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 .
This action is in response to the amendment filed on 08/04/2026. Claims 1-12 and 14-20 are pending in the case.
Applicant Response
In Applicant’s response dated 08/04/2026, Applicant amended Claims 1, 19 and 20 and argued against all objections and rejections previously set forth in the Office Action dated 05/04/2026.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 08/04/2026 has been entered.
Examiner Comments
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 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.
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 1-12 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Dandy (Pub. No.: US 20210081592 B1, Pub. Date: 2021-03-18) in view of ASENOV (Pub. No. US 20170103153 A1, Pub. Date 2017-04-13) in further view of Wittenbach (US 20220067737 A1 2022-03-03)
Dandy teaches a method of generating a deep learning model (see Dandy: Fig.3, [0032], system 300 also includes a machine 310 that hosts a machine learning facility 312 configured to use supervised or unsupervised machine learning techniques.”), the method being performed by executing program code by at least one processor, the program codes being stored in computer readable media (see Dandy: Fig.3, [0032], “The computing device 320 has at least one processor 322, at least one input 324, and an output 326.”), the method comprising:
generating basic training data corresponding to a combination of device data and simulation result data using a [circuit simulation model ] (see Dandy: Fig.1, [0023], At 106, a training dataset that includes the simulation model component parameters ( i.e. device data) from operation 102 and the corresponding simulated values produced in operation 104 ( i.e. simulation result data) are provided as training data input to a machine learning facility.” … [0022], “The simulator accepts the input values and, at operation 104, uses a circuit simulator (i.e. compact model) to generate simulated output values that are stored in the particular instance that was used to generate the output values”), the [circuit simulation model ] configured to generate the simulation result data by performing a simulation based on the device data (see Dandy: Fig.1, [0014], “a dataset of generated values are combined into a simulation result value for each of the simulation model component parameters. The inputs to the simulation may include values for the parameters of the individual components in the circuit identified in operation 102, and the output of the simulation may include a very large dataset of individual instances of the inputs to the simulation along with simulated circuit outputs.”… [0028], “The measured data may be manipulated to account for measurement uncertainty and reflect a set of possible measurements. For example, if the DC gain of a certain measurement had a ±2% accuracy, then signals that were up to 2% higher and up to 2% lower could be stored as an additional training set or as additional instances of the original training set.”), the simulation result data indicating characteristics of a semiconductor device corresponding to the device data (see Dandy: Fig.1, [0022], The output values simulated by the simulator and stored in the dataset in operation 104 may include current, voltage, etc., as shown in Table 1 (i.e. characteristics of a semiconductor device). In some embodiments the simulated output values may also include waveforms of particular testing nodes of the network at particular time.”)
training the deep learning model based on the basic training data such that the deep learning model is configured to output (see Dandy: Fig.1, [0024], “After the machine learning facility receives its training data in operation 106, the trained network is created in operation 108. In operation 108, for example, a neural network may read the inputs from the first instance and generate a predicted outcome. Then the neural network compares its generated predicted outcome to the data used to create the simulated results, also included in the instance, and uses back propagation to modify weights and biases within the neural network so that its next prediction will be closer to the original data value.”):
prediction data indicating the characteristics of the semiconductor device (see Dandy: Fig.3, [0035], “The machine learning facility 312 may apply machine learning to the simulated values to generate a predicted value for each inputted parameter and compare the predicted value to its corresponding simulated value. The machine learning facility 312 may adjust the simulation model based on the comparison of the predicted to the simulated values.”);
Dandy does not teach the system wherein:
compact model configured to generate the simulation result;
a model uncertainty value indicating uncertainty of the prediction data caused by insufficiency of the training data;
a data uncertainty value indicating uncertainty of the prediction data caused by noises in the basic training data;
performing a first retraining when the model uncertainty value is larger than a model reference value, wherein the model reference value is based on a target performance of the trained deep learning model; and
performing a second retraining when the data uncertainty value is larger than a data reference value, wherein the data reference value is based on the target performance of the trained deep learning model.
