DETAILED ACTION
This Office Action is in response to Applicant's Communication received on 07/12/2023 for application number 18/221,301.
Claims 1-20 are presented for examination. Claims 1, 10, and 16 are independent claims.
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 07/12/2023 and 12/05/2024 have been considered by the Examiner.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 5-10, 13-16, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Honda et al. (US 2021/0142122 A1 hereinafter Honda).
Regarding Claim 1, Honda teaches a method ([0002] classification of wafers during semiconductor manufacturing), comprising:
obtaining, by a processing device ([0014] processor-based tool that provides a visual display of information in a formatted manner):
first data indicative of substrate generation parameters of a first substrate ([0059] other inputs can include the process history of each wafer (i.e., substrate) from the wafer equipment history (WEH) including process module, tool ID, and chamber ID; load the history of processes and tools for a given wafer ID and product; [0047] drilldown capability could be implemented for each wafer, for example, to review WEH, metrology, defect, indicator, PCM, etc. (i.e., generation parameters)),
second data indicative of properties of the first substrate ([0017] column 126 identifies the current classification (i.e., data indicative of properties) for the wafer in that row as determined by a first rule-based (RB) model; column 127 identifies the classification as determined by a collaborative learning (CL) model; [0018] one or more wafer maps such as maps 141, 142 (i.e., data indicative of properties) are displayed for a row that has been selected),
third data indicative of substrate generation parameters of a second substrate ([0018] rows 122A and 122B are highlighted to indicate they have been selected (i.e., including second substrate); [0059] other inputs can include the process history of each wafer from the wafer equipment history (WEH) including process module, tool ID, and chamber ID; load the history of processes and tools for a given wafer ID and product; [0047] drilldown capability could be implemented for each wafer, for example, to review WEH, metrology, defect, indicator, PCM, etc. (i.e., generation parameters)), and
fourth data indicative of properties of the second substrate ([0018] rows 122A and 122B are highlighted to indicate they have been selected (i.e., including second substrate);[0017] column 126 identifies the current classification (i.e., data indicative of properties) for the wafer in that row as determined by a first rule-based (RB) model; column 127 identifies the classification as determined by a collaborative learning (CL) model; [0018] one or more wafer maps such as maps 141, 142 (i.e., data indicative of properties) are displayed for a row that has been selected);
providing a user interface (UI), the UI comprising a first UI element for presenting a visual depiction of the second data and a second UI element for presenting a visual depiction of the fourth data ([0014] GUI for classifying wafers; [0016] GUI 100 includes two main windows or panels: a first window 110 for wafer information, and a second window 140 for wafer maps, such as maps 141, 142; [0018] a set of corresponding wafer maps 141A and 142A for the wafer identified in rows 122A and 122B, respectively, are concurrently displayed in the second window 140 - the wafer maps/ UI elements visually depict properties of first and second wafers/ substrates);
receiving a first user input comprising a first user classification of the first substrate in relation to the second data and a second user input comprising a second user classification of the second substrate in relation to the fourth data ([0024] the user can select one or more wafers or lots to review and consider, and perhaps dive deeper into the data to try and better understand any unexplained anomalies or excursions before settling on a final classification; the user can provide feedback to either confirm the classifications, or to enter a different classification, as in column 128; claim 1 - receiving the user input from the first user interactive element or the second user interactive element of the display to establish a final classification);
performing analysis relating the first data and the third data to the first user classification and the second user classification ([0025] the classification information stored via database 212 is also used to train an ML model in module 211 such that the ML prediction model in module 204 is updated periodically; [0031] user input 330, including confirmations, updates, and comments, allows the classification model to learn and grow - thus, training the machine learning model on user classifications together with each wafer's WEH process history is an analysis relating substrate generation parameters to the user classifications); and
performing a corrective action based on the analysis ([0025] the ML prediction model in module 204 is updated periodically; [0031] user input 330, including confirmations, updates, and comments, allows the classification model to learn and grow; claim 2 - training the machine learning model on the basis of the initial classification, the predicted classification and the final classification for plurality of wafers).
As to dependent Claim 5, Honda teaches all the limitations of Claim 1. Honda further teaches wherein the first UI element comprises a visualization of geometry of the first substrate ([0018] rows 122A and 122B are highlighted to indicate they have been selected, and therefore a set of corresponding wafer maps 141A and 142A for the wafer identified in rows 122A and 122B, respectively, are concurrently displayed in the second window 140; [0030] the BinMap, namely the wafer (x, y) coordinate of the chip and the Bin for the chip; the BinMap can be transformed using convolution in a Convolution Neural Network; (2) a zonal summary, namely, for each zone (center, outer, top right, etc.), compute the count of wafers for each Bin that have been transformed using z-transformation; and (3) other typical transformations, such Wavelet, 2D FFT, transformed on the 2D Wafer BinMap).
