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 .
A Preliminary Amendment was filed on July 15, 2024 providing Replacement Sheets of FIGS. 2, 3A, 3B, and 3C.
Claims 1-20 are pending, of which claims 1, 14, and 17 are independent claims.
Information Disclosure Statement
The references cited in the information disclosure statement (IDS) submitted on February 11, 2026 has been considered by the examiner.
Claim Objections
The following claims are objected to for lack of antecedent support or for redundancies. The Examiner recommends the following changes:
Claim 1, line 2, insert “wherein” before “the first data set”.
Claim 1, line 3, replace “comprising” with “comprises”.
Claim 1, line 10, insert “wherein” before “the second”.
Claim 1, line 11, replace “comprising” with “comprises”.
Claim 5, line 3, insert “of the” before “process”.
Claim 6, line 3, insert “and” after “data;”.
Claim 8, line 2, insert “the” before “second”.
Claim 8, line 6, insert “the” before “second”.
Claim 14, line 4, insert “wherein” before “the first data set”.
Claim 14, line 5, replace “comprising” with “comprises”.
Claim 14, line 13, insert “wherein” before “the second”.
Claim 14, line 14, replace “comprising” with “comprises”.
Claim 17, line 3, insert “wherein” before “the first data set”.
Claim 17, line 4, replace “comprising” with “comprises”.
Claim 17, line 11, insert “wherein” before “the second”.
Claim 17, line 12, replace “comprising” with “comprises”.
Claim 20, line 4, insert “and” after “data;”.
Appropriate correction is respectfully requested.
Claim Rejections - 35 USC § 102
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.
Claims 1-3, 5, 10, 11, 12, 14, 15, 17, and 18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Shankaramurthy et al. (US Patent Publication No. 2023/0014145 A1) (“Shankaramurthy”).
Regarding independent claim 1, Shankaramurthy teaches:
A method, comprising: Shankaramurthy: Abstract (“A method includes identifying a recipe for depositing layers on a substrate in a processing chamber of a substrate processing system. The recipe comprises iterations of a set of one or more processes, and wherein each iteration of the iterations is for depositing at least one layer of the layers. The method further includes determining changes to parameters for depositing the at least one layer on the substrate. Each of the changes corresponds to a respective iteration of the iterations and is associated with a relative position of a corresponding layer. The layers are to be deposited on one or more substrates based on the recipe and the changes.”)
receiving a first data set for a first process run of a process recipe, the first data set comprising first process parameter values for process parameters of the process recipe, first performance data of the process recipe performed on a first substrate using the first process parameter values, and target performance data of the process recipe; Shankaramurthy: Paragraph [0033] (“Recipe 150 includes a set of processes 152 that includes manufacturing parameters 154. Recipe 150 further includes iterations 156 (e.g., loops, repetitions of the set of processes 152). Manufacturing parameters 154 include … process parameters (e.g., temperature, pressure, flow, rate, electrical current, voltage, gas flow, lift speed, etc.).”) Shankaramurthy: Paragraph [0039] (“Recipe 150 and metrology data 172 are used as input into the trained machine learning model and iteration adjustments 162 and multipliers 164 (e.g., offset table 160) is determined from the output of the trained machine learning model.”) Shankaramurthy: Paragraph [0048] (“The iteration adjustments 162 are used to cause the layers of the substrate to be uniform (e.g., same thickness, same interlayer spacing, etc.).”) Shankaramurthy: Paragraph [0054] (“Referring to FIG. 2A, at block 210 of method 200A, processing logic identifies a recipe including iterations (e.g., loops) of a set of process to deposit layers on a substrate (e.g., in a processing chamber of a substrate processing system). Each iteration is for depositing at least one layer of the layers on the substrate. The set of processes includes manufacturing parameters (e.g., gas flow rate, HFRF power, LFRF power, lift spacing (distance), chamber pressure, bottom tuner parameters, inner zone heater temperature, etc.) for depositing the at least one layer on the substrate.”) Shankaramurthy: Paragraph [0072] (“Referring to FIG. 2B, at block 220 of method 200B, processing logic deposits first layers of a first type on a first substrate. In some examples, the processing logic executes first processes (that include manufacturing parameters) to cause five layers of oxide to be deposited on a first substrate.”) [The manufacturing parameters of a recipe initially performed reads on “receiving a first data set for a first process run of a process recipe”. The process parameters of the manufacturing parameters for a first layer of the layers deposited reads on “the first data set comprising first process parameter values for process parameters of the process recipe”. The metrology data of the first layer deposited based on the recipe initially performed reads on “first performance data of the process recipe performed on a first substrate using the first process parameters”. The adjustments and multipliers to obtain uniform layers of specific thickness read on “target performance data of the process recipe”.]