However, ASENOV teaches a system wherein:
a compact model is configured to generate simulation result data indicating the characteristics of semiconductor device (see ASENOV: Fig.1, [0053], “Compact transistor models such as BSIM4 (Berkeley Short-channel IGFET Model 4) and BSIM-CMG (Berkeley Short-channel IGFET Model—Common Multi-Gate) are simplified physical models typically employed in circuit simulators, for example SPICE (Simulation Program with Integrated Circuit Emphasis), to model the behavior of semiconductor devices such as CMOS field effect transistors in integrated circuits.”)
simulation result data indicating the characteristics of semiconductor device corresponding to device data (see ASENOV: Fig.1, [0053], “The set of compact model parameters that specify the behavior of a particular semiconductor device are stored in a data structure called a model card, which is used as an input to a SPICE simulation process.” … [0061], “Step 204 comprises running ensemble Monte Carlo simulations of a plurality of semiconductor devices having a first plurality of configurations in the DoE space to produce EMC results. Thus EMC simulations of the semiconductor device are performed at each DoE node. In this example, the EMC simulations generate current-voltage (I-V) characteristics that are used for subsequent mobility parameter extraction.”)
Because both Dandy and ASENOV are in the same/similar field of semiconductor device characteristics simulation, accordingly, it would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of medium of Dandy to include the system, method and medium of wherein a compact model is configured to generate simulation result data indicating the characteristics of semiconductor device as taught by ASENOV. After modification of Dandy, the circuit simulation model simulate device data to generate a training data for the deep learning model. It can also incorporate the use of compact model to generate simulation result data as taught by as taught by ASENOV. One would have been motivated to make such a combination in order to provide effective and efficient semiconductor device production by increasing production speed and quality.
Dandy and ASENOV does not explicitly teach the system wherein:
a model uncertainty value indicating uncertainty of the prediction data caused by insufficiency of the training data;
a data uncertainty value indicating uncertainty of the prediction data caused by noises in the basic training data;
performing a first retraining when the model uncertainty value is larger than a model reference value, wherein the model reference value is based on a target performance of the trained deep learning model; and
performing a second retraining when the data uncertainty value is larger than a data reference value, wherein the data reference value is based on the target performance of the trained deep learning model.
However, Wittenbach teaches the system wherein :
a model uncertainty value indicating uncertainty of the prediction data caused by insufficiency of the training data (see Wittenbach: Fig.5, [0042], “A prediction with high epistemic uncertainty may prompt a data scientist to add the sample to a future training set to better fit the model. As noted herein, the epistemic uncertainty is the scientific uncertainty in the model parameters used to predict the process of interest and is due to limited data and/or knowledge. In other words, where the training data is deemed insufficient”); and
a data uncertainty value indicating uncertainty of the prediction data caused by noises in the basic training data (see Wittenbach: Fig.4, [0042], “the aleatoric uncertainty refers to the inherent uncertainty due to the probabilistic variability—i.e., driven by intrinsic uncertainty in the data, and therefore, newer/better data is requested from the potential customer.”);
performing a first retraining when the model uncertainty value is larger than a model reference value (see Wittenbach: Fig.1, [0039], “In calculating an epistemic uncertainty score, server system 120 may calculate an entropy value associated with each predicted value of the applied set of parameters 510. Then server system 120 may calculate an average of the calculated entropy values 520 and output the calculated average as the epistemic uncertainty score 530. If the epistemic uncertainty score is below a threshold, then server system 120 may continue to perform the prediction calculation 540. On the other hand, if the epistemic uncertainty score is above the threshold, server system 120 may modify the unlabeled data set by assigning a piece of data for human-assisted labeling responsive to the epistemic uncertainty score.”), wherein the model reference value is based on a target performance of the trained deep learning model (see ROTHSTEIN: Fig.1, [0039], “calculate an average of the calculated entropy values 520 and output the calculated average as the epistemic uncertainty score 530. If the epistemic uncertainty score is below a threshold, then server system 120 may continue to perform the prediction calculation 540. On the other hand, if the epistemic uncertainty score is above the threshold, server system 120 may modify the unlabeled data set by assigning a piece of data for human-assisted labeling responsive to the epistemic uncertainty score. In this regard, server system 120 may send the request for additional labeling to clients 105/110 for further labeling based on corrected prediction information 550. Moreover, server system 120 may then retrain the predictive model, using the human-assisted labeling, where the retraining is performed with a remainder of the unlabeled data set 560.”);