As to dependent Claim 6, Honda teaches all the limitations of Claim 1. Honda further teaches wherein determining, based on the analysis, that values of a first substrate generation parameter of the first data and the third data are correlated with the first user classification and the second user classification ([0059] other inputs can include the process history of each wafer from the wafer equipment history (WEH) including process module, tool ID, and chamber ID; [0025] the classification information stored via database 212 is also used to train an ML model in module 211 such that the ML prediction model in module 204 is updated periodically; [0031] user input 330, including confirmations, updates, and comments, allows the classification model to learn and grow; [0049] known root-cause information can be stored in root-cause storage 214 and also associated or linked with specific excursions or defects in the database 212 - thus, the trained model encodes correlation between generation parameters in WEH and user provided classifications); and updating the UI to display data indicative of an effect of values of the first substrate generation parameter on substrate properties ([0049] the root-cause information can also be made available to the user in GUI 206 as part of the user's manual classification review as drill-down information, and likewise, the user feedback can be provided and incorporate into root-cause learning as well; [0016] GUI 100 includes two main windows or panels: a first window 110 for wafer information, and a second window 140 for wafer maps, such as maps 141, 142; [0017] column 124 identifies the wafer lot while column 125 identifies the specific wafer; column 126 identifies the current classification for the wafer in that row as determined by a first rule-based (RB) model, typically through heuristic and deterministic methods; column 127 identifies the classification as determined by a collaborative learning (CL) model, and column 128 identifies a user-entered modification to the final classification).
As to dependent Claim 7, Honda teaches all the limitations of Claim 1. Honda further teaches wherein the corrective action comprises: updating a process recipe; providing the first user classification for training a machine learning model; providing an alert to a user; scheduling generation of a third substrate; or updating a substrate processing simulation model ([0025] the ML prediction model in module 204 is updated periodically; [0031] user input 330, including confirmations, updates, and comments, allows the classification model to learn and grow; claim 2 - training the machine learning model on the basis of the initial classification, the predicted classification and the final classification for plurality of wafers).
As to dependent Claim 8, Honda teaches all the limitations of Claim 1. Honda further teaches wherein the analysis comprises determining a boundary between a first region of substrate generation parameter space associated with a first property corresponding to the first substrate and a second region of processing condition space associated with a second property corresponding to the second substrate ([0057] the process engineer should be provided with the functionality to identify boundary conditions/parameter cutoffs for this product flow, either automatically or manually; [0061] dc=a fixed distance that is close enough to the selected wafer to consider it identical; df=a fixed distance that is considered far away enough to consider there is no similarity; [0032] The algorithmic element 350 can be defined by any typical classifier with or without hyper-parameter tuning including, for example, K-Nearest neighbors, Robust Logic Regression with regularization, Naïve Bayes, Multilayer Perception and other Neural Networks, Linear & Nonlinear SVM, an ensemble of Decision Trees).
As to dependent Claim 9, Honda teaches all the limitations of Claim 1. Honda further teaches wherein the first user classification and the second user classification comprise a ranking of a target property of the first and second substrates ([0036] model-based prediction decisioning and model-based confidence scoring (i.e., ranking) should also be available to review; [0048] the collaborative classification method could be automated into the process workflow; for displayed results, wafers have the least model confidence can be displayed towards the top to ensure user review).
