DETAILED ACTION
Claims 1-20 are presented for examination. This office action is response to the submission on 10/12/2023.
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 statements (IDS) submitted on 10/12/2023, 4/16/2025, and 6/15/2026 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings filed on 10/12/2023 are acceptable for examination proceedings.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1 and 16-17:
Claims 1 and 16-17 are drawn to systems/apparatuses. Therefore claims 1 and 16-17 fall under one of the four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter).
Step 2A: Is the claim directed to a law of nature, a natural phenomenon (product of nature), or an abstract idea?
It is an abstract idea.
Step 2A-Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes.
MPEP 2106.04(a) - “Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion).”
Claims 1 and 16-17 are directed to a judicially recognized exception of an abstract idea without significantly more. Each of claims 1 and 16-17 recites functions below that under the limitation’s broadest reasonable interpretation, enumerates mental concepts. Other than reciting generic computer elements “a controller”, “a trained model” and “storage” (as recited in claim 1), nothing in the claims preclude the functions from the mental concept.
The mere nominal recitation of a generic processor to perform the mental concept does not take the claim limitations out of the abstract idea (See MPEP 2106.04(a)(2)(III)).
“and compares the input data and the reference data to determine whether or not to perform the substrate processing.” A human can determine whether to perform processing based on input data (judgment).
“creates the input data based on the pre-processing measurement data and target data indicating a target value for the thickness of the object; and” A human can create input data based on pre-processing measurement data and target thickness data (judgment).
Step 2A-Prong 2: Does the claim recite additional element that integrate the judicial exception into a practical application?
No.
2106.05(f) Mere Instructions To Apply An Exception
“As explained by the Supreme Court, in order to make a claim directed to a judicial exception patent-eligible, the additional element or combination of elements must do "‘more than simply stat[e] the [judicial exception] while adding the words ‘apply it’". Alice Corp. v. CLS Bank, 573 U.S. 208, 221, 110 USPQ2d 1976, 1982-83 (2014) (quoting Mayo Collaborative Servs. V. Prometheus Labs., Inc., 566 U.S. 66, 72, 101 USPQ2d 1961, 1965). Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible. Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 (warning against a § 101 analysis that turns on "the draftsman’s art").”
The following is using generic computer elements:
“a controller that enters input data in a trained model” and “storage that stores reference data acquired based on pieces of training data used to construct the trained model,” (as recited in claim 1)
2106.05(g) Insignificant Extra-Solution Activity
The term "extra-solution activity" can be understood as activities incidental to the primary process or product that are merely a nominal or tangential addition to the claim. Extra-solution activity includes both pre-solution and post-solution activity. An example of pre-solution activity is a step of gathering data for use in a claimed process, e.g., a step of obtaining information about credit card transactions, which is recited as part of a claimed process of analyzing and manipulating the gathered information by a series of steps in order to detect whether the transactions were fraudulent. An example of post-solution activity is an element that is not integrated into the claim as a whole, e.g., a printer that is used to output a report of fraudulent transactions, which is recited in a claim to a computer programmed to analyze and manipulate information about credit card transactions in order to detect whether the transactions were fraudulent.
The following is pre-solution activity (mere data gathering):
“the controller: causes the thickness gauge to measure the thickness of the object before the substrate processing is performed and then acquires pre-processing measurement data indicating the thickness of the object before the substrate processing is performed;” (as recited in claim 1)
The following is post-solution activity:
“which outputs processing conditions when the substrate processing is performed, to cause the trained model to output the processing conditions,” (as recited in claim 1)
MPEP 2106.05(h) – Field of Use
MPEP 2106.05(h) states: “Another consideration when determining whether a claim integrates the judicial exception into a practical application in Step 2A Prong Two or recites significantly more than a judicial exception in Step 2B is whether the additional elements amount to more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.”
“the input data indicating a target value for a processing amount by the substrate processing;” is merely describing what the input data contains, it does not integrate the judicial exception into a practical application.
“wherein the training data indicates a processing amount by the substrate processing during training, and” is merely describing what the training data indicates, it does not integrate the judicial exception into a practical application.
The examiner has considered the limitations together as a single abstract idea for Step 2A Prong Two rather than as a plurality of separate ideas to be analyzed individually.
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Well-Understood, Routine, and Conventional Activity (2106.05(d)):
As Berkheimer evidence that the claim elements “a thickness gauge that measures thickness of an object included in the substrate;” are well understood, routine, and conventional, Lansford et al. (US20050098535A1) provides support that a thickness gauge for measuring thickness of a substrate is conventional in Lansford [0023] "The pre-etch metrology tool 120 measures the incoming thicknesses of the upper and underlying layers and provides the pre-etch thickness measurements to the process controller 150."
Additional support that these elements are well understood, routine, and conventional are provided in the pertinent art section in the conclusion.
The additional elements amount to well-understood, routine, and conventional components and implementing a generic controller, model, and storage towards a field of use and insignificant pre and post solution activity. “Courts have held computer‐implemented processes not to be significantly more than an abstract idea (and thus ineligible) where the claim as a whole amounts to nothing more than generic computer functions merely used to implement an abstract idea, such as an idea that could be done by a human analog (i.e., by hand or by merely thinking). On the other hand, courts have held computer-implemented processes to be significantly more than an abstract idea (and thus eligible), where generic computer components are able in combination to perform functions that are not merely generic.” DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1257-59, 113 USPQ2d 1097, 1105-07 (Fed. Cir. 2014). ”Selecting information, based on types of information and availability of information in a power-grid environment, for collection, analysis and display” Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).
MPEP 2106.05(d)(II)(i) provides support that receiving or transmitting data over a network is well understood, routine, and conventional.
As such, claims 1 and 16-17 are not patent eligible.
Dependent Claims 2-15 and 18:
Step 1:
Claims 2-15 are drawn to a system and claim 18 is drawn to a computer implemented method, therefore each of claims 2-15 and 18 fall under one of four categories of statutory subject matter (process/method, machines/products/apparatus, manufactures, and compositions of matter). Nonetheless, dependent claims 2-15 and 18 are also ineligible for the same reasons given with respect to claims 1 and 16-17.
Steps 2A-2B:
Claims 3, 5, 10-11 recite further the insignificant input-solution activity of storing information that substrate processing has been performed. Displaying a graph of training data. Displaying a graph including a threshold. (See MPEP 2106.05(g)).
Claims 2-6 recite further mental abstract concepts of determining whether to perform processing. Selecting one of a plurality of determination items from determining not to perform processing, performing processing and storing information, or performing processing. Selecting one of the above options based on thresholds. Including a fourth option of performing processing based on predetermined conditions. Selecting one of the four options based on the thresholds. (See MPEP 2106.04(a)(2)(III)).
Claims 7-10 recite further generic computer elements including a display device. (See MPEP 2106.05(f)).
Claim 7-10 recites further limitations directing the display device towards a field of use, indicating that it includes a setting field where a threshold may be set in, which is not integrated into a practical application. The display device is directed towards a field of use, indicating that it includes a setting field where a determination item may be set in, which is not integrated into a practical application. (See MPEP 2106.05(h)).
Claims 12-15 recite further Well-Understood, Routine, and Conventional Activity including standard nozzle discharging etchant liquids. See 35 USC § 103 Rejection of claims 12-15 and conclusion for support that these elements are Well-Understood, Routine, and Conventional (See MPEP 2106.05(d)).