performing a second retraining when the data uncertainty value is larger than a data reference value (see Wittenbach: Fig.1, [0042], “server system 120 may generate an entropy value based on the average value 420 and output the entropy value as the aleatoric uncertainty score 430. If the aleatoric uncertainty score is determined to be below a threshold, then server system 120 may continue to perform the predictive model 440. Alternatively, if the aleatoric uncertainty score is determined to be above the threshold, then server system 120 may need to perform a modification of the generated parameters in order to modify the model itself. As noted herein, the aleatoric uncertainty reflects inherent issues with the model itself and a modification of the model may be required if the uncertainty score is above a threshold value. Moreover, once the parameters are modified, server system 120 may then apply the modified parameters to both labeled and unlabeled datasets. This is because the predictive model may need to be trained after the parameters are modified.”), the data reference value is based on the target performance of the trained deep learning model (see Wittenbach: Fig.1, [0038), “calculate an average prediction value of an overall output of the predictive model 410, the overall output including a prediction for each parameter of the set of parameters for example. Then, server system 120 may generate an entropy value based on the average value 420 and output the entropy value as the aleatoric uncertainty score 430. If the aleatoric uncertainty score is determined to be below a threshold, then server system 120 may continue to perform the predictive model 440. Alternatively, if the aleatoric uncertainty score is determined to be above the threshold, then server system 120 may need to perform a modification of the generated parameters in order to modify the model itself. As noted herein, the aleatoric uncertainty reflects inherent issues with the model itself and a modification of the model may be required if the uncertainty score is above a threshold value.”);
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of medium of Dandy to perform retraining of the model based on the value of the model uncertainty data that indicating uncertainty of the prediction data caused by insufficiency of the training data and data uncertainty value that indicate uncertainty of the prediction data caused by noises in the basic training data as taught by Wittenbach. After modification of Dandy, the circuit simulation model simulated device data to generate a training data for the deep learning model and can also incorporate the use of a model uncertainty value and a data uncertainty value to determine retraining of the model to produce efficient and accurate model as taught by Wittenbach. One would have been motivated to make such a combination in order to increase optimization of learning models by generating and analyzing extensive prediction data for diagnosis and prognosis for the device.
Regarding Claim 2,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 1. Dandy further teaches the method wherein:
the uncertainty data include a model uncertainty value indicating the uncertainty of the prediction data caused by insufficiency of the basic training data ( see Wittenbach: Fig.1, [0043], “(see Wittenbach: Fig.5, [0042], “A prediction with high epistemic uncertainty may prompt a data scientist to add the sample to a future training set to better fit the model. As noted herein, the epistemic uncertainty is the scientific uncertainty in the model parameters used to predict the process of interest and is due to limited data and/or knowledge. In other words, where the training data is deemed insufficient.”)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the invention, to modify the teaching of medium of Dandy to include the wherein the uncertainty data include a model uncertainty value indicating the uncertainty of the prediction data caused by insufficiency of the basic training data as taught by Wittenbach. One would have been motivated to make such a combination in order to increase optimization of learning models by generating and analyzing extensive prediction data for diagnosis and prognosis for the device.
Regarding Claim 3,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 2. Dandy further teaches the method wherein:
the retraining the deep learning model (see Dandy: Fig.1,0024, “The training continues progressing through all of the instances until the network is fully trained. In some embodiments the training makes multiple passes through the training database, as indicated through the optional loopback 109. In some embodiments the neural network may change the order of the instances as they appear in the training dataset to avoid training biases.”), includes:
comparing the model uncertainty value with a model reference value (see Dandy: Fig1, [0024], “Then the neural network compares its generated predicted outcome to the data used to create the simulated results, also included in the instance, and uses back propagation to modify weights and biases within the neural network so that its next prediction will be closer to the original data value.”)
generating addition training data using the compact model when the model uncertainty value is larger than the model reference value (see Dandy: Fig1, [0028], “Once these measurements are obtained, the measurements acquired in operation 202 may be applied to the trained learning network, in an operation 204, to predict or infer a set of revised simulation model parameters that better match the measured result than the original predicted model. In certain implementations, the measured data may be manipulated to account for measurement uncertainty and reflect a set of possible measurements”; and
retraining the deep learning model based on the addition training data, wherein the addition training data is different from the basic training data (see Dandy: Fig1, [0028], “The machine learning network could then be improved (i.e., updated or retrained) in an operation 206 with the additional instances or new training dataset as described above. The result after re-training the machine learning network in operation 206 would be a set of nominal model parameters, each of which would have a certain range of uncertainty due to measurement errors.”)