processing the first data set using a dynamic controller to determine second process parameter values for the process parameters of the process recipe, wherein the second process parameter values differ from the first process parameter values for at least one process parameter; Shankaramurthy: Paragraphs [0039], [0054], and [0072] [As described above.] Shankaramurthy: Paragraph [0025] (“In some embodiments, server 110 is a controller of a semiconductor processing system and is used to control the manufacturing equipment 124.”) Shankaramurthy: Paragraph [0048] (“The iteration adjustments 162 indicate an additive operation (e.g., addition, subtraction) for a manufacturing parameter 154 (e.g., corresponding to a process of a set of processes 152) for each of the iterations 156... The iteration adjustments 162 are used to cause the layers of the substrate to be uniform (e.g., same thickness, same interlayer spacing, etc.).”) Shankaramurthy: Paragraph [0056] (“In some embodiments, the recipe has iterations (e.g., loop information) to loop from a first operation to a second operation for a loop count.”) Shankaramurthy: Paragraph [0073] (“At block 222, processing logic determines first metrology data (e.g., variations layer to layer) of the first layers. In some examples, the processing logic determines the thickness of each layer of the five layers of oxide.”) Shankaramurthy: Paragraph [0074] (“At block 224, processing logic generates, based on the first metrology data, first iteration adjustments. In some embodiments, the first iteration adjustments are updates to manufacturing parameters of the first processes used to cause the five layers of oxide to be deposited on the first substrate.”) Shankaramurthy: Paragraph [0076] (“At block 226, processing logic deposits second layers of a second type on a second substrate. In some examples, the processing logic executes second processes (that include manufacturing parameters) to cause five layers of nitride to be deposited on a second substrate (different from the first substrate where the five layers of oxide were deposited).”) Shankaramurthy: Paragraph [0077] (“At block 228, processing logic determines second metrology data of the second layers.”) Shankaramurthy: Paragraph [0078] (“At block 230, processing logic generates, based on the second metrology data, second iteration adjustments.”) [The generating of the first iteration adjustments or updates to the manufacturing parameters reads on “processing the first data set using a dynamic controller to determine second process parameter values for the process parameters of the process recipe”. The updated process parameters of the manufacturing parameters for another layer of the layers deposited, whether it is for the first layers of a first type or the second layers of a second type, reads on “the second process parameter values differ from the first process parameter values for at least one process parameter”.]
receiving a second data set for a second process run of the process recipe, the second data set comprising the second process parameter values, second performance data of the process recipe performed on a second substrate using the second process parameter values, and the target performance data of the process recipe; and Shankaramurthy: Paragraphs [0039], [0048], [0054], [0056], [0072]-[0074], and [0076]-[0078] [As described above.] Shankaramurthy: Paragraph [0057] (“At block 212, processing logic determines iteration adjustments to cause uniformity (e.g., each layer has the same thickness, each layer is uniform, each set of adjacent layers has the same interplanar spacing, etc.) of the layers of the substrate. Each iteration adjustment corresponds to a respective iteration of the set of processes. Each iteration adjustment updates one or more of the manufacturing parameters of the respective iteration by performing one or more additive operations (e.g., add a value, add a negative value, add zero, etc.).”) [The updates of the manufacturing parameters of the recipe initially performed reads on “receiving a second data set for a second process run of a process recipe”. The updated or adjusted process parameters of the manufacturing parameters for another layer of the layers deposited reads on “the second data set comprising second process parameter values for process parameters of the process recipe”. The metrology data of the second layer deposited based on the recipe performed reads on “second performance data of the process recipe performed on a second substrate using the second process parameters”. The adjustments and multipliers to obtain uniform layers of specific thickness read on “the target performance data of the process recipe”.]
responsive to determining that the second performance data satisfies one or more criteria with respect to the target performance data, saving the second process parameter values as final process parameter values for the process recipe. Shankaramurthy: Paragraphs [0039], [0054], [0056], [0072]-[0074], and [0076]-[0078] [As described above.] Shankaramurthy: Paragraph [0068] (“The processing logic takes the default setpoint (e.g., default manufacturing parameters) from the recipe operation, adds the offsets (e.g., iteration adjustments) from the arrays (e.g., and multiplies the resulting sums by the corresponding multipliers) and stores the resulting values (e.g., back into vectors).”) [The updated manufacturing parameters that generate the uniform layer reads on “responsive to determining that the second performance data satisfies one or more criteria with respect to the target performance data”.]