Claim 18 is substantially similar to claim 1 and is rejected for similar reasons stated above in claim 1 rejection.
The additional functions that are form of insignificant extra-solution activities and Well-Understood, Routine, and Conventional Activity, do not amount to significantly more than an abstract idea because the court decisions have determined that this additional steps to be well-understood, routine, and conventional when claimed in a merely generic manner for data storing, collecting and transmitting (See MPEP § 2106.05(d)(II)(i: Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information)) or (iv: Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)), See further Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 119 USPQ2d 1739 (Fed. Cir. 2016))).
As such, Claims 2-15 and 18 are not patent eligible.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-4 and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Lansford et al. (US20050098535A1) in view of Hubaux et al. (US20220082949A1).
Claim 1:
Lansford teaches “A substrate processing apparatus that performs substrate processing that is processing of a substrate, comprising: a thickness gauge that measures thickness of an object included in the substrate;” (Lansford teaches a substrate processing apparatus that includes a metrology tool 120 which measures thickness in Lansford [0023] "The pre-etch metrology tool 120 measures the incoming thicknesses of the upper and underlying layers and provides the pre-etch thickness measurements to the process controller 150."),
“a controller that enters input data in a trained model, which outputs processing conditions when the substrate processing is performed, to cause the trained model to output the processing conditions, the input data indicating a target value for a processing amount by the substrate processing;” (Lansford teaches the metrology tool 120 providing thickness measurements i.e. input data to the Lansford [0023] "Based on the pre-etch thickness measurements, the process controller 150 generates operating recipe parameters for controlling the etch selectivity of the etch tool 130. Controlling the etch selectivity controls the etch rates of the etch tool 130 for the materials of the upper and underlying layers, thus affecting their post-etch thicknesses. Post-etch thickness measurements provided by the post-etch metrology tool 140 may be used to update the etch selectivity model used by the process controller 150 to determine the operating recipe of the etch tool 130."; Lansford teaches an etch selectivity model trained on historical data in Lansford [0028] "The etch selectivity model is trained based on historical data collected from numerous processing runs of the etch tool 130."; Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer."; Lansford teaches a feedback mode where measured thickness and target thickness may determine an operating recipe in Lansford [0027] "The process controller 150 may change the recipe of the etch tool 130 in a feedback mode or in a feedforward mode of operation. In a feedback mode, the thickness measurements from the metrology tools 120, 140 may be used in conjunction with a target post-etch thickness to determine a new operating recipe for subsequently processed wafers."),
“storage that stores reference data acquired based on pieces of training data used to construct the trained model, wherein the training data indicates a processing amount by the substrate processing during training,” (Lansford teaches the hidden layer of the neural network is trained using historical performance data of the etch tool i.e. the historical performance data is stored in Lansford [0030] "The hidden layer 220 “learns” the effects that recipe parameters in the operating recipe of the etch tool 130 have on determining the post-etch thicknesses of the underlying layer during a training procedure by which the neural network 200 is exposed to historical performance data of the etch tool 130 or a similar etch tool (not shown). The hidden layer 220 weights each of the inputs and/or combinations of the inputs to predict future performance. Through analysis of historical data, the weighting values are changed to try to increase the success at which the model predicts the future performance."),
“and the controller: causes the thickness gauge to measure the thickness of the object before the substrate processing is performed and then acquires pre-processing measurement data indicating the thickness of the object before the substrate processing is performed;” (Lansford teaches a substrate processing apparatus that includes a metrology tool 120 which measures thickness before etching in Lansford [0023] "The pre-etch metrology tool 120 measures the incoming thicknesses of the upper and underlying layers and provides the pre-etch thickness measurements to the process controller 150."), and
“creates the input data based on the pre-processing measurement data and target data indicating a target value for the thickness of the object;” (Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer.").
Lansford does not appear to explicitly teach “and compares the input data and the reference data to determine whether or not to perform the substrate processing.” However, Hubaux does teach this claim limitation (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator i.e. it may compare the measured thickness and the historical thickness to determine the categorical indicator in Hubaux [0051] "FIG. 4 is a flowchart describing a method for making a decision in a manufacturing process utilizing a fault detection and classification (FDC) method/system as disclosed herein. Scanner data 400 is generated during exposure (i.e., exposure scanner data), or following a maintenance action (or by any other means). This scanner data 400, which is numerical in nature, is fed into the FDC system 410. The FDC system 410 converts the data into functional, scanner physics-based indicators and aggregates these functional indicators according to the system physics, so as to determine a categorical system indicator for each substrate. The categorical indicator could be binary, such as whether they meet a quality threshold (OK) or not (NOK). Alternatively there may be more than two categories (e.g., based on statistical binning techniques)."; Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052] "A check decision 420 is made to decide whether a substrate is to be checked/inspected, based on the scanner data 400, and more specifically, on the categorical indicator assigned to that substrate. If it is decided not to check the substrate, then the substrate is forwarded for processing 430. It may be that a few of these substrates still undergo a metrology step 440 (e.g., input data for a control loop and/or to validate the decision made at step 420). If a check is decided at step 420, the substrate is measured 440, and based on the result of the measurement, a rework decision 450 is made, to decide whether the substrate is to be reworked. ").
Lansford and Hubaux are analogous art because they are from the same field of endeavor of substrate processing It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Lansford and Hubaux before him/her, to modify the teachings of Method and apparatus for controlling etch selectivity of Lansford to include the assigning of a categorical indicator based on whether the substrate meets a quality threshold and determining whether to rework it of Hubaux because adding the Method for decision making in a semiconductor manufacturing process of Hubaux would allow for a decision making method that reduces the number of false positives and negatives as described in Hubaux [0067] “As such, a decision making method/system is described herein, for which the number of false positives and negatives will decrease. The improved prediction functionality results from the use of new indicators derived from scanner physics, and that the criteria (thresholds) for setting a categorical indicator value are determined and learned from actual product use cases. Threshold/control limit maintenance is replaced by the automated (no human intervention required) validation feedback loop described, i.e., by monitoring indicator accuracy. Furthermore, the feedback loop can be as close as possible to the scanner to prevent noise introduced by other process steps. The decision model therefore comprises a single model which integrates physics models and machine learning models and automatically adapts its predictions from user application.”
Claim 2:
Lansford in view of Hubaux teaches “The substrate processing apparatus according to claim 1, wherein the storage stores at least one threshold for a comparison result between the input data and the reference data,” (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator i.e. it may compare the measured thickness and the historical thickness to determine the categorical indicator based on a threshold in Hubaux [0051] "FIG. 4 is a flowchart describing a method for making a decision in a manufacturing process utilizing a fault detection and classification (FDC) method/system as disclosed herein. Scanner data 400 is generated during exposure (i.e., exposure scanner data), or following a maintenance action (or by any other means). This scanner data 400, which is numerical in nature, is fed into the FDC system 410. The FDC system 410 converts the data into functional, scanner physics-based indicators and aggregates these functional indicators according to the system physics, so as to determine a categorical system indicator for each substrate. The categorical indicator could be binary, such as whether they meet a quality threshold (OK) or not (NOK). Alternatively there may be more than two categories (e.g., based on statistical binning techniques)."), and
“and the controller compares the input data and the reference data to acquire a comparison result, and then determines whether or not to perform the substrate processing based on the comparison result and the at least one threshold.” (Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052] "A check decision 420 is made to decide whether a substrate is to be checked/inspected, based on the scanner data 400, and more specifically, on the categorical indicator assigned to that substrate. If it is decided not to check the substrate, then the substrate is forwarded for processing 430. It may be that a few of these substrates still undergo a metrology step 440 (e.g., input data for a control loop and/or to validate the decision made at step 420). If a check is decided at step 420, the substrate is measured 440, and based on the result of the measurement, a rework decision 450 is made, to decide whether the substrate is to be reworked.").
