DETAILED ACTION
Claims 1, 2, 4-10 and 12-20 are presented for examination.
Claims 1 and 9 have been amended.
Claims 3 and 11 have been cancelled.
This office action is in response to the amendment submitted on 18-FEB-2025.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.
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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 02/18/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments – 35 USC 101
On pgs. 8 of the Applicant/Arguments Remarks (hereinafter ‘Remarks’), Applicant argues the amended claims have overcome the rejection under 35 USC 101.
Applicant’s arguments have been fully considered and are persuasive. The rejection has been withdrawn.
Response to Arguments – 35 USC 103
The amendments have been considered and upon further consideration, the rejection is maintained. Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
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 1-2, 5-10, and 13-20 are rejected under 35 U.S.C. 103 as being unpatentable over Guha et al. (US20180082826A1) in view of Malkani et al. (WO2019055576A1)
Regarding Claim 1, Guha teaches receiving, from a plurality of sensors, sensor data
associated with processing a substrate via a processing chamber of substrate processing equipment
([0015] “A plurality of sensors of the plasma reactor is included, and each of the plurality of sensors is configured to produce a data stream of information during operation of the plasma reactor for carrying out the plasma process”).
the sensor data comprises a first subset received from one or more first sensors and a second subset received from one or more second sensors ([0050] “As shown, the sensors 132 may include one or more of an optical emission spectrometry (OES) sensor, a pressure sensor, a voltage sensor, a current sensor, a temperature sensor, a flow rate sensor, frequency sensor, a power sensor, a metrology sensor, and combinations of two or more thereof. As an example, the following table A illustrates example information that can be obtained from various sensors of a plasma reactor.” EN: The sensors 132 include multiple types of data, each establishing a subset of distinct physical sensors and corresponding data).
the first subset being mapped to the second subset ([0074] “signals, will be classified and correlated in order of relevance to represent Processing State of the reactor. For example, capacitor tuning position in a match system (i.e., coupled to an RF power source) could be correlated with Optical Emission Spectrum (OES) from the plasma and can be correlated together. In a similar way we can define different orders of correlation, with higher order defining strong correlation and lower order defining weak correlation of sensor signals.” [0084] describes using the ML model to create the correlation between the first subset and the second subset. The first subset includes the OES sensor, and the second includes the capacitor position.)
wherein the first subset comprises one or more first types of data and the second subset comprises one or more second types of data that are different from the one or more first types of data (Please see [0050] and table A. The examiner picks any identifiable stream, like OES sensor, to be in the first subset and any remaining identifiable stream, like the pressure and capacitor position, to be in the second subset.)
wherein the second subset comprises chamber component temperature data corresponding to the first subset ([0066] “the data produced by the phenomenological model 184 may be characterized in the form of any one of the outputs produced by the sensors 132, as described with reference to FIG. 1,” and [0050] “the sensors 132 may include one or more of an optical emission spectrometry (OES) sensor, a pressure sensor, a voltage sensor, a current sensor, a temperature sensor”).
inputting, into a180 will be receiving periodic information from etch rate analysis, which may be performed after one or more substrates are tested using a metrology tool. Similar processes can be performed with a monitor wafer, which is configured to approximate the type of processing desired to be executed by the plasma reactor 100…machine learning engine 180 may be provided with information regarding reactor wall surface dynamics 182. This information may include data regarding the inferred characteristics of the chamber wall surfaces, as they change during processing” This describes the data representing the operating space of the chamber).
wherein the model input data comprises the one or more first types of data ([0065] “As an optional refinement input to the machine learning engine 180 input from a phenomenological model 184 is used, that approximates the behavior of the plasma within the chamber, given the reactor wall surface dynamics 182. The phenomenological model 184, in one embodiment, is used to approximate the nature of the chemical reactions that are occurring within the processing volume, and associated interactions with the reactor wall surfaces. Broadly speaking, a phenomenological model is sometimes referred to as a statistical model, as it is a mathematical expression that relates several different empirical observations of phenomena to each other. This relation is consistent with fundamental theory, but is not directly derived from theory. Thus, a phenomenological model does not attempt to explain why the variables in the plasma (i.e., when chemical bonds break to define different chemical species or when the recombine to define a different chemical form upon coming into contact with a surface in the reactor, e.g., a chamber wall). Generally, the phenomenological model 184 is configured to characterize the anticipated chemical kinetics of gases in the plasma of the plasma reactor, and their behavior relative to the reactor wall surface dynamics 182. These kinetics may include, for example, electron collision reactions, wall recombination reactions, wall loss reactions, etc., for different chemistries. Thus, this model simply attempts to describe the relationship, with the assumption that the relationship extends past the measured values. The phenomenological model 184 is configured to produce input to the machine learning engine 180 that is in terms of sensor output. That is, the characterization of the plasma behavior by the phenomenological model 184 is configured to produce input data to the machine learning engine 180 in the form of information similar to that which can be captured by a sensor coupled to the plasma reactor 100.”)