Regarding claim 2, Shankaramurthy teaches all the claimed features of claim 1, from which claim 2 depends. Shankaramurthy further teaches:
The method of claim 1, further comprising: responsive to determining that the second performance data does not satisfy the one or more criteria with respect to the target performance data, processing the second data set using the dynamic controller to determine third process parameter values for the process parameters, wherein the third process parameter values differ from the second process parameter values for at least one process parameter. Shankaramurthy: Paragraphs [0039], [0054], [0056], [0072]-[0074], and [0076]-[0078] [As described in claim 1.] [Repeating the iteration adjustment of the updated manufacturing parameters until the layer is uniform reads on “responsive to determining that the second performance data does not satisfy the one or more criteria with respect to the target performance data, processing the second data set using the dynamic controller to determine third process parameter values for the process parameters”.]
Regarding claim 3, Shankaramurthy teaches all the claimed features of claim 1, from which claim 3 depends. Shankaramurthy further teaches:
The method of claim 1, wherein for at least some use cases at most three process runs are performed, each being based on a unique set of process parameter values output by the dynamic controller, before the final process parameter values for the process recipe are determined. Shankaramurthy: Paragraphs [0039], [0054], [0056], [0072]-[0074], and [0076]-[0078] [As described in claim 1.] [A given iteration of putting down a layer and then adjusting the manufacturing parameters for the proceeding layer reads on “for at least some use cases at most three process runs are performed”.]
Regarding claim 5, Shankaramurthy teaches all the claimed features of claim 1, from which claim 5 depends. Shankaramurthy further teaches:
The method of claim 1, wherein the first data set further comprises one or more constraints for process parameter values of one or more process parameters for the process recipe, wherein the one or more constraints indicate at least one of a) which process parameter values are adjustable, b) maximum permitted process parameter values for the one or more process parameters, or c) minimum permitted process parameter vales for the one or more process parameters. Shankaramurthy: Paragraph [0050] (“FIG. 1D is a tolerance table 170 for use with iteration adjustments and multipliers (e.g., offset table 160, iteration adjustments 162, and/or multipliers 164 of FIGS. 1A and 1C), according to certain embodiments. In some embodiments, the tolerance table 170 is indicative of one or more of a default value (e.g., “Default”), a minimum value (e.g., “Min”), and/or a maximum value (e.g., “Max”) of a manufacturing parameter 154. In some examples, manufacturing parameter 154A has a default value of 10, a minimum value of 0, and a maximum value of 100. In some examples, manufacturing parameter 154B has a default value of 50, a minimum value of 0, and a maximum value of 100. In some examples, manufacturing parameter 154C has a default value of 20, a minimum value of 0, and a maximum value of 50.”) Shankaramurthy: Paragraph [0051] (“In some embodiments, the variable loop control component 114 adds an iteration adjustment 162 to a manufacturing parameter 154 to generate a sum and multiplies the sum by a multiplier 164 to generate a product. The product is compared to the tolerance table 170. Responsive to the product not being below the minimum value and not being above the maximum value, the product is used. Responsive to the product being below the minimum value, the minimum value is used. Responsive to the product being above the maximum value, the maximum value is used. In some embodiments, responsive to the product being below the minimum value or above the maximum value, a corrective action is performed (e.g., an alert is sent to the client device 120, the execution of the recipe 150 is stopped, the manufacturing equipment 124 is interrupted, etc.).”) [The maximum, minimum, and/or default values of the manufacturing parameters for the first layer 154A reads on “wherein the first data set further comprises one or more constraints for process parameter values of one or more process parameters for the process recipe, wherein the one or more constraints indicate at least one of … b) maximum permitted process parameter values for the one or more process parameters, or c) minimum permitted process parameter vales for the one or more process parameters.”]
Regarding claim 10, Shankaramurthy teaches all the claimed features of claim 1, from which claim 10 depends. Shankaramurthy teaches:
The method of claim 1, further comprising: receiving a selection of one or more process parameters that are to be adjustable, wherein process parameter values for non-selected process parameters are not adjustable, wherein for the one or more selected process parameters the second process parameter values differ from the first process parameter values, and Shankaramurthy: Paragraphs [0039], [0054], [0056], [0072]-[0074], and [0076]-[0078] [As described in claim 1.] Shankaramurthy: Paragraph [0071] (“In some embodiments, using nested loops with an offset table, iteration adjustments, and/or multipliers is not allowed. In some embodiments, lift position as same as before/after, HRFR before/after mode, and LFRF before/after mode cannot be used in the recipe operation that has an offset table, iteration adjustments, and/or multipliers. In some embodiments, same-as-after mode cannot be selected in a recipe operation preceding a recipe operation that is adjusted by an offset table, iteration adjustments, and/or multipliers. In some embodiments, same-as-before mode cannot be selected in a recipe operation following a recipe operation that is adjusted by an offset table, iteration adjustments, and/or multipliers. In some embodiments, the LFRF loop adjustment cannot be used in a first step of a loop.”)