Claim 3:
Lansford in view of Hubaux teaches “The substrate processing apparatus according to claim 2, wherein the controller selects one of a plurality of determination items based on the comparison result and the at least one threshold,” (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator i.e. it may compare the measured thickness and the historical thickness to determine the categorical indicator based on a threshold in Hubaux [0051] "FIG. 4 is a flowchart describing a method for making a decision in a manufacturing process utilizing a fault detection and classification (FDC) method/system as disclosed herein. Scanner data 400 is generated during exposure (i.e., exposure scanner data), or following a maintenance action (or by any other means). This scanner data 400, which is numerical in nature, is fed into the FDC system 410. The FDC system 410 converts the data into functional, scanner physics-based indicators and aggregates these functional indicators according to the system physics, so as to determine a categorical system indicator for each substrate. The categorical indicator could be binary, such as whether they meet a quality threshold (OK) or not (NOK). Alternatively there may be more than two categories (e.g., based on statistical binning techniques)."),
“wherein the plurality of determination items includes: a first determination item that indicates determining not to perform the substrate processing;” (Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052] "A check decision 420 is made to decide whether a substrate is to be checked/inspected, based on the scanner data 400, and more specifically, on the categorical indicator assigned to that substrate. If it is decided not to check the substrate, then the substrate is forwarded for processing 430. It may be that a few of these substrates still undergo a metrology step 440 (e.g., input data for a control loop and/or to validate the decision made at step 420). If a check is decided at step 420, the substrate is measured 440, and based on the result of the measurement, a rework decision 450 is made, to decide whether the substrate is to be reworked.”),
“a second determination item that indicates determining to perform the substrate processing based on the processing conditions acquired from the trained model, and also to cause the storage to store therein information indicating that the substrate processing has been performed;” (Hubaux teaches that in addition to the OK or NOK decision (left of DB1 in Fig. 7 indicates OK), Hubaux may have an additional categorization based on decision boundary DB2, wherein the data within DB2 has a higher density of data points e.g. when determined to be OK but outside of DB2, the substrate may be flagged as abnormal in Hubaux [0071-0072] "Such an embodiment may comprise defining of two decision boundaries: a first decision boundary using a supervised method which will learn to discriminate between two classes (e.g., as has already been described); and a second decision boundary using an unsupervised method (e.g., a clustering algorithm or similar) which can learn a “normal” region which has a high density of data points. FIG. 7 conceptually illustrates such an approach. It shows a data set with each point representing a wafer in any (non-specific) data space at two time instances (time t and t+1). The gray data points are OK wafers and black data points are NOK wafers. In each case a triangle signifies a labeled wafer and a circle an unlabeled wafer. At time t, the first decision boundary DB1 is determined using supervised techniques (calibrated/learnt based on the labeled wafers). In parallel a second decision boundary DB2 is determined using unsupervised or semi-supervised techniques (e.g., which divides normal/nominal behavior from less normal/abnormal/outlier behavior). An advantage of having two decision boundaries, as illustrated by the equivalent plot for time t+1 is that it now becomes possible to capture new abnormal behavior; e.g., new NOK data point DP. which is on the OK side of first decision boundary DB1 but on the abnormal side of second decision boundary DB2. Such abnormal characteristics were not discovered during training, hence its incorrect classification with respect to the decision boundary DB1. It should be noted that there may be more than one first decision boundary (i.e., where the first decision model provides a non-binary categorical indicator output). Similarly, there may be more than one second decision boundary."), and
“and a third determination item that indicates determining to perform the substrate processing based on the processing conditions acquired from the trained model.” (Hubaux teaches that in addition to the OK or NOK decision (left of DB1 in Fig. 7 indicates OK), Hubaux may have an additional categorization based on decision boundary DB2, wherein the data within DB2 has a higher density of data points e.g. when determined to be OK and inside of DB2, the substrate may be processed normally in Hubaux [0071-0072].).
Claim 4:
Lansford in view of Hubaux teaches “The substrate processing apparatus according to claim 3, wherein the at least one threshold includes a first threshold and a second threshold less than the first threshold,” (Hubaux teaches that in addition to the OK or NOK decision (left of DB1 in Fig. 7 indicates OK), Hubaux may have an additional categorization based on decision boundary DB2, wherein the data within DB2 has a higher density of data points and the threshold of within DB2 is less than DB1 in Hubaux [0071-0072].),
“and the controller: selects the first determination item when the comparison result is greater than the first threshold;” (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator of NOK if outside of the threshold in Hubaux [0051].),
“selects the second determination item when the comparison result is greater than the second threshold and less than or equal to the first threshold;” (Hubaux teaches that in addition to the OK or NOK decision (left of DB1 in Fig. 7 indicates OK), Hubaux may have an additional categorization based on decision boundary DB2, wherein the data within DB2 has a higher density of data points e.g. when determined to be OK but outside of DB2, the substrate may be flagged as abnormal in Hubaux [0071-0072].), and
“and selects the third determination item when the comparison result is equal to or less than the second threshold.” (Hubaux teaches that in addition to the OK or NOK decision (left of DB1 in Fig. 7 indicates OK), Hubaux may have an additional categorization based on decision boundary DB2, wherein the data within DB2 has a higher density of data points e.g. when determined to be OK and inside of DB2, the substrate may be processed normally in Hubaux [0071-0072].).