wherein the model output data comprises the one or more second types of data ([0066] and [0067] “Thus, it can be said that the phenomenological model 184 provides input to the machine learning engine 180 in the form of or in terms of sensor output data. The example provided above with respect to OES sensor data is just one example, in the same type of modeling that can be provided for other types of sensor data, such as sensors associated with capacitance, voltage, current, or other measurement characteristics produced by actual sensors that are coupled to the plasma reactor 100.” The phenomenological model’s output (used for the ML input) comprises capacitance, which is from the second type of data as mapped earlier.)
Guha however, does not appear to explicitly teach receiving, as output from the physics-based model, model output data based on the model input data input into the physics-based model
the one or more calibration parameters are to be used by the physics-based model to perform one or more corrective actions associated with the processing chamber.
Malaki teaches receiving, as output from the physics-based model, model output data based on the model input data input into the physics-based model (Fig. 7D shows the input and output to the model, and [0123] “i) Melt pool width: a high-fidelity physics simulation model that predicts the rapid solidification rate of the metal while the laser scans a particular region. The predicted widths are compared to experimental measurements for the width of melt pools at various operating conditions”).
the one or more calibration parameters are to be used by the physics-based model to perform one or more corrective actions associated with the processing chamber ([0123] “(D) simulating, by the at least one processor, based on at least one AM simulation model, at least one testing adjustment to at least one of i) the one or more initial operational parameters of the AM machine or ii) the one or more initial AM build process parameters, when the first AM testing layer fails the compliance to the one or more predefined criteria;” and “(E) causing, by the at least one processor, the AM machine to implement the at least one testing adjustment in the AM build process”).
Guha and Malkani are analogous art because they are from the same field of endeavor in modelling and simulation using machine learning, more specifically covering manufacturing, substrates and semiconductors. Guha covers calibration, modeling and simulation for semi-conductors, while Malkani covers digital twins, calibration, modeling and simulation for manufacturing processes. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Guha and Malkani to arrive at using physics based models to calibrate the substrate chamber while avoiding their possible deficiencies. “Typically, physics-based simulation models are inaccurate relative to reality….the exemplary inventive simulation models of the present disclosure are calibrated or "tuned" to existing experimental data to at least reduce the impact of the above mentioned sources of inaccuracy.” (Malkani [0094-0095])
Regarding Claim 2, Guha in view of Malkani teaches the method of claim 1. Guha further teaches the first subset of the sensor data comprises one or more of spacing data, chamber pressure data, heater temperature data, or chamber flow rate data ([0050] “the sensors 132 may include one or more of an optical emission spectrometry (OES) sensor, a pressure sensor, a voltage sensor, a current sensor, a temperature sensor, a flow rate sensor, frequency sensor, a power sensor, a metrology sensor, and combinations of two or more thereof”).
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Regarding Claim 5, Guha in view of Malkani teaches the method of claim 1. Malkani further teaches the physics-based model comprises a digital twin model that is used to update processing parameters of the processing chamber ([0004] “ the initial digital twin comprises at least one of… (D) simulating, by the at least one processor, based on at least one AM simulation model... wherein the at least one testing adjustment is configured to compensate at least one difference in the actual AM part from the desired design due to the first AM testing layer; (E) causing, by the at least one processor, the AM machine to implement the at least one testing adjustment in the AM build process.” The simulation model is specified as physics based in [0123] “a high-fidelity physics simulation model” ).