wherein for the non-selected process parameters the second process parameter values are the same as the first process parameter values. Shankaramurthy: Paragraph [0019] (“Each iteration adjustment updates one or more manufacturing parameters of the respective iteration of the set of processes by performing additive operations (e.g., addition of a value, subtraction of a value).”) Shankaramurthy: Paragraph [0048] (“In some examples, for process A of the set of processes 152, manufacturing parameter 154A is increased by 0 in a first iteration, increased by 0 in a second iteration, ... In some examples, for process B of the set of processes 152, manufacturing parameter 154B is increased by 0 in a first iteration, increased by 5 in a second iteration,…”)
Regarding claim 11, Shankaramurthy teaches all the claimed features of claim 1, from which claim 11 depends. Shankaramurthy teaches:
The method of claim 1, wherein the first performance data and the second performance data each comprise one or more measurements from metrology data of a processed substrate that indicate one or more critical dimensions, Shankaramurthy: Paragraphs [0054], [0056], [0072]-[0074], and [0076]-[0078] [As described in claim 1.] Shankaramurthy: Paragraph [0036] (“Metrology data 172 includes image data, thickness values, interlayer spacing values, uniformity values, performance data, whether layers of a substrate meet threshold values, an indication of variance in the layers of the substrate, property data of substrates, yield, substrate spatial film properties, dimensions (e.g., thickness, height, etc.), dielectric constant, dopant concentration, density, defects (e.g., whether a substrate and/or layer meets a threshold property, why the substrate and/or layer does not meet a threshold property), and/or the like. In some embodiments, the metrology data 172 is for finished or semi-finished substrates. In some embodiments, the metrology data 172 is different for each substrate and/or layer.”) Shankaramurthy: Paragraph [0039] (“For purpose of illustration, rather than limitation, aspects of the disclosure describe determining iteration adjustments 162 and multipliers 164 to cause depositing of layers on substrates. In some implementations, a heuristic model or rule-based model is used to determine iteration adjustments 162 and multipliers 164 (e.g., cause generation of offset table 160). Variable loop control component 112 monitors metrology data 172 to determine iteration adjustments 162 and multipliers 164 (e.g., cause generation of an offset table 160) for recipe 150. In some embodiments, a trained machine learning model is used to determine iteration adjustments 162 and multipliers 164 (e.g., determine an offset table 160). Historic recipes and historic metrology data are used as data input and historical iteration adjustments and historical multipliers (e.g., historical offset tables) (e.g., of uniform layers, of layers of specific thicknesses) are used as target output to train a machine learning model. Recipe 150 and metrology data 172 are used as input into the trained machine learning model and iteration adjustments 162 and multipliers 164 (e.g., offset table 160) is determined from the output of the trained machine learning model.”) [Based on the metrology data to determine iteration adjustments and multipliers (e.g., cause generation of an offset table 160) for recipe reads on “wherein the first performance data and the second performance data each comprise one or more measurements from metrology data of a processed substrate that indicate one or more critical dimensions”.]
the method further comprising: processing the metrology data using a trained machine learning model to generate at least one of the first performance data or the second performance data. Shankaramurthy: Paragraphs [0054], [0056], [0072]-[0074], and [0076]-[0078] [As described in claim 1.] Shankaramurthy: Paragraphs [0036] and [0039] [As described above.] [The iteration adjustments based on the monitored metrology data reads on “processing the metrology data”.]
Regarding claim 12, Shankaramurthy teaches all the claimed features of claim 1, from which claim 12 depends. Shankaramurthy teaches:
The method of claim 1, further comprising: processing a plurality of product substrates using the process recipe having the final process parameter values, wherein third performance data of the process recipe performed on the plurality of product substrates using the process recipe having the final process parameter values satisfies the one or more criteria with respect to the target performance data. Shankaramurthy: Paragraphs [0039], [0054], [0056], [0072]-[0074], and [0076]-[0078] [As described in claim 1.] [Repeating the iteration adjustment of the updated manufacturing parameters until the layer is uniform reads on “third performance data of the process recipe performed on the plurality of product substrates using the process recipe having the final process parameter values satisfies the one or more criteria with respect to the target performance data”.]