Claim 16:
Lansford teaches “A substrate processing system that comprises a thickness gauge apparatus that measures thickness of an object included in a substrate and a substrate processing apparatus that performs substrate processing, which is processing of the substrate, after the thickness gauge apparatus measures the thickness of the object,” (Lansford teaches a substrate processing apparatus that includes a metrology tool 120 which measures thickness in Lansford [0023] "The pre-etch metrology tool 120 measures the incoming thicknesses of the upper and underlying layers and provides the pre-etch thickness measurements to the process controller 150."),
“the substrate processing apparatus including: a controller that enters input data in a trained model, which outputs processing conditions when the substrate processing is performed, to cause the trained model to output the processing conditions, the input data indicating a target value for a processing amount by the substrate processing;” (Lansford teaches the metrology tool 120 providing thickness measurements i.e. input data to the Lansford [0023] "Based on the pre-etch thickness measurements, the process controller 150 generates operating recipe parameters for controlling the etch selectivity of the etch tool 130. Controlling the etch selectivity controls the etch rates of the etch tool 130 for the materials of the upper and underlying layers, thus affecting their post-etch thicknesses. Post-etch thickness measurements provided by the post-etch metrology tool 140 may be used to update the etch selectivity model used by the process controller 150 to determine the operating recipe of the etch tool 130."; Lansford teaches an etch selectivity model trained on historical data in Lansford [0028] "The etch selectivity model is trained based on historical data collected from numerous processing runs of the etch tool 130."; Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer."; Lansford teaches a feedback mode where measured thickness and target thickness may determine an operating recipe in Lansford [0027] "The process controller 150 may change the recipe of the etch tool 130 in a feedback mode or in a feedforward mode of operation. In a feedback mode, the thickness measurements from the metrology tools 120, 140 may be used in conjunction with a target post-etch thickness to determine a new operating recipe for subsequently processed wafers."),
“and storage that stores reference data acquired based on pieces of training data used to construct the trained model, wherein the training data indicates a processing amount by the substrate processing during training,” (Lansford teaches the hidden layer of the neural network is trained using historical performance data of the etch tool i.e. the historical performance data is stored in Lansford [0030] "The hidden layer 220 “learns” the effects that recipe parameters in the operating recipe of the etch tool 130 have on determining the post-etch thicknesses of the underlying layer during a training procedure by which the neural network 200 is exposed to historical performance data of the etch tool 130 or a similar etch tool (not shown). The hidden layer 220 weights each of the inputs and/or combinations of the inputs to predict future performance. Through analysis of historical data, the weighting values are changed to try to increase the success at which the model predicts the future performance."),
“and the controller: acquires pre-processing measurement data from measurement results by the thickness gauge apparatus, the pre-processing measurement data indicating the thickness of the object before the substrate processing is performed;” (Lansford teaches a substrate processing apparatus that includes a metrology tool 120 which measures thickness before etching in Lansford [0023] "The pre-etch metrology tool 120 measures the incoming thicknesses of the upper and underlying layers and provides the pre-etch thickness measurements to the process controller 150."), and
“creates the input data based on the pre-processing measurement data and target data indicating a target value for the thickness of the object;” (Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer.").
Lansford does not appear to explicitly teach “and compares the input data and the reference data to determine whether or not to perform the substrate processing.” However, Hubaux does teach this claim limitation (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator i.e. it may compare the measured thickness and the historical thickness to determine the categorical indicator in Hubaux [0051] "FIG. 4 is a flowchart describing a method for making a decision in a manufacturing process utilizing a fault detection and classification (FDC) method/system as disclosed herein. Scanner data 400 is generated during exposure (i.e., exposure scanner data), or following a maintenance action (or by any other means). This scanner data 400, which is numerical in nature, is fed into the FDC system 410. The FDC system 410 converts the data into functional, scanner physics-based indicators and aggregates these functional indicators according to the system physics, so as to determine a categorical system indicator for each substrate. The categorical indicator could be binary, such as whether they meet a quality threshold (OK) or not (NOK). Alternatively there may be more than two categories (e.g., based on statistical binning techniques)."; Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052] "A check decision 420 is made to decide whether a substrate is to be checked/inspected, based on the scanner data 400, and more specifically, on the categorical indicator assigned to that substrate. If it is decided not to check the substrate, then the substrate is forwarded for processing 430. It may be that a few of these substrates still undergo a metrology step 440 (e.g., input data for a control loop and/or to validate the decision made at step 420). If a check is decided at step 420, the substrate is measured 440, and based on the result of the measurement, a rework decision 450 is made, to decide whether the substrate is to be reworked. ").
Lansford and Hubaux are analogous art because they are from the same field of endeavor of substrate processing It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Lansford and Hubaux before him/her, to modify the teachings of Method and apparatus for controlling etch selectivity of Lansford to include the assigning of a categorical indicator based on whether the substrate meets a quality threshold and determining whether to rework it of Hubaux because adding the Method for decision making in a semiconductor manufacturing process of Hubaux would allow for a decision making method that reduces the number of false positives and negatives as described in Hubaux [0067] “As such, a decision making method/system is described herein, for which the number of false positives and negatives will decrease. The improved prediction functionality results from the use of new indicators derived from scanner physics, and that the criteria (thresholds) for setting a categorical indicator value are determined and learned from actual product use cases. Threshold/control limit maintenance is replaced by the automated (no human intervention required) validation feedback loop described, i.e., by monitoring indicator accuracy. Furthermore, the feedback loop can be as close as possible to the scanner to prevent noise introduced by other process steps. The decision model therefore comprises a single model which integrates physics models and machine learning models and automatically adapts its predictions from user application.”
Claim 17:
Lansford teaches “A substrate processing system comprising: a thickness gauge apparatus that measures thickness of an object included in a substrate;” (Lansford teaches a substrate processing apparatus that includes a metrology tool 120 which measures thickness before etching in Lansford [0023] "The pre-etch metrology tool 120 measures the incoming thicknesses of the upper and underlying layers and provides the pre-etch thickness measurements to the process controller 150."),
“and a substrate processing apparatus that causes a trained model, which outputs processing conditions when the substrate processing is performed, to output the processing conditions to perform the substrate processing,” (Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer."),
“wherein the determining apparatus includes storage that stores reference data acquired based on pieces of training data used to construct the trained model,” (Lansford teaches the hidden layer of the neural network is trained using historical performance data of the etch tool i.e. the historical performance data is stored in Lansford [0030] "The hidden layer 220 “learns” the effects that recipe parameters in the operating recipe of the etch tool 130 have on determining the post-etch thicknesses of the underlying layer during a training procedure by which the neural network 200 is exposed to historical performance data of the etch tool 130 or a similar etch tool (not shown). The hidden layer 220 weights each of the inputs and/or combinations of the inputs to predict future performance. Through analysis of historical data, the weighting values are changed to try to increase the success at which the model predicts the future performance."),
“the training data indicates a processing amount by the substrate processing during training,” (Lansford teaches the hidden layer of the neural network is trained using historical performance data of the etch tool i.e. the historical performance data contains how much was etched in Lansford [0030] "The hidden layer 220 “learns” the effects that recipe parameters in the operating recipe of the etch tool 130 have on determining the post-etch thicknesses of the underlying layer during a training procedure by which the neural network 200 is exposed to historical performance data of the etch tool 130 or a similar etch tool (not shown). The hidden layer 220 weights each of the inputs and/or combinations of the inputs to predict future performance. Through analysis of historical data, the weighting values are changed to try to increase the success at which the model predicts the future performance."),
“the determining device: acquires pre-processing measurement data from measurement results by the thickness gauge apparatus, the pre-processing measurement data indicating the thickness of the object before the substrate processing is performed;” (Lansford teaches a substrate processing apparatus that includes a metrology tool 120 which measures thickness before etching in Lansford [0023] "The pre-etch metrology tool 120 measures the incoming thicknesses of the upper and underlying layers and provides the pre-etch thickness measurements to the process controller 150."),
“creates input data based on target data and the pre-processing measurement data, the input data indicating a target value for a processing amount by the substrate processing, the target data indicating a target value for the thickness of the object;” (Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer."), and
“the substrate processing apparatus includes a controller that, when the determining apparatus determines to perform the substrate processing, enters the input data in the trained model to cause the trained model to output the processing conditions.” (Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer.").
Lansford does not appear to explicitly teach “a determining apparatus that determines whether or not to perform substrate processing, which is processing of the substrate;”, “a determining device that determines whether or not to perform the substrate processing,”, and “compares the input data and the reference data to determine whether or not to perform the substrate processing,” However, Hubaux does teach these claim limitations.