Regarding Claim 6, Guha in view of Malkani teaches the method of claim 1. Malkani further teaches wherein responsive to the one or more calibration parameters being tuned, the model input data and the one or more calibration parameters are input to the physics-based model to generate updated model output data, wherein the updated model output data is used to perform the one or more corrective actions ([0142] Describes the physics based model simulations running and driving the calibration of the manufacturing process. Step (F) is iterative, “(F) repeating (A) through (E),” until the calibration process is completed including the feedback loop for the input data and the calibration parameters).
Regarding Claim 7, Guha in view of Malkani teaches the method of claim 1. Guha further teaches the one or more corrective actions comprise one or more of: providing an alert; interrupting operation of the processing chamber; or updating manufacturing parameters of the processing chamber ([0039] “The machine learning engine operates a mathematical model that is refined over time and is able to learn and correct not only the desired processing state values but also the compensation variables and its magnitude which upon translation into physical variables can be used as tuning knobs to physical controls, values, settings of a plasma reactor”).
Regarding Claim 8, Guha in view of Malkani teaches the method of claim 1. Malkani further teaches comprising assigning a range of values to the one or more calibration parameters prior to training the machine learning model, wherein tuning of the one or more calibration parameters comprises adjusting corresponding values of the one or more calibration parameters within the range of values ([0113] “1) Digital twin uncertain parameters (i) which may be calibrated via Kennedy and O'Haggan methodology;
2) AM machine response status in time (j); and
3) AM machine hardware knobs that are calibrated via optimization to match a desired response range (k).” The range (k) is the set of acceptable values the calibration is constrained by. This constrains the data before the model is trained).
Claim 9-10, and 13-15 recite limitations similar to claims 1-2, and 5-7 respectively and are rejected under the same rationale.
Regarding Claim 16, Guha teaches a non-transitory machine-readable storage medium storing instructions which, when executed cause a processing device to perform operations comprising ([0131] “The computer system includes a central processing unit (CPU) 804, which is coupled through bus 810 to random access memory (RAM) 806, read-only memory (ROM) 812, and mass storage device 814. System controller program 808 resides in random access memory (RAM) 806, but can also reside in mass storage 814”).
The remaining limitations are similar to claim 1 and are rejected under the same rationale.
Claim 17-20 recite limitations similar to claims 3,5,6, and 8 respectively and are rejected under the same rationale.
Claims 4 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Guha et al. (US20180082826A1) in view of Malkani et al. (WO2019055576A1) and further in view of Hsu et al. (WO2021113332A1).
Regarding Claim 4, Guha in view of Malkani teaches the method of claim 1. Hsu further teaches the calibration parameters comprise one or more of predicted thermal contact resistance values or predicted thermal contact conductance values between corresponding components of the processing chamber (Fig 11 illustrates the testing system of thermal contact conductance and Fig 12 shows the calibration of the system of Fig 11. [0114] “Fig. 11 shows the equipment to quantitatively characterize the thermal contact conductance,” and [0117] “Calibration of the testing apparatus was performed every time before measurement. As shown in Fig. 12 A, a heater was put on the top surface of copper plate which is covered by a layer of polyurethane foam of 3 cm thick to avoid heat loss. The other part is the same with testing system. Fig. 12B shows the voltage reading of the heat flux sensor when different heating power is supplied” ).
Guha, Malkani, and Hsu are analogous art because they are from the same field of endeavor in modelling and simulation using machine learning, more specifically covering manufacturing, substrates and semiconductors. Guha covers calibration, modeling and simulation for semi-conductors, while Malkani covers digital twins, calibration, modeling and simulation for manufacturing processes. Before the effective filing date of the invention, it would have been obvious to a person of ordinary skill in the art, to combine Guha, Malkani to arrive at measuring the thermal conductance in wafter production. “suppression of thermal contact resistance and the engineering of surface morphology and optical property. Building energy simulation shows that the dual-mode device, if widely deployed in the United States, can save 19.2% heating and cooling energy, which is 1.7 times higher than cooling-only and 2.2 times higher than heating-only approaches.” (Hsu [0068])
Claim 12 recites limitations similar to claims 4 and is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US11586789B2: Discloses optimizing substrate production in production chambers employing machine learning.
US20230013919A1: Discloses machine learning and process model calibration in the production of substrates.
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/A.E.D./Examiner, Art Unit 2187
/LEWIS A BULLOCK JR/Supervisory Patent Examiner, Art Unit 2199