Regarding independent claim 14, Shankaramurthy teaches:
A system comprising: Shankaramurthy: Abstract (“A method includes identifying a recipe for depositing layers on a substrate in a processing chamber of a substrate processing system. The recipe comprises iterations of a set of one or more processes, and wherein each iteration of the iterations is for depositing at least one layer of the layers. The method further includes determining changes to parameters for depositing the at least one layer on the substrate. Each of the changes corresponds to a respective iteration of the iterations and is associated with a relative position of a corresponding layer. The layers are to be deposited on one or more substrates based on the recipe and the changes.”)
a memory; and a processing device coupled to the memory, the processing device to: Shankaramurthy: Paragraph [0094] (“In some embodiments, the computer system 300 includes a processing device 302, a volatile memory 304 (e.g., Random Access Memory (RAM)), a non-volatile memory 306 (e.g., Read-Only Memory (ROM) or Electrically-Erasable Programmable ROM (EEPROM)), and/or a data storage device 316, which communicates with each other via a bus 308.”)
The remaining recitations of independent claim 14 recite similar limitations as corresponding independent claim 1 and is rejected using the same teachings and rationale.
Regarding claim 15, the claim recites similar limitations as corresponding claim 2 and is rejected using the same teachings and rationale.
Regarding independent claim 17, Shankaramurthy teaches:
A non-transitory machine-readable storage medium comprising instructions that, when executed by a processing device, cause the processing device to: Shankaramurthy: Paragraph [0006] (“In another aspect of the disclosure, a non-transitory machine-readable storage medium storing instructions which, when executed cause a processing device to perform operations including identifying a recipe for depositing a plurality of layers on a substrate in a processing chamber of a substrate processing system.”)
The remaining recitations of independent claim 17 recite similar limitations as corresponding independent claim 1 and is rejected using the same teachings and rationale.
Regarding claim 18, the claim recites similar limitations as corresponding claim 2 and is rejected using the same teachings and rationale.
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
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 4, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable Shankaramurthy, in view of Pack et al. (US Patent Publication No. 2022/0198333 A1) (“Pack”).
Regarding claim 4, Shankaramurthy teaches all the claimed features of claim 1, from which claim 4 depends. Shankaramurthy further teaches:
The method of claim 1, further comprising: prior to receiving the first data set, training the dynamic controller using a plurality of data sets,… Shankaramurthy: Paragraph [0039] (“Historic recipes and historic metrology data are used as data input and historical iteration adjustments and historical multipliers (e.g., historical offset tables) (e.g., of uniform layers, of layers of specific thicknesses) are used as target output to train a machine learning model.”)
Shankaramurthy does not expressly teach “the plurality of data sets are associated with a design of experiments (DOE) for the process recipe, and wherein each of the plurality of data sets comprises a unique combination of process parameter values and performance data”. However, Pack describes process recipe optimization. Pack teaches:
…wherein the plurality of data sets are associated with a design of experiments (DOE) for the process recipe, and wherein each of the plurality of data sets comprises a unique combination of process parameter values and performance data. Pack: Paragraph [0020] (“Server machine 170 includes a training set generator 172 that is capable of generating training data sets (e.g., a set of data inputs and a set of target outputs) to train, validate, and/or test a machine-learning model 190 (e.g., a feature model). Machine-learning model 190 can be any algorithmic model capable of learning from data. In some embodiments, the data set generator 172 can partition the training data into a training set, a validating set, and a testing set. In some embodiments, the predictive system 110 generates multiple sets of training data.” Which reads on “each of the plurality of data sets comprises a unique combination of process parameter values and performance data”.) Pack: Paragraph [0125] (“At block 524, the processing logic trains a machine learning model using data input including the sets of historical parameters (e.g., experimental designs or DOEs of process recipe parameters and/or historical recipes and parameters) and target output (e.g., target data) including the historical performance data (e.g., historical performance data and/or DOE) to generate a trained machine learning model. In some embodiments, the trained machine learning model uses one or more of; Bayesian Probabilistic Learning, Bayesian Regression or Classification, Gaussian Process Regression or Classification, Bayesian Neural Networks, Neural Network Gaussian Processes, Gaussian Process Regressor (GPR), Bayesian Probabilistic Learning, Bayesian, Deep Belief Network, Gaussian Mixture Model, and/or the like).”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Shankaramurthy and Pack before them, for the plurality of data sets are associated with a design of experiments (DOE) for the process recipe, and wherein each of the plurality of data sets comprises a unique combination of process parameter values and performance data because the references are in the same field of endeavor as the claimed invention and they are focused on recipe optimization.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to model or optimize computationally expensive methods (e.g., use data from complex plasma simulations to train and optimize a general model with minimal number of added simulations). Pack Paragraph [0125].
Regarding claim 16, the claim recites similar limitations as corresponding claim 4 and is rejected using the same teachings and rationale.
Regarding claim 19, the claim recites similar limitations as corresponding claim 4 and is rejected using the same teachings and rationale.
Claims 6, 7, 8, 9, and 20 are rejected under 35 U.S.C. 103 as being unpatentable Shankaramurthy, in view of Cawlfield (CN 1161753 A) (“Cawlfield”).