Hubaux teaches “a determining apparatus that determines whether or not to perform substrate processing, which is processing of the substrate;” (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator i.e. it may compare the measured thickness and the historical thickness to determine the categorical indicator in Hubaux [0051] "FIG. 4 is a flowchart describing a method for making a decision in a manufacturing process utilizing a fault detection and classification (FDC) method/system as disclosed herein. Scanner data 400 is generated during exposure (i.e., exposure scanner data), or following a maintenance action (or by any other means). This scanner data 400, which is numerical in nature, is fed into the FDC system 410. The FDC system 410 converts the data into functional, scanner physics-based indicators and aggregates these functional indicators according to the system physics, so as to determine a categorical system indicator for each substrate. The categorical indicator could be binary, such as whether they meet a quality threshold (OK) or not (NOK). Alternatively there may be more than two categories (e.g., based on statistical binning techniques)."; Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052] "A check decision 420 is made to decide whether a substrate is to be checked/inspected, based on the scanner data 400, and more specifically, on the categorical indicator assigned to that substrate. If it is decided not to check the substrate, then the substrate is forwarded for processing 430. It may be that a few of these substrates still undergo a metrology step 440 (e.g., input data for a control loop and/or to validate the decision made at step 420). If a check is decided at step 420, the substrate is measured 440, and based on the result of the measurement, a rework decision 450 is made, to decide whether the substrate is to be reworked."),
“a determining device that determines whether or not to perform the substrate processing,” (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator i.e. it may compare the measured thickness and the historical thickness to determine the categorical indicator in Hubaux [0051]. Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052].), and
“compares the input data and the reference data to determine whether or not to perform the substrate processing,” (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator i.e. it may compare the measured thickness and the historical thickness to determine the categorical indicator in Hubaux [0051]. Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052].).
Lansford and Hubaux are analogous art because they are from the same field of endeavor of substrate processing It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Lansford and Hubaux before him/her, to modify the teachings of Method and apparatus for controlling etch selectivity of Lansford to include the assigning of a categorical indicator based on whether the substrate meets a quality threshold and determining whether to rework it of Hubaux because adding the Method for decision making in a semiconductor manufacturing process of Hubaux would allow for a decision making method that reduces the number of false positives and negatives as described in Hubaux [0067] “As such, a decision making method/system is described herein, for which the number of false positives and negatives will decrease. The improved prediction functionality results from the use of new indicators derived from scanner physics, and that the criteria (thresholds) for setting a categorical indicator value are determined and learned from actual product use cases. Threshold/control limit maintenance is replaced by the automated (no human intervention required) validation feedback loop described, i.e., by monitoring indicator accuracy. Furthermore, the feedback loop can be as close as possible to the scanner to prevent noise introduced by other process steps. The decision model therefore comprises a single model which integrates physics models and machine learning models and automatically adapts its predictions from user application.”
Claim 18:
Lansford teaches “A data processing method performed by the substrate processing apparatus according to claim 1,comprising: measuring, by the thickness gauge, thickness of an object included in a substrate before substrate processing is performed by the substrate processing apparatus to acquire pre-processing measurement data indicating measurement results of the thickness of the object;” (Lansford teaches a substrate processing apparatus that includes a metrology tool 120 which measures thickness in Lansford [0023] "The pre-etch metrology tool 120 measures the incoming thicknesses of the upper and underlying layers and provides the pre-etch thickness measurements to the process controller 150."),
“creating, by the controller, input data based on target data and the pre-processing measurement data, the input data indicating a target value for a processing amount by the substrate processing, the target data indicating a target value for the thickness of the object;” (Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer."),
“the reference data being acquired based on pieces of training data used to construct a trained model, wherein the training data indicates a processing amount by the substrate processing during training,” (Lansford teaches the hidden layer of the neural network is trained using historical performance data of the etch tool i.e. the historical performance data is stored in Lansford [0030] "The hidden layer 220 “learns” the effects that recipe parameters in the operating recipe of the etch tool 130 have on determining the post-etch thicknesses of the underlying layer during a training procedure by which the neural network 200 is exposed to historical performance data of the etch tool 130 or a similar etch tool (not shown). The hidden layer 220 weights each of the inputs and/or combinations of the inputs to predict future performance. Through analysis of historical data, the weighting values are changed to try to increase the success at which the model predicts the future performance."), and
“the trained model outputs, in response to the input data being entered therein, processing conditions when the substrate processing is performed.” (Lansford teaches the metrology tool 120 providing thickness measurements i.e. input data to the Lansford [0023] "Based on the pre-etch thickness measurements, the process controller 150 generates operating recipe parameters for controlling the etch selectivity of the etch tool 130. Controlling the etch selectivity controls the etch rates of the etch tool 130 for the materials of the upper and underlying layers, thus affecting their post-etch thicknesses. Post-etch thickness measurements provided by the post-etch metrology tool 140 may be used to update the etch selectivity model used by the process controller 150 to determine the operating recipe of the etch tool 130."; Lansford teaches an etch selectivity model trained on historical data in Lansford [0028] "The etch selectivity model is trained based on historical data collected from numerous processing runs of the etch tool 130."; Lansford teaches the thickness measurement is inputted and the model outputs a prediction of required parameters to arrive at the target post-etch thickness i.e. the input includes both pre and post-etch thickness or how much it needs to be processed in Lansford [0030] "Turning briefly to FIG. 2, a simplified diagram of a neural network 200 is provided. The neural network 200 includes an input layer 210, a hidden layer 220, and an output layer 230. The input layer 210 receives those input values deemed appropriate for modeling the etch selectivity of the etch tool 130. In the illustrated embodiment, the incoming upper and underlying layer thickness measurements, as measured by the metrology tools 120, 140, are received as inputs, although other inputs may also be used... The output layer 230 distills the manipulation of the hidden layer 220 to generate a prediction of, for example, the temperature, pressure, and/or reactant gas composition required to perform the etch and arrive at a target post-etch thickness for the underlying layer."; Lansford teaches a feedback mode where measured thickness and target thickness may determine an operating recipe in Lansford [0027] "The process controller 150 may change the recipe of the etch tool 130 in a feedback mode or in a feedforward mode of operation. In a feedback mode, the thickness measurements from the metrology tools 120, 140 may be used in conjunction with a target post-etch thickness to determine a new operating recipe for subsequently processed wafers.").
Lansford does not appear to explicitly teach “and comparing, by the controller, reference data and the input data to determine whether or not to perform the substrate processing,” However, Hubaux does teach this claim limitation (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator i.e. it may compare the measured thickness and the historical thickness to determine the categorical indicator in Hubaux [0051] "FIG. 4 is a flowchart describing a method for making a decision in a manufacturing process utilizing a fault detection and classification (FDC) method/system as disclosed herein. Scanner data 400 is generated during exposure (i.e., exposure scanner data), or following a maintenance action (or by any other means). This scanner data 400, which is numerical in nature, is fed into the FDC system 410. The FDC system 410 converts the data into functional, scanner physics-based indicators and aggregates these functional indicators according to the system physics, so as to determine a categorical system indicator for each substrate. The categorical indicator could be binary, such as whether they meet a quality threshold (OK) or not (NOK). Alternatively there may be more than two categories (e.g., based on statistical binning techniques)."; Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052] "A check decision 420 is made to decide whether a substrate is to be checked/inspected, based on the scanner data 400, and more specifically, on the categorical indicator assigned to that substrate. If it is decided not to check the substrate, then the substrate is forwarded for processing 430. It may be that a few of these substrates still undergo a metrology step 440 (e.g., input data for a control loop and/or to validate the decision made at step 420). If a check is decided at step 420, the substrate is measured 440, and based on the result of the measurement, a rework decision 450 is made, to decide whether the substrate is to be reworked. ").