Regarding Claim 4,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 3. Dandy further teaches the method wherein:
retraining the deep learning model further (see Dandy: Fig.1, [0024], “The training continues progressing through all of the instances until the network is fully trained. In some embodiments the training makes multiple passes through the training database, as indicated through the optional loopback 109. In some embodiments the neural network may change the order of the instances as they appear in the training dataset to avoid training biases.”), includes:
determining an addition data range corresponding to a range of data such that the model uncertainty value is larger than the model reference value (see Dandy: Fig.1,[0028], “In certain implementations, the measured data may be manipulated to account for measurement uncertainty and reflect a set of possible measurements. For example, if the DC gain of a certain measurement had a ±2% accuracy, then signals that were up to 2% higher and up to 2% lower could be stored as an additional training set or as additional instances of the original training set.”)
Regarding Claim 5,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 1. Dandy further teaches the method wherein:
the addition training data correspond to a combination of the device data included in the addition data range (see Dandy: Fig.1, [0019], “the input parameters for the dataset may be bound within a range of values. For example, the values of the resistance parameter for each of the three resistors in the above example may be bound between 100 Ohms and 1000 Ohms when generating the training dataset.”), and the simulation result data (see Dandy: Fig.1, [0020], “the generation of the dataset produced in the operation 104, the simulation may create instances by varying the resistance values for each of the three resistors independently, as well as simulate and store output values for the various testing nodes within (or at the endpoints of) the resistor network.”)
Regarding Claim 6,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 3. Dandy further teaches the method wherein:
the deep learning model that has been trained based on the basic training data is further trained based on the addition training data (see Dandy: Fig.2, [0028], “for example, if the DC gain of a certain measurement had a ±2% accuracy, then signals that were up to 2% higher and up to 2% lower could be stored as an additional training set or as additional instances of the original training set. The machine learning network could then be improved (i.e., updated or retrained) in an operation 206 with the additional instances or new training dataset as described above.”)
Regarding Claim 7,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 1. Dandy further teaches the method wherein:
the uncertainty data includes a data uncertainty value, the data uncertainty value indicating the uncertainty of the prediction data caused by noises in the basic training data (see Dandy: Fig.2, [0028], “he result after re-training the machine learning network in operation 206 would be a set of nominal model parameters, each of which would have a certain range of uncertainty due to measurement errors.”)
Regarding Claim 8,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 7. Dandy further teaches the method wherein:
retraining the deep learning model (see Dandy: Fig.1, [0024], “The training continues progressing through all of the instances until the network is fully trained. In some embodiments the training makes multiple passes through the training database, as indicated through the optional loopback 109. In some embodiments the neural network may change the order of the instances as they appear in the training dataset to avoid training biases.”), includes:
comparing the data uncertainty value with the data reference value (see Dandy: Fig.3, [0035], “he machine learning facility 312 may apply machine learning to the simulated values to generate a predicted value for each inputted parameter and compare the predicted value to its corresponding simulated value. The machine learning facility 312 may adjust the simulation model based on the comparison of the predicted to the simulated values.”);
providing measurement data by measuring the characteristics of the semiconductor device, when the data uncertainty value is larger than the data reference value (see Dandy: Fig.3, [0033], “The input 324 may be structured to receive measurement data either directly from a measurement or data extracted from another measurement device. The input 324 may also be structured to receive data that has been previously stored or data from an information cloud.”);
correcting the compact model based on the measurement data (see Dandy: Fig.2, [0031], “If the physical embodiment, such as a circuit, is not working as expected, the updated simulation model may generate possible insight into what is different from the intended design, as well as which parameters may be contributing to these differences. The adjusted simulation model may also provide a validated starting point for design revisions.”);
generating updated training data using the corrected compact model (see Dandy: Fig.2, [0028], “e machine learning network could then be improved (i.e., updated or retrained) in an operation 206 with the additional instances or new training dataset as described above. The result after re-training the machine learning network in operation 206 would be a set of nominal model parameters, each of which would have a certain range of uncertainty due to measurement errors.”); and
retraining the deep learning model based on the updated training (see Dandy: Fig.2, [0028], “machine learning network could then be improved (i.e., updated or retrained) in an operation 206 with the additional instances or new training dataset as described above. The result after re-training the machine learning network in operation 206 would be a set of nominal model parameters, each of which would have a certain range of uncertainty due to measurement errors.”)