Regarding claim 6, Shankaramurthy teaches all the claimed features of claim 1, from which claim 6 depends. Shankaramurthy does not expressly teach the features of claim 6. Cawlfield describes a kind of model predictive control apparatus and method, be suitable for predicting the process response in future of controlled system dynamic model. Cawlfield teaches:
The method of claim 1, further comprising: determining one or more modeling error values based on a difference between the first performance data and first predicted performance data; adjusting control coefficients of the dynamic controller based on the one or more modeling error values. Cawlfield: Abstract (“A model predictive control apparatus and method for controlling the operation of a process having a process input signal and a process output signal, which includes determining a predicted process output signal at a future steady state condition according to a process model, determining a steady state error signal according to the difference between the predicted process output signal and a desired set point signal, and determining a set of future process input change signals requires to correct for the estimated steady state error by providing at least one step response in the process output at a future time. The next net process input change is then applied according to the sum of the currently determined first element of the set of future process input change signals summed with any future process input change signals that were previously calculated for the next process input change signal that is calculated according to this method. The model is expressed in velocity form. Process tuning parameters are available to dampen the net process input change as well as the currently determined first element of the process input change with respect to a process input change signal that is calculated according to the steady state error divided by the steady state process gain.”) Cawlfield: Page 6, twelfth paragraph (“… control procedure operation comprises the process output signal of determining the prediction under future steady state conditions according to process model, determine the steady-state error signal according to the difference between forecasting process output signal and the required set point signal, and provide at least one step response by being engraved in the process output when in the future a certain, determine one group of required process input in future variable signal of steady-state error of proofreading and correct estimation. So, according to first element of process input in the future variable signal group of determining at present and arbitrary process input in future variable signal addition of before having calculated for next process input variable signal according to this method and, apply next process input net change. Model is preferably represented with velocity form. In addition, available processes adjusting parameter is imported variation only to process and is imported first element of variation divided by steady-state process gain and amount of decrease with respect to process input variable signal according to the current definite process of steady-state error. In addition, in view of all limiting factors in the process input dynamic range, be equipped with the limit filtration device according to the order of sequence to controller.”) Cawlfield: Page 8, first paragraph (“Controller of the present invention is preferably used a kind of impulse response model of velocity form, and this is a kind of simple model, does not require [more than] the coefficient vector . Relative variation of process output that the description of impulse response vector is expressed from the next and the relation between the process input variation…”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Shankaramurthy and Cawlfield before them, to determine one or more modeling error values based on a difference between the first performance data and first predicted performance data; and adjust control coefficients of the dynamic controller based on the one or more modeling error values because the references are in the same field of endeavor as the claimed invention and they are focused on control optimization.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to suitably predict the process response in future of controlled system dynamic model, and considering this prediction output of computing controller afterwards. Particularly use suitable row of the pseudo inverse matrix (pseudo-inverse) of dynamic model that a kind of process control regulator that makes least square method error minimum is provided. Cawlfield Page 5, first paragraph.
Regarding claim 7, Shankaramurthy and Cawlfield teach all the claimed features of claim 6, from which claim 7 depends. Cawlfield further teaches:
The method of claim 6, wherein the determining the one or more modeling error values based on the difference between the first performance data and first predicted performance data comprises: Cawlfield: Abstract; Page 6, twelfth paragraph; and Page 8, first paragraph [As described in claim 6]
determining a direction and magnitude of change in the difference between the target performance data and the first performance data. Cawlfield: (“In case determined the model coefficient of process, facilitated this model representation is become a matrix A by formula 4 definition. Therefore, if characterize with index k sometimes, this process just is not in stable state; If known previous input variable signal (is X K-n+2, X K-n+3... X k) n-1 element vectors X 0, but also known procedure output signal Y k, suppose that no longer including the process input changes generation, then can calculate stable state output valve Y according to giving me a little k+n-1 (after this, no longer include to export to change and occur) K+n-1Very clear, can determine Y like this K+n-1, promptly at first be matrix multiplication A ° X °, draw Y ° of prediction output variable signal K+1, Y ° K+2... Y ° K+n-1Vector, will predict then output diverse vector the various element addition, again the result is added to Y k”) Cawlfield: (“Y will with set point the output of process relatively. (controller input). X process input (controller output). Y describes the vector that one group of the output of process changes. X describes the vector that one group of process input changes. A describes the vector of process impulse response.”) [The vector that is output reads on “determining a direction and magnitude of change”.]
The motivation to combine Shankaramurthy and Cawlfield as provided in claim 6 is incorporated herein.