Lansford and Hubaux are analogous art because they are from the same field of endeavor of substrate processing It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Lansford and Hubaux before him/her, to modify the teachings of Method and apparatus for controlling etch selectivity of Lansford to include the assigning of a categorical indicator based on whether the substrate meets a quality threshold and determining whether to rework it of Hubaux because adding the Method for decision making in a semiconductor manufacturing process of Hubaux would allow for a decision making method that reduces the number of false positives and negatives as described in Hubaux [0067] “As such, a decision making method/system is described herein, for which the number of false positives and negatives will decrease. The improved prediction functionality results from the use of new indicators derived from scanner physics, and that the criteria (thresholds) for setting a categorical indicator value are determined and learned from actual product use cases. Threshold/control limit maintenance is replaced by the automated (no human intervention required) validation feedback loop described, i.e., by monitoring indicator accuracy. Furthermore, the feedback loop can be as close as possible to the scanner to prevent noise introduced by other process steps. The decision model therefore comprises a single model which integrates physics models and machine learning models and automatically adapts its predictions from user application.”
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Lansford et al. (US20050098535A1) in view of Hubaux et al. (US20220082949A1), further in view of Freese et al. (US20230400508A1).
Claim 5:
Lansford in view of Hubaux teaches “The substrate processing apparatus according to claim 3,” as described above. Neither Lansford or Hubaux appear to explicitly teach “wherein the storage further stores predetermined conditions as the processing conditions, and the plurality of determination steps items further includes a fourth determination item that indicates determining to perform the substrate processing based on the predetermined conditions, and also to cause the storage to store therein information indicating that the substrate processing has been performed.” However, Freese does teach this claim limitation (Freese teaches an operator may set custom conditions and triggers i.e. they may set a response of setting a processing parameter if a condition is within a threshold range in Freese [0097] "The thresholds may include parameter thresholds as well as HI boundaries and/or parameter minimum and maximum limits. Each of the conditions may include checking whether one or more parameters are at one or more predetermined values, levels and/or within predetermined ranges. A default set of triggers, thresholds, conditions, and/or limits may be used. One of the systems referred to herein and/or a system operator may create a customized set of triggers, thresholds, conditions, HI boundaries and/or limits, which may alternatively be used. The HI module 230 and/or other modules referred to herein may change the triggers, thresholds, conditions, HI boundaries and/or limits over time."; Freese teaches setting a flag and continuing operation if in between a first and second threshold in Freese [0132] "Any operation time up to the point at which the second threshold is met may be considered normal and operation is permitted to continue, but a “flag” may be generated when the first threshold is met. The first threshold being met indicates the component is degrading and as a result is investigated. First and second thresholds may also be used in association with HI values and similar operations may be performed.").
Lansford, Hubaux, and Freese are analogous art because they are from the same field of endeavor of substrate processing It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Lansford, Hubaux, and Freese before him/her, to modify the teachings of Method and apparatus for controlling etch selectivity of Lansford modified to include the assigning of a categorical indicator based on whether the substrate meets a quality threshold and determining whether to rework it of Hubaux to include the custom conditions and triggers and flag setting of Freese because adding the Substrate processing system tools for monitoring, assessing and responding based on health including sensor mapping and triggered datalogging of Freese would allow for an operator to troubleshoot issues quickly as described in Freese [0118] “The above-described method and other features disclosed herein allow a system operator to easily troubleshoot an issue by quickly and easily being able to determine the locations of sensors and monitor data and information associated with the sensors.”
Claim 6:
Lansford in view of Hubaux, further in view of Freese teaches “The substrate processing apparatus according to claim 5, wherein the at least one threshold includes a first threshold and a second threshold less than the first threshold,” (Hubaux teaches that in addition to the OK or NOK decision (left of DB1 in Fig. 7 indicates OK), Hubaux may have an additional categorization based on decision boundary DB2, wherein the data within DB2 has a higher density of data points and the threshold of within DB2 is less than DB1 in Hubaux [0071-0072]),
“and the controller: selects the first determination item when the comparison result is greater than the first threshold;” (Hubaux teaches determining whether a substrate meets a quality threshold and assigns a categorical indicator of NOK if outside of the threshold in Hubaux [0051]. Hubaux teaches determining whether a substrate is to be reworked i.e. not perform processing or forwarded to processing based on the categorical indicator in Hubaux [0052]),
“selects the second determination item (Hubaux teaches that in addition to the OK or NOK decision (left of DB1 in Fig. 7 indicates OK), Hubaux may have an additional categorization based on decision boundary DB2, wherein the data within DB2 has a higher density of data points e.g. when determined to be OK but outside of DB2, the substrate may be flagged as abnormal in Hubaux [0071-0072]),
“and selects the third determination item when the comparison result is equal to or less than the second threshold.” (Hubaux teaches that in addition to the OK or NOK decision (left of DB1 in Fig. 7 indicates OK), Hubaux may have an additional categorization based on decision boundary DB2, wherein the data within DB2 has a higher density of data points e.g. when determined to be OK and inside of DB2, the substrate may be processed normally in Hubaux [0071-0072]), and
“selects (Freese teaches an operator may set custom conditions and triggers i.e. they may set a response of setting a processing parameter if a condition is within a threshold range in Freese [0097] "The thresholds may include parameter thresholds as well as HI boundaries and/or parameter minimum and maximum limits. Each of the conditions may include checking whether one or more parameters are at one or more predetermined values, levels and/or within predetermined ranges. A default set of triggers, thresholds, conditions, and/or limits may be used. One of the systems referred to herein and/or a system operator may create a customized set of triggers, thresholds, conditions, HI boundaries and/or limits, which may alternatively be used. The HI module 230 and/or other modules referred to herein may change the triggers, thresholds, conditions, HI boundaries and/or limits over time."; Freese teaches setting a flag and continuing operation if in between a first and second threshold in Freese [0132] "Any operation time up to the point at which the second threshold is met may be considered normal and operation is permitted to continue, but a “flag” may be generated when the first threshold is met. The first threshold being met indicates the component is degrading and as a result is investigated. First and second thresholds may also be used in association with HI values and similar operations may be performed.").
Claims 7 and 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Lansford et al. (US20050098535A1) in view of Hubaux et al. (US20220082949A1), further in view of Chun et al. (US20200176683A1).
Claim 7:
Lansford in view of Hubaux teaches “The substrate processing apparatus according to claim 2,” as described above. Neither Lansford or Hubaux appear to explicitly teach “further comprising a display device that presents a setting screen, wherein the setting screen includes a setting field that is allowed to set the at least one threshold in.” However, Chun does teach this claim limitation (Chun teaches a GUI that displays data tables indicating a threshold height thickness where the user may adjust a threshold percentage in Chun [0062] "GUI 300 displays two data tables that are similar to data table 210 of FIG. 2A. The first and second data tables may display data for an average of a set of pixels or data for a standard pixel based on the selection of radio button 302. In an exemplary embodiment, the first table displays data that is relative to an actual center film height or thickness of a pixel and the second table may display data that is relative to a target center film height or thickness for a pixel. In other words, the percentage area aperture ratios displayed in the first data table may be relative to the actual film center height or thickness for a pixel and the area aperture ratios displayed in the second data table may be relative to the target film center height or thickness for a pixel. For both the first and second data table, GUI 300 may also display a threshold height difference, where a threshold percentage (e.g., 95% or the like) of data from pixels falls within the threshold height difference for the average center film height or thickness. The target thickness is editable and may be input into box 304 by a user.").