Regarding Claim 9,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 8. Dandy further teaches the method wherein:
retraining the deep learning model (see Dandy: Fig.1, [0024], “The training continues progressing through all of the instances until the network is fully trained. In some embodiments the training makes multiple passes through the training database, as indicated through the optional loopback 109. In some embodiments the neural network may change the order of the instances as they appear in the training dataset to avoid training biases.”), further includes:
determining a measurement data range corresponding to a range of data such that the data uncertainty value is larger than the data reference value ( see Dandy: Fig.1, [0039], “The machine learning network may be trained with data that replaces the nominal circuit with varying possible manufacturing defects (e.g., missing components, shorts across nets, incorrect parts, etc.). In such implementations, one can vary parameters across possible fault values in addition to normal tolerance ranges. The measured data can be fed into a trained neural network to infer failures on a production line based on what defect is likely to be present, for example. Such implementations facilitate automated or semi-automated troubleshooting and corrective action efforts.”
Regarding Claim 10,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 9. ASENOV further teaches the method wherein:
the characteristics of the semiconductor device corresponds to the device data included in the measurement data range ( see ASENOV: Fig.1, [0050], “a drift-diffusion model is calibrated using the results of the ensemble Monte Carlo simulation. This is done by using the EMC generated current-voltage characteristics for a particular transistor to determine values of the parameters of a mobility model that is incorporated in the drift-diffusion model of this particular transistor.”)
See the motivation to combine Dandy, and ASENOV in claim 1.
Regarding Claim 11,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 8. Dandy further teaches the method wherein:
the deep learning model that has been trained based on the basic training data is initialized (see Dandy: Fig.1, [0023], “At 106, a training dataset that includes the simulation model component parameters from operation 102 and the corresponding simulated values produced in operation 104 are provided as training data input to a machine learning facility. Table 1 may be considered an overly simplified training dataset for explanation purposes.”), and the initialized deep learning model is trained based on the measurement data (see Dandy: Fig.1, [0024], “after the machine learning facility receives its training data in operation 106, the trained network is created in operation 108. In operation 108, for example, a neural network may read the inputs from the first instance and generate a predicted outcome. Then the neural network compares its generated predicted outcome to the data used to create the simulated results, also included in the instance, and uses back propagation to modify weights and biases within the neural network so that its next prediction will be closer to the original data value.”)
Regarding Claim 12,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 1. Dandy further teaches the method wherein:
the model uncertainty value indicates the uncertainty of the prediction data caused by insufficiency of the basic training data (see Dandy: Fig.1, [0028] “Once these measurements are obtained, the measurements acquired in operation 202 may be applied to the trained learning network, in an operation 204, to predict or infer a set of revised simulation model parameters that better match the measured result than the original predicted model. In certain implementations, the measured data may be manipulated to account for measurement uncertainty and reflect a set of possible measurements.”), and the data uncertainty value indicates the uncertainty of the prediction data caused by noises of the basic training data (see Dandy: Fig.1, [0028], “The result after re-training the machine learning network in operation 206 would be a set of nominal model parameters, each of which would have a certain range of uncertainty due to measurement errors.”)
Regarding Claim 14,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 1. Dandy further teaches the method wherein:
whether to perform the first retraining is first determined based on the model uncertainty value, and when it is determined that the first retraining is not performed, subsequently determining whether to perform the second retraining is determined based on the data uncertainty value ( see Dandy: Fig.1, [0024], “back propagation to modify weights and biases within the neural network so that its next prediction will be closer to the original data value. In the example of Table 1, the inputs to the network are the simulated results (Node 1 Predicted voltage, Node 2 Predicted Voltage, etc.), and the predicted output is a predicted value for R1, R2, and R3, which is compared, during training, to the original values of R1, R2, and R3 that were used to create the simulated results. The training continues progressing through all of the instances until the network is fully trained. In some embodiments the training makes multiple passes through the training database, as indicated through the optional loopback 109. In some embodiments the neural network may change the order of the instances as they appear in the training dataset to avoid training biases.”)
