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 .
Note on Prior Art
There are no prior art references that teach or make obvious feeding a neural network the deviation value of the full width at half maxima of seed crystal diffraction peak… and a deviation value of the thermal resistance value of insulating layer… Standard deviation measurements are known. Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials by Dropka et al teaches feeding measurement to a neural network for crystal growth. Growth and physical characterization of high resistivity Fe: β-Ga2O3 crystals by Zhang et al teaches full width at half maxima of seed crystal diffraction peak in section 3, “The rocking curve is symmetrical with the full-width at half-maximum of 118.5 arcsec, indicating that the crystal has a good crystal quality without sub-grain boundaries.” The arcsecs are the value of the full width at half maxima of seed crystal diffraction peak. Optimization of thermal field of 150 mm SiC crystal growth by PVT method by Zhang et al (Zhang2) abstract teaches “the effects of thermal insulation adjustment of crystal growth thermal fields, application of seed crystals with different diameters, and shelter structure on the crystal growth process were also studied.” The Thermal resistance value of the crucible is taught in a table in Zhang2, see below,
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CN102392298A (‘98A) teaches varying the thickness of the thermal insulator, “The thickness of thermal insulator 2 is 2～10mm…” This would change the thermal resistance of the thermal insulator.
However, neither ‘98A, Zhang2 nor Zhang teach measuring a standard deviation of their respective values. Further, Zhang2 is not prior art. Therefore, the prior art of record does not teach or make obvious claims 13-14.
A similar gap exists in the art with respect to the claimed deviation value of the predicted full width at half maxima of diffraction peak, an axial deviation value of the predicted full width at half maxima of diffraction peak, a radial deviation value of the predicted resistivity. Therefore, the prior art of record does not teach or make obvious claim 15.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 11-15 and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea of a mental concept without significantly more. The claims recite collecting data, preprocessing data and inputting the preprocessed data into a neural network to predict resistivity in a gallium oxide crystal. This judicial exception is not integrated into a practical application because the additional elements such as various types of data “simply an attempt to limit the use of the abstract idea to a particular technological environment”. MPEP 2106.05(h) example vi. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because additional elements such as a processor and storage media are generic computer parts.
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.
Claims 11, 12 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Application of Artificial Neural Networks in Crystal Growth of Electronic and Opto-Electronic Materials by Dropka et al, US3898051A to Schmid, Growth and physical characterization of high resistivity Fe: β-Ga2O3 crystals by Zhang et al and US20190385047A1 to Lei et al. Proof that Zhang was published in July 2020 is attached to this office action as “Issue 8 - Volume 29 - Chinese Physics B – IOPscience”.
Dropka teaches claim 11. A prediction method of high resistance [crystals] (Dropka abs “application of artificial neural networks (ANNs) in the crystal growth of electronic and opto-electronic materials. The main reason for using ANNs is to detect the patterns and relationships in non-linear static and dynamic data sets which are common in crystal growth processes, all in a real time. The fast forecasting is particularly important for the process control, since common numerical simulations are slow and in situ measurements of key process parameters are not feasible.” And Fig. 1 below)
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obtaining a preparation data of a high resistance gallium oxide single crystal, the preparation data comprising a seed crystal data, an environmental data, a control data, and a raw material data, the control data (Dropka p. 9 “Resulting feed-forward ANN with 4 hidden layers … able to correlate 11 inputs (boundary temperatures, seed rotation rates, sizes of the crucible and seed and spatial coordinates (r,z) of 400 points in the axisymmetric computational domain) with 3 outputs (flow velocity components (radial ur, axial uz) and chemical composition of the solution in the points in the computational domain shown in Figure 7b.”)
inputting the (Dropka p. 9 “Resulting feed-forward ANN with 4 hidden layers … able to correlate 11 inputs (boundary temperatures, seed rotation rates, sizes of the crucible and seed and spatial coordinates (r,z) of 400 points in the axisymmetric computational domain) with 3 outputs (flow velocity components (radial ur, axial uz) and chemical composition of the solution in the points in the computational domain shown in Figure 7b.”)
Dropka doesn’t teach seed coolant flow rate.
However, Schmid teaches control data comprising a seed crystal coolant flow rate. (Schmid 4:20 “relatively small flow of helium through the heat exchanger (typically at a rate of about 40 c.f.h.) will prevent the seed crystal from melting.”)