Lansford, Hubaux, and Chun are analogous art because they are from the same field of endeavor of substrate processing It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Lansford, Hubaux, and Chun before him/her, to modify the teachings of Method and apparatus for controlling etch selectivity of Lansford modified to include the assigning of a categorical indicator based on whether the substrate meets a quality threshold and determining whether to rework it of Hubaux to include the GUI that displays a threshold thickness where the user may adjust the threshold of Chun because adding the Analysis of material layers on surfaces of Chun would allow for the user to adjust the threshold based on the requirements for the substrate, which a person having ordinary skill in the art would recognize as improving functionality as described in in Chun [0062] "For both the first and second data table, GUI 300 may also display a threshold height difference, where a threshold percentage (e.g., 95% or the like) of data from pixels falls within the threshold height difference for the average center film height or thickness. The target thickness is editable and may be input into box 304 by a user."
Claim 10:
Lansford in view of Hubaux, further in view of Chun teaches “The substrate processing apparatus according to claim 7, wherein the setting screen further includes a graph display field that presents a graph (Chun teaches the ability to display a graph and adjust boundaries in Chun [0102] "FIG. 13C illustrates how the bank measurements can be edited by a user. For example, boundaries 1322 may be selected by a user and moved such that the boundaries used to determine the bank measurements are adjusted. Accordingly, boundaries on graphs 1302, 1304, 1306, and 1308, such as boundary 1350, are also adjusted. Based on the adjustment, measurement values, such as values 1352 and 1354, are adjusted. Data table 1324 similarly reflects the adjusted values for the measurements of the banks based on the adjusted boundaries."), and
“The substrate processing apparatus according to claim 7, wherein the setting screen further includes a graph display field that presents a graph in which the pieces of training data is quantified.” (Hubaux teaches a display of previous wafers i.e. training data in Hubaux [0072] "FIG. 7 conceptually illustrates such an approach. It shows a data set with each point representing a wafer in any (non-specific) data space at two time instances (time t and t+1). The gray data points are OK wafers and black data points are NOK wafers. In each case a triangle signifies a labeled wafer and a circle an unlabeled wafer. At time t, the first decision boundary DB1 is determined using supervised techniques (calibrated/learnt based on the labeled wafers). In parallel a second decision boundary DB2 is determined using unsupervised or semi-supervised techniques (e.g., which divides normal/nominal behavior from less normal/abnormal/outlier behavior)." and in Hubaux Fig. 7).
Claim 11:
Lansford in view of Hubaux, further in view of Chun teaches “The substrate processing apparatus according to claim 10, wherein the setting screen presents the at least one threshold in the graph display field.” (Hubaux teaches a display of previous wafers i.e. training data which include the decision boundaries DB1 and DB2 i.e. thresholds in Hubaux [0072] "FIG. 7 conceptually illustrates such an approach. It shows a data set with each point representing a wafer in any (non-specific) data space at two time instances (time t and t+1). The gray data points are OK wafers and black data points are NOK wafers. In each case a triangle signifies a labeled wafer and a circle an unlabeled wafer. At time t, the first decision boundary DB1 is determined using supervised techniques (calibrated/learnt based on the labeled wafers). In parallel a second decision boundary DB2 is determined using unsupervised or semi-supervised techniques (e.g., which divides normal/nominal behavior from less normal/abnormal/outlier behavior)." and in Hubaux Fig. 7).
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Lansford et al. (US20050098535A1) in view of Hubaux et al. (US20220082949A1), further in view of Bun et al. (US20030060917A1).
Claim 8:
Lansford in view of Hubaux teaches “The substrate processing apparatus according to claim 3,” as described above. Neither Lansford or Hubaux appear to explicitly teach “further comprising a display device that presents a setting screen, wherein the setting screen includes a first setting field that, for the at least one threshold, is allowed to set one determination item in, the one determination item being one of the plurality of determination items.” However, Bun does teach this claim limitation (Bun teaches a menu with two modes, one where the controller automatically performs feedback control and another that requires user input to change a processing condition in response to thickness measurements in Bun [0125] "The menu 50 displays two modes, i.e., an “automatic change mode” and a “user confirmation mode”. In the “automatic change mode”, the controller 10 automatically performs feedback control with no user confirmation. When the operator selects the automatic change mode, therefore, no operation is required until the film thickness measurement and the feedback control thereof are completed. On the other hand, the “user confirmation mode” temporarily requires user confirmation for changing the processing condition of any processing unit in response to the results of the film thickness measurement. Therefore, the processing is progressed with user confirmation in the user confirmation mode.").
Lansford, Hubaux, and Bun are analogous art because they are from the same field of endeavor of substrate processing It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Lansford, Hubaux, and Bun before him/her, to modify the teachings of Method and apparatus for controlling etch selectivity of Lansford modified to include the assigning of a categorical indicator based on whether the substrate meets a quality threshold and determining whether to rework it of Hubaux to include the setting adjustment of Bun because adding the Substrate processing apparatus control system of Bun would allow for the user to improve the degree of freedom in feedback control as described in in Bun [0143] "Thus, the correction value can be changed through manipulation by the operator, whereby the degree of freedom in feedback control after film thickness measurement can be improved.”
Claim 9:
Lansford in view of Hubaux, further in view of Bun teaches “The substrate processing apparatus according to claim 8, wherein the setting screen further includes a second setting field that is allowed to set the at least one threshold in.” (Hubaux teaches that an expert may set a threshold e.g. using a setting field in Hubaux [0064] "FIG. 6 comprise three plots which illustrate the deriving of the functional and categorical indicators, and their effectiveness over the statistical indicators used presently. FIG. 6(a) is a plot of raw parameter data, more specifically reticle align (RA) against time t. FIG. 6(b) is an equivalent (e.g., for reticle align) non-linear model function (or fit) mf derived according to methods described herein. As described, such a model can be derived from knowledge of the scanner physics, and can further be trained on production data (e.g., reticle align measurements performed when performing a specific manufacturing process of interest). The training of this model may use statistical, regression, Bayesian learning or deep learning techniques, for example. FIG. 6(c) comprises the residual Δ between the plots of FIG. 6(a) and FIG. 6(b) which can be used as the functional indicator of the methods disclosed herein. One or more thresholds ΔT can be set and/or learned (e.g., initially based on user knowledge/expert opinion and/or training as described), thereby providing a categorical indicator.").
Claims 12-15 are rejected under 35 U.S.C. 103 as being unpatentable over Lansford et al. (US20050098535A1) in view of Hubaux et al. (US20220082949A1), further in view of Taniyama et al. (US6096233A).