Regarding Claim 10, Honda teaches a non-transitory machine-readable storage medium storing instructions which, when executed, cause a processing device to perform operations ([0014] the GUI 100 is a processor-based tool that provides a visual display of information in a formatted manner and for providing control functions, all as generally known; [0015] the processor could be desktop-based or part of a networked system; but given the heavy loads of information to be processed and interactively displayed, processor capabilities (CPU, RAM, etc.) should be current state-of-the-art to maximize effectiveness) comprising:
obtaining: first data indicative of processing conditions of a first substrate ([0014] processor-based tool that provides a visual display of information in a formatted manner; [0059] other inputs can include the process history of each wafer (i.e., substrate) from the wafer equipment history (WEH) including process module, tool ID, and chamber ID; load the history of processes and tools for a given wafer ID and product; [0047] drilldown capability could be implemented for each wafer, for example, to review WEH, metrology, defect, indicator, PCM, etc.),
second data indicative of properties of the first substrate ([0017] column 126 identifies the current classification (i.e., data indicative of properties) for the wafer in that row as determined by a first rule-based (RB) model; column 127 identifies the classification as determined by a collaborative learning (CL) model; [0018] one or more wafer maps such as maps 141, 142 (i.e., data indicative of properties) are displayed for a row that has been selected),
third data indicative of processing conditions of a second substrate ([0018] rows 122A and 122B are highlighted to indicate they have been selected (i.e., including second substrate); [0059] other inputs can include the process history of each wafer from the wafer equipment history (WEH) including process module, tool ID, and chamber ID; load the history of processes and tools for a given wafer ID and product; [0047] drilldown capability could be implemented for each wafer, for example, to review WEH, metrology, defect, indicator, PCM, etc.), and
fourth data indicative of properties of the second substrate ([0018] rows 122A and 122B are highlighted to indicate they have been selected (i.e., including second substrate);[0017] column 126 identifies the current classification (i.e., data indicative of properties) for the wafer in that row as determined by a first rule-based (RB) model; column 127 identifies the classification as determined by a collaborative learning (CL) model; [0018] one or more wafer maps such as maps 141, 142 (i.e., data indicative of properties) are displayed for a row that has been selected);
providing a user interface (UI), the UI comprising a first UI element for presenting a visual depiction of the second data and a second UI element for presenting a visual depiction of the fourth data ([0014] GUI for classifying wafers; [0016] GUI 100 includes two main windows or panels: a first window 110 for wafer information, and a second window 140 for wafer maps, such as maps 141, 142; [0018] a set of corresponding wafer maps 141A and 142A for the wafer identified in rows 122A and 122B, respectively, are concurrently displayed in the second window 140 - the wafer maps/ UI elements visually depict properties of first and second wafers/ substrates);
receiving a first user input comprising a first user classification of the first substrate in relation to the second data and a second user input comprising a second user classification of the second substrate in relation to the fourth data ([0024] the user can select one or more wafers or lots to review and consider, and perhaps dive deeper into the data to try and better understand any unexplained anomalies or excursions before settling on a final classification; the user can provide feedback to either confirm the classifications, or to enter a different classification, as in column 128; claim 1 - receiving the user input from the first user interactive element or the second user interactive element of the display to establish a final classification);
performing analysis relating the first data and the third data to the first user classification and the second user classification ([0025] the classification information stored via database 212 is also used to train an ML model in module 211 such that the ML prediction model in module 204 is updated periodically; [0031] user input 330, including confirmations, updates, and comments, allows the classification model to learn and grow - thus, training the machine learning model on user classifications together with each wafer's WEH process history is an analysis relating substrate generation parameters to the user classifications); and
performing a corrective action based on the analysis ([0025] the ML prediction model in module 204 is updated periodically; [0031] user input 330, including confirmations, updates, and comments, allows the classification model to learn and grow; claim 2 - training the machine learning model on the basis of the initial classification, the predicted classification and the final classification for plurality of wafers).
Claims 13-15 are medium claims corresponding to the method claims 6, 7, and 9 respectively and therefore, rejected for the same reasons.
Claims 16, 19-20 are system claims corresponding to the medium claim 10 and similar to the method claims 6 and 9 respectively and therefore, rejected for the same reasons. Honda further teaches wherein system, comprising memory and a processing device coupled to the memory, wherein the processing device is configured to perform method ([0014] the GUI 100 is a processor-based tool that provides a visual display of information in a formatted manner and for providing control functions, all as generally known; [0015] the processor could be desktop-based or part of a networked system; but given the heavy loads of information to be processed and interactively displayed, processor capabilities (CPU, RAM, etc.) should be current state-of-the-art to maximize effectiveness).
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 2-3, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Honda in view of Sawlani et al. (US 2022/0374572 A1 hereinafter Sawlani).
As to dependent Claim 2, Honda teaches all the limitations of Claim 1. However, Honda fails to expressly teach wherein providing first simulation parameters associated with the first data to a substrate processing simulation model; and obtaining output indicative of the second data from the substrate processing simulation model, wherein the first substrate is a simulated substrate.