Dropka, Schmid and the claims are all directed to crystal growth. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to add coolant flow rate to the control data because the coolant flow rate “will prevent the seed crystal from melting.” Id.
Dropka doesn’t use gallium oxide.
However, Zhang teaches high resistance gallium oxide… (Zhang title “Growth and physical characterization of high resistivity Fe: β-Ga2O3 crystals”) the raw material data comprising a doping type data and a doping concentration… (Zhang sec. 3.1 “The diameter of 0.02 mol% Fe: β-Ga2O3 crystal is about 6 mm, the length is about 20 mm, and the analysis of GDMS shows that the actual doping concentration of Fe ion is 17 µg/g.” The type is Fe. The concentration is 17 µg/g.)
Dropka, Zhang and the claims all grow crystals. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to use gallium oxide because “The β-gallium oxide (β-Ga2O3) crystal has become an increasingly attractive semiconductor for the potential applications in the fields of high-power devices, solar blind ultra violet photodetectors, and Schottky x-ray detectors[1–3] due to its outstanding material properties.” Zhang sec. 1 introduction.
Dropka doesn’t teach preprocessing input data.
However, Lei teaches preprocessing the preparation data to obtain a preprocessed preparation data; and
inputting the preprocessed preparation data… (Lei abs. “Measurements of test transistors are gathered into training data including gate and drain voltages and transistor width and length, and target data such as the drain current measured under the input conditions. The training data is converted by an input pre-processor that can apply logarithms of the inputs or perform a Principal Component Analysis (PCA). Rather than use measured drain current as the target when training the deep neural network, a target transformer transforms the drain current into a transformed drain current, such as a derivative of the drain current with respect to gate or drain voltages, or a logarithm of the derivative.” Lei title “Semiconductor Device Modeling Using Input Pre-Processing and Transformed Targets for Training a Deep Neural Network”)
Lei, Dropka and the claims are directed to semiconductor manufacturing. It would have been obvious to a person having ordinary skill in the art, at the time of filing, to preprocess the input in order to build a “desired… model… that accurately models currents over a wide range, including the sub-threshold region.” Lei para 17.
Dropka teaches claim 12. The prediction method of high resistance gallium oxide based on deep learning and heat exchange method according to claim 11, wherein preprocessing the preparation data to obtain a preprocessed preparation data comprises:
obtaining a (Dropka p. 9 “Resulting feed-forward ANN with 4 hidden layers … able to correlate 11 inputs (boundary temperatures, seed rotation rates, sizes of the crucible and seed and spatial coordinates (r,z) of 400 points in the axisymmetric computational domain) with 3 outputs (flow velocity components (radial ur, axial uz) and chemical composition of the solution in the points in the computational domain shown in Figure 7b.” Eleven inputs are at least a vector, and a vector is a type of matrix.)
Dropka doesn’t teach a matrix of preprocessed data.
However, Lei teaches preprocessing the preparation data to obtain a preprocessed preparation data comprises:
obtaining a preprocessed preparation data according to the … the preprocessed preparation data is a matrix… (Lei para 33 “Input pre-processor 40 can perform various pre-processing on training data 34, such as obtaining the natural logarithm of input voltages −ln(Vgs), ln(Vgs). Principal Component Analysis (PCA) may be performed by input pre-processor 40 operating on training data 34 to obtain the principal variables that most strongly impact the transform of the drain current. PCA detects which input variables most strongly impact the drain current. PCA may use the eigenvector of the covariance matrix to reduce the variable dimensions.” The matrix is the natural log of the input voltages and the PCA. Even if the matrix in Lei is a vector, a vector is a matrix.)
Dropka teaches claim 20. A high resistance gallium oxide preparation system based on deep learning and heat exchange method, comprising a non-transitory memory and a processor, a computer program is stored in the non-transitory memory, and the processor executes the computer program to operate the steps of the prediction method according to claim 11. (Dropka abs “application of artificial neural networks (ANNs) in the crystal growth of electronic and opto-electronic materials. The main reason for using ANNs is to detect the patterns and relationships in non-linear static and dynamic data sets which are common in crystal growth processes, all in a real time. The fast forecasting is particularly important for the process control, since common numerical simulations are slow and in situ measurements of key process parameters are not feasible.” And Fig. 1 below. Dropka p.1 “Machine learning is a subarea of AI that attempts to imitate with computer algorithms the way in which humans learn from previous experience.”)
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Conclusion
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/AUSTIN HICKS/Primary Examiner, Art Unit 2142
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