Regarding claim 8, Shankaramurthy teaches all the claimed features of claim 1, from which claim 8 depends. Shankaramurthy does not expressly teach the features of claim 8. Cawlfield describes a kind of model predictive control apparatus and method, be suitable for predicting the process response in future of controlled system dynamic model. Cawlfield teaches:
The method of claim 1, wherein the processing the first data set using the dynamic controller to determine second process parameter values for the process parameters of the process recipe, comprises: determining one or more performance error values based on a difference between the target performance data and the first performance data; and generating second process parameter values based on the one or more performance error values. Cawlfield: Abstract (“A model predictive control apparatus and method for controlling the operation of a process having a process input signal and a process output signal, which includes determining a predicted process output signal at a future steady state condition according to a process model, determining a steady state error signal according to the difference between the predicted process output signal and a desired set point signal, and determining a set of future process input change signals requires to correct for the estimated steady state error by providing at least one step response in the process output at a future time. The next net process input change is then applied according to the sum of the currently determined first element of the set of future process input change signals summed with any future process input change signals that were previously calculated for the next process input change signal that is calculated according to this method. The model is expressed in velocity form. Process tuning parameters are available to dampen the net process input change as well as the currently determined first element of the process input change with respect to a process input change signal that is calculated according to the steady state error divided by the steady state process gain.”) Cawlfield: Page 6, twelfth paragraph (“… control procedure operation comprises the process output signal of determining the prediction under future steady state conditions according to process model, determine the steady-state error signal according to the difference between forecasting process output signal and the required set point signal, and provide at least one step response by being engraved in the process output when in the future a certain, determine one group of required process input in future variable signal of steady-state error of proofreading and correct estimation. So, according to first element of process input in the future variable signal group of determining at present and arbitrary process input in future variable signal addition of before having calculated for next process input variable signal according to this method and, apply next process input net change. Model is preferably represented with velocity form. In addition, available processes adjusting parameter is imported variation only to process and is imported first element of variation divided by steady-state process gain and amount of decrease with respect to process input variable signal according to the current definite process of steady-state error. In addition, in view of all limiting factors in the process input dynamic range, be equipped with the limit filtration device according to the order of sequence to controller.”) Cawlfield: Page 8, first paragraph (“Controller of the present invention is preferably used a kind of impulse response model of velocity form, and this is a kind of simple model, does not require [more than] the coefficient vector . Relative variation of process output that the description of impulse response vector is expressed from the next and the relation between the process input variation…”) Cawlfield: Page 11, last paragraph (“Any process of iteration all can use this to separate X, controls with implementation procedure; During each iteration, all want error of calculation e, then multiply by X.”) Cawlfield: Page 12, first paragraph (“Be appreciated that according to foregoing description, because each iteration has all been determined m the variable signal of process input in the future, so the future value of m-1 the also unconsummated so far process input variation of being estimated by preceding once (for example k-1) iteration all can be arranged for each k, have the value that m-2 the also unconsummated so far process input of estimating from the k-2 time iteration changes, or the like. That is, for given k+i process input in the future changes, have m-i calculate in advance from previous iteration process input changing value. Be appreciated that thus input is in the future changed sum multiply by steady-state process gain s 1, can determine directly that input in the future changes the effect to last stable state output, like this, can above-mentioned formula be revised as comprise these calculate in advance process input in future change i.e. error term in the formula 10…”) [The signals generated in the iteration reads on “the second process parameter values.”]
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Shankaramurthy and Cawlfield before them, to determine one or more performance error values based on a difference between the target performance data and the first performance data; and generate second process parameter values based on the one or more performance error values because the references are in the same field of endeavor as the claimed invention and they are focused on control optimization.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to suitably predict the process response in future of controlled system dynamic model, and considering this prediction output of computing controller afterwards. Particularly use suitable row of the pseudo inverse matrix (pseudo-inverse) of dynamic model that a kind of process control regulator that makes least square method error minimum is provided. Cawlfield Page 5, first paragraph.
Regarding claim 9, Shankaramurthy and Cawlfield teach all the claimed features of claim 8, from which claim 9 depends. Cawlfield teaches:
The method of claim 8, wherein the determining the one or more performance error values based on the difference between the target performance data and the first performance data comprises: Cawlfield: Abstract; Page 6, twelfth paragraph; Page 8, first paragraph; Page 11, last paragraph; and Page 12, first paragraph [As described in claim 8]
determining a direction and magnitude of change in the difference between the first performance data and the first predicted performance data. Cawlfield: (“In case determined the model coefficient of process, facilitated this model representation is become a matrix A by formula 4 definition. Therefore, if characterize with index k sometimes, this process just is not in stable state; If known previous input variable signal (is X K-n+2, X K-n+3... X k) n-1 element vectors X 0, but also known procedure output signal Y k, suppose that no longer including the process input changes generation, then can calculate stable state output valve Y according to giving me a little k+n-1 (after this, no longer include to export to change and occur) K+n-1Very clear, can determine Y like this K+n-1, promptly at first be matrix multiplication A ° X °, draw Y ° of prediction output variable signal K+1, Y ° K+2... Y ° K+n-1Vector, will predict then output diverse vector the various element addition, again the result is added to Y k”) Cawlfield: (“Y will with set point the output of process relatively. (controller input). X process input (controller output). Y describes the vector that one group of the output of process changes. X describes the vector that one group of process input changes. A describes the vector of process impulse response.”) [The vector that is output reads on “determining a direction and magnitude of change”.]