Regarding Claim 15,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 1. Dandy further teaches the method wherein:
whether to perform the first retraining and whether to perform the second retraining are determined independently of each other (see Dandy: Fig.1, [0026], “The machine learning network could then be improved (i.e., updated or retrained) in an operation 206 with the additional instances or new training dataset as described above. The result after re-training the machine learning network in operation 206 would be a set of nominal model parameters, each of which would have a certain range of uncertainty due to measurement errors.”)
Regarding Claim 16,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 1. Dandy further teaches the method wherein:
the deep learning model includes a Bayesian Neural Network (BNN) ( see Dandy: Fig.1, [0025], “ Operation 108 may use supervised machine learning or unsupervised machine learning. Supervised machine learning as used herein generally refers to machine learning that is based upon training sets that contain labeled data. Unsupervised machine learning generally refers to ‘learning’ on training sets that contain mostly unlabeled data to train the neural network. The machine learning facility may apply a particular technique, such as a Bayesian approach, Random Forest, regression models, or classification models.”)
Regarding Claim 17,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 1. Dandy further teaches the method wherein:
wherein the device data indicates structure and an operation condition of the semiconductor device (see Dandy: Fig.1, [0053], “Example 7 is the method of any one of examples 1-6, in which the simulation model is a circuit simulation model, and wherein the circuit simulation model includes at least one component selected from the group consisting of: a resistor, a transistor, a capacitor, an inductor, a diode, an operational amplifier, a voltage source, a current source, and a transmission line”
the simulation result data and the prediction data indicate electrical characteristics of the semiconductor device (see Dandy: Fig.1, [0054], “the simulation model is a circuit simulation model, and wherein the at least one parameter is selected from the group consisting of: resistance, impedance, temperature coefficient, parasitic capacitance, transmission line length, transmission line width, material dielectric constant, and geometry.”), and
the device data is included in input data of the deep learning model (see Dandy: Fig.1, [0023], At 106, a training dataset that includes the simulation model component parameters ( i.e. device data) from operation 102 and the corresponding simulated values produced in operation 104 ( i.e. simulation result data) are provided as training data input to a machine learning facility.”),
Regarding Claim 18,
As shown above, Dandy, ASENOV and Wittenbach teaches all the limitations of claim 17. Dandy further teaches the method wherein:
the input data of the deep learning model further includes process data indicating a condition of manufacturing process of the semiconductor device (see Dandy: Fig.1, [0013], “the machine learning facility receives its training data in operation 106, the trained network is created in operation 108. In operation 108, for example, a neural network may read the inputs from the first instance and generate a predicted outcome. Then the neural network compares its generated predicted outcome to the data used to create the simulated results, also included in the instance, and uses back propagation to modify weights and biases within the neural network so that its next prediction will be closer to the original data value.”)
Regarding Claim independent 19,
Claim 19 is directed to a method claim and has similar/same claim limitation as claim 1 and is rejected under the same rationale.
Regarding Claim independent 20,
Claim 20 is directed to a computing device claim and has the same/similar claim limitations claim 1 and is rejected under the same rationale.
Response to Arguments
Claim Rejections - 35 U.S.C. § 103,
Applicant’s arguments with respect to claim amendments have been considered but are moot considering the new combination of references being used in the current rejection. The new combination of references was necessitated by Applicant’s claim amendments. Therefore, the claims are rejected under the new combination of references as indicated above.
Claim Rejections - 35 U.S.C. § 101,
Regarding the 35 U.S.C. 101 rejection for being directed non-statutory subject matter has been withdrawn based on applicant amendments and. Therefore, the 35 U.S.C. 101 rejection has been updated and sustained.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
PGPUB
NUMBER:
INVENTOR-INFORMATION:
TITLE / DESCRIPTION
US 20220036218 A1
ROTHSTEIN, EITAN
Title: Metrology and process control for semiconductor manufacturing
Description: a semiconductor metrology method is provided including collecting, using a spectrum acquisition tool and in accordance with a first measurement protocol, a baseline set of spectra on a first set of semiconductor wafer targets, collecting, using an optical metrology too
US 11494636 B1
Cihangir; Neslihan Kos
Title: Machine learning-based semiconductor manufacturing yield prediction system and method
Description: To predict a semiconductor manufacturing yield, measurement data obtained after main processes are used. That is, a yield may be predicted by collecting measurement data generated during main processes and comparing the data and past data.
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/Zelalem Shalu/Examiner, Art Unit 2145
/CESAR B PAULA/Supervisory Patent Examiner, Art Unit 2145
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