various advantages such as handling huge models, custom windowing techniques, faster data access, etc. (p.1).
For claim 2, Yang discloses the method of claim 1, as described above. Yang further discloses: wherein a size of an adaptive window is determined by reducing the coupling capacitance between an environmental conductor and the main conductor to 1% of the self-capacitance of the main conductor in a simulation experiment (§III.D ¶3).
For claim 3, Yang discloses the method of claim 1, as described above. Yang further discloses: wherein a structural model of three metal layers is considered when data representation is gridded, the main conductor is located in a center of a middle layer, and a number of conductors in each layer is not fixed (§III.D fig.10).
For claim 4, Yang discloses the method of claim 1, as described above. Yang further discloses: wherein a grid is uniformly divided, each conductor layer is represented as a vector x according to a density (§III.B: “Grid-based data representation” ¶1-2, fig.7), in which information of the main conductor and an environmental conductor is contained through the following encoding mode:
if the main conductor covers the ith grid, then xi=di+1 (§III.B ¶2);
if the environmental conductor covers the ith grid, then xi=-di (§III.B ¶2);
where, di represents a density of an extraction window (§III.B ¶1).
For claim 5, Yang discloses the method of claim 1, as described above. Yang further discloses: wherein the energy-efficient capacitance extraction method using XGBoost machine learning is realized by off-line training (§IV ¶1-3 gives overview of training mode, with the model being entirely trained on 90% of the data before using the remainder as test data, hence, the model method is trained prior to and is offline to the testing phase).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Koumura (US 20220147682 A1) discloses parasitic capacitance extraction via reinforcement learning, see abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LIANG LI whose telephone number is (303)297-4263. The examiner can normally be reached Mon-Fri 9-12p, 3-11p MT (11-2p, 5-1a ET).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor Jennifer Welch can be reached on (571)272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LIANG LI/
Primary examiner AU 2143