The motivation to combine Shankaramurthy and Cawlfield as provided in claim 8 is incorporated herein.
Regarding claim 20, the claim recites similar limitations as corresponding claim 6 and is rejected using the same teachings and rationale.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable Shankaramurthy, in view of Banna (US Patent Publication No. 2020/0279066 A1) (“Banna”).
Regarding claim 13, Shankaramurthy teaches all the claimed features of claim 1, from which claim 13 depends. Shankaramurthy does not expressly teach the features of claim 13. However, Banna describes building a predictive model for spatial distribution of one or more dimensions of interest of a device being fabricated on a wafer. Banna teaches:
The method of claim 1, wherein the dynamic controller is a multiple input-multiple output (MIMO) controller. Banna: Paragraph [0057] (“The spatial model allows process engineers to define multiple constraints (i.e. process specifications) on the process performance, such as a desired spatial profile per dimension across the wafer, a desired uniformity range, a desired mean value across the wafer etc. This is referred to a multi-input multi-output (MIMO) process optimization.”)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of Shankaramurthy and Banna before them, for the dynamic controller is a multiple input-multiple output (MIMO) controller because the references are in the same field of endeavor as the claimed invention and they are focused on control optimization.
One of ordinary skill in the art before the effective filing date of the claimed invention would have been motivated to do this modification to allow process engineers to define multiple constraints (i.e. process specifications) on the process performance. Banna Paragraph [0057]
It is noted that any citations to specific paragraphs or figures in the prior art references and any interpretation of the reference should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent No. 5,408,405 to Mozumder et al. describes a multi-variable statistical process controller for discrete manufacturing. Mozumder describes in Column 4, lines 4-36 (“The controller concludes that the process state has changed if the model predictions for the given equipment settings are statistically different from the measured values of the monitor wafers. The procedure used to test the hypothesis that the process state is significantly different from the state represented by the model is termed as model based SQC. Based on the distribution of the error in the model predictions, the probability of observing the discrepancy between the monitor wafer data and the model predictions is determined. If the probability of observing such a deviation is significantly small, then the process is assumed to be in a state different from what the model represents. The likelihood of errors in model predictions are determined during the model fitting (regression) using error analysis techniques, such as ANOVA (analysis of variance). The difference between the model predictions and actual measurements are plotted on a SQC chart to view and determine SQC failures. The limits on the SQC charts are determined from the moments (usually the mean and standard deviation) of the model prediction errors. For the PECVD Nitride process there are six adjustable equipment settings: N.sub.2 flow, SiH.sub.4 flow, NH.sub.3 flow, pressure, RF power, and the electrode gap. Four product parameters are measured: film deposition rate, index of refraction, stress, and thickness non-uniformity. A SQC failure based any of the four parameters signifies a state change, which in turn activates the model adaptation and process optimization. Although an univariate SQC scheme is used for simplicity, the model adaptation and the optimization algorithms use all four output models in conjunction to estimate the new process state and optimal equipment settings.”) Mozumder describes in Column 4, lines 40-64 (“Model tuning can be accomplished by either updating only the constant term of the model (bias tuning) or by using a multi-variable scheme to update the coefficients. Any of the various bias tuning schemes well known in the art may be utilized. In this controller, a multi-variable update scheme is used to adapt the models to the new process and equipment state. The scheme uses the measurements made on the monitor wafers to determine the combination of "changes" or "perturbations" to the input settings that best explains the data. The difference between the actual settings, and the settings that best explain the data, is attributed to a change in process and equipment state. In the present version of the algorithm, the difference can either take the form of a constant gain (multiplicative) or constant offset (additive) for each controllable setting. Additionally, the residual differences between the monitor wafer measurements and predicted values, based on the new gains and offsets values in the model, are then attributed to unmodeled effects and minimized by updating the bias term for each model. The updated models are used to predict the output parameters for a given equipment setting by applying the gains and offsets as correction factors to the inputs to the models.”)
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/ALICIA M. CHOI/Primary Patent Examiner, Art Unit 2117
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