In the same field of endeavor, Sawlani teaches wherein providing first simulation parameters associated with the first data to a substrate processing simulation model ([0110] performing a plurality of virtual simulations for processing the component, each simulation controlled by one recipe from the plurality of recipes; obtaining a machine-learning (ML) model by training an ML algorithm using experiment results and virtual results from the virtual simulations; [0058] the recipe features include parameters associated with the recipe, such as workflow, gas flows, chamber temperature, chamber pressure, step durations, radiofrequency (RF) values (e.g., frequencies, voltages), etc. (i.e., simulation parameters)); and obtaining output indicative of the second data from the substrate processing simulation model, wherein the first substrate is a simulated substrate ([0110] obtaining a machine-learning (ML) model by training an ML algorithm using experiment results and virtual results from the virtual simulations; [0059] the experiment-result features and the virtual-result features include values measured from the resulting semiconductor, such as conformality, lateral ratio, isotropic ratio, deposition depth, global sticking coefficient, surface dependent sticking coefficient, delay thickness, neutral-to-ion ratio, ion angular distribution function, etc.).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein providing first simulation parameters associated with the first data to a substrate processing simulation model; and obtaining output indicative of the second data from the substrate processing simulation model, wherein the first substrate is a simulated substrate, as taught by Sawlani into Honda. Doing so would be desirable because it would reduce the process development time to obtain a recipe that can set all the process parameters to obtain components that meet the desired qualification metrics (Sawlani [0007]), thereby resulting in higher operational efficiency (Sawlani [0050]).
As to dependent Claim 3, Honda and Sawlani teach all the limitations of Claim 2. Sawlani further teaches wherein processing a substrate using process conditions indicated by the third data ([0036] the simulation tool 206 builds a three-dimensional model of what will happen on a substrate if the recipe 202 (i.e., third data) were run through the process, and the simulation tool 206 generates simulation results, which are measured by metrology 214; the metrology 214 provides measurements of the simulation results 212, and the metrology 214 includes items such as layer thickness, resistivity, etc.); performing one or more metrology operations to generate substrate metrology data of the substrate ([0036] the simulation tool 206 generates simulation results, which are measured by metrology 214; the metrology 214 provides measurements of the simulation results 212, and the metrology 214 includes items such as layer thickness, resistivity, etc.; image analysis may be used to examine the simulation results 212and the metrology 214 includes items such as layer thickness, resistivity, etc.); and producing one or more images based on the substrate metrology data, wherein the fourth data comprises the one or more images ([0036] the simulation tool 206 builds a three-dimensional model of what will happen on a substrate if the recipe 202 were run through the process, and the simulation tool 206 generates simulation results, which are measured by metrology 214; the metrology 214 provides measurements of the simulation results 212, and the metrology 214 includes items such as layer thickness, resistivity, etc.; image analysis may be used to examine the simulation results 212 (i.e., fourth data comprising image) and the metrology 214 includes items such as layer thickness, resistivity, etc.; image analysis may be used to examine the simulation results 212).
Claim 11 is a medium claim corresponding to the method claim 2 above and therefore, rejected for the same reasons.
Claim 17 is a system claim similar to the method claim 2 above and therefore, rejected for the same reasons.
Claims 4, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Honda in view of Sundar et al. (US 2022/0246457 A1 hereinafter Sundar).
As to dependent Claim 4, Honda teaches all the limitations of Claim 1. Honda further teaches wherein a plot displaying a first data marker associated with the first substrate and a second data marker associated with the second substrate ([0004] the template is configured such that the GUI is generated to contain and display for user review wafer information including results of wafer inspections; a typical wafer information display will include at least wafer identifying information, wafer classification information, and wafer map images; [0005] recognition of clusters in the wafer maps; [0014] GUI for classifying wafers; [0016] GUI 100 includes two main windows or panels: a first window 110 for wafer information, and a second window 140 for wafer maps, such as maps 141, 142; [0018] rows 122A and 122B are highlighted to indicate they have been selected, and therefore a set of corresponding wafer maps 141A and 142A for the wafer identified in rows 122A and 122B, respectively, are concurrently displayed in the second window 140. See fig. 1 - it shows the wafer maps/ plots displaying data markers/ cluster points associated with the wafers/ first and second substrates), and wherein the first UI element is displayed proximate the first data marker and the second UI element is displayed proximate the second data marker ([0004] the template is configured such that the GUI is generated to contain and display for user review wafer information including results of wafer inspections; a typical wafer information display will include at least wafer identifying information, wafer classification information, and wafer map images; [0005] recognition of clusters in the wafer maps; [0014] GUI for classifying wafers; [0016] GUI 100 includes two main windows or panels: a first window 110 for wafer information, and a second window 140 for wafer maps, such as maps 141, 142; [0018] rows 122A and 122B are highlighted to indicate they have been selected, and therefore a set of corresponding wafer maps 141A and 142A for the wafer identified in rows 122A and 122B, respectively, are concurrently displayed in the second window 140. See fig. 1 - it shows the data markers/ cluster points in the wafer maps/ UI elements).
However, Honda fails to expressly teach wherein the plot indicates values of a first substrate generation parameter and a second substrate generation parameter associated with the