Claim 12:
Lansford in view of Hubaux teaches “The substrate processing apparatus according to claim 1,” as described above. Neither Lansford or Hubaux appear to explicitly teach “further comprising a nozzle that discharges a processing liquid toward the substrate, wherein a process of the substrate processing includes a process of discharging the processing liquid from the nozzle toward the substrate.” However, Taniyama does teach this claim limitation (Taniyama teaches a nozzle that supplies etchant solution towards the film on a wafer in Taniyama [Column 7 lines 13-25] "During rotation of the wafer W, the first nozzle 31 begins to supply the etchant solution 3 toward the thin film 2 (step S7). Also, the controller 40 supplies a command signal to the first nozzle moving mechanism 34 so as to control the moving speed of the first nozzle 31. In this step, the first nozzle 31 is moved in a radial direction from the peripheral portion toward the central portion of the wafer W while controlling the moving speed of the first nozzle 31 to conform with the moving speed for each passing point which is obtained in step S4 (step S8). The thin film 2 is etched with the etchant solution 3 supplied from the first nozzle 31 during its scanning movement so as to flatten the surface of the thin film 2.").
Lansford, Hubaux, and Taniyama are analogous art because they are from the same field of endeavor of substrate processing It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention, having teachings of Lansford, Hubaux, and Taniyama before him/her, to modify the teachings of Method and apparatus for controlling etch selectivity of Lansford modified to include the assigning of a categorical indicator based on whether the substrate meets a quality threshold and determining whether to rework it of Hubaux to include the nozzle that supplies etchant solution of Taniyama because adding the Method for wet etching of thin film of Taniyama would allow for increased controllability of etching rate as the moving speed change point increases as described in in Taniyama [Column 9 lines 21-34] " Incidentally, the controllability of the etching rate is improved with increase in the number of moving speed changing points.
Let us describe more in detail the moving speed of the first nozzle 31 with reference to FIG. 12. Suppose a CVD oxide film 2 having a profile as shown in FIG. 12 is etched at 23.degree. C. with a dilute hydrofluoric acid solution (50% hydrofluoric acid solution: pure water=1:99) to make the oxide film 2 uniform in thickness (98 nm). In this case, the etching rate k is set at 0.4 nm/sec. Under this condition, the moving speed and time required for the movement of the first nozzle 31 between adjacent points can be determined by formula (8) as follows. Incidentally, the wafer W is sized at 8 inches in diameter.”
Claim 13:
Lansford in view of Hubaux, further in view of Taniyama teaches “The substrate processing apparatus according to claim 12, further comprising a nozzle-moving mechanism that cause the nozzle to move when the substrate processing is performed.” (Taniyama teaches a nozzle that supplies etchant solution towards the film on a wafer is moved in Taniyama [Column 7 lines 13-25] "During rotation of the wafer W, the first nozzle 31 begins to supply the etchant solution 3 toward the thin film 2 (step S7). Also, the controller 40 supplies a command signal to the first nozzle moving mechanism 34 so as to control the moving speed of the first nozzle 31. In this step, the first nozzle 31 is moved in a radial direction from the peripheral portion toward the central portion of the wafer W while controlling the moving speed of the first nozzle 31 to conform with the moving speed for each passing point which is obtained in step S4 (step S8). The thin film 2 is etched with the etchant solution 3 supplied from the first nozzle 31 during its scanning movement so as to flatten the surface of the thin film 2.").
Claim 14:
Lansford in view of Hubaux, further in view of Taniyama teaches “The substrate processing apparatus according to claim 13, wherein the processing conditions include a moving speed of the nozzle.” (Taniyama teaches a nozzle that supplies etchant solution towards the film on a wafer at a speed based on an arithmetic operation in Taniyama [Column 7 lines 4-25] "In the next step (step S4), the moving speed of the first nozzle 31 for each passing point is determined by an arithmetic operation on the basis of the etching rate of the thin film set in step S1 and the film thickness detected in step S3. Then, the first nozzle 31 is positioned above a peripheral portion of the wafer W (step S5), followed by supplying a command signal from the controller 40 to the spin chuck 10 so as to rotate the wafer W at a rotating speed of about 300 rpm (step S6). During rotation of the wafer W, the first nozzle 31 begins to supply the etchant solution 3 toward the thin film 2 (step S7). Also, the controller 40 supplies a command signal to the first nozzle moving mechanism 34 so as to control the moving speed of the first nozzle 31. In this step, the first nozzle 31 is moved in a radial direction from the peripheral portion toward the central portion of the wafer W while controlling the moving speed of the first nozzle 31 to conform with the moving speed for each passing point which is obtained in step S4 (step S8). The thin film 2 is etched with the etchant solution 3 supplied from the first nozzle 31 during its scanning movement so as to flatten the surface of the thin film 2.").
Claim 15:
Lansford in view of Hubaux, further in view of Taniyama teaches “The substrate processing apparatus according to claim 12, wherein the processing liquid includes an etchant that etches the object.” (Taniyama teaches a nozzle that supplies etchant solution towards the film on a wafer in Taniyama [Column 7 lines 13-25] "During rotation of the wafer W, the first nozzle 31 begins to supply the etchant solution 3 toward the thin film 2 (step S7). Also, the controller 40 supplies a command signal to the first nozzle moving mechanism 34 so as to control the moving speed of the first nozzle 31. In this step, the first nozzle 31 is moved in a radial direction from the peripheral portion toward the central portion of the wafer W while controlling the moving speed of the first nozzle 31 to conform with the moving speed for each passing point which is obtained in step S4 (step S8). The thin film 2 is etched with the etchant solution 3 supplied from the first nozzle 31 during its scanning movement so as to flatten the surface of the thin film 2.").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Benvegnu et al. (US20200151868A1) teaches a thickness measurement system in Benvegnu [0025] “The thickness of a layer on a substrate can be optically measured before or after polishing, e.g., at an in-line or stand-alone metrology station. However, some optical techniques such as spectrometry require expensive spectrographs and computationally heavy manipulation of spectra data. Even apart from computational load, in some situations the algorithm results do not meet the ever increasing accuracy requirements of the user. However, another metrology technique is to take a color image of the substrate, and analyze the image in a color space to determine regions with acceptable thickness.”
Fujii et al. (US20140197129A1 ) teaches a nozzle that discharges etching liquid and has a scan velocity in Fujii [0032] “Another preferred embodiment of the present invention provides a substrate processing apparatus for processing a substrate comprising: a substrate holding rotating unit for holding the substrate and rotating the substrate about a rotational axis passing through a principal face of the substrate; an etching liquid supply mechanism including a nozzle for discharging an etching liquid toward the principal face of the substrate held by the substrate holding rotating unit; a nozzle moving mechanism for moving a liquid application position, the etching liquid discharged out of the nozzle being applied thereon, within the principal face of the substrate; and a control device for controlling the substrate holding rotating unit, the etching liquid supply mechanism, and the nozzle moving mechanism.” And in Fujii [0074] “The etching liquid nozzle 31 is rotationally moved from the position where the etching liquid nozzle 31 vertically opposes the edge portion of the top face of the substrate W and a position where the etching liquid nozzle 31 vertically opposes the center portion of the top face of the substrate W at a given rotational velocity (scan velocity). Accordingly an etching liquid application position is rotationally moved from the edge portion to the center portion of the top face of the substrate W at a given scan velocity. Thereby etching processing for the top face of the substrate W is carried out (scan step or step S3).”
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Zachary A Cain whose telephone number is (571)272-4503. The examiner can normally be reached Mon-Fri 7:00-3:30 CST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kenneth M Lo can be reached at (571) 272-9774. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Z.A.C./ Examiner, Art Unit 2116
/KENNETH M LO/ Supervisory Patent Examiner, Art Unit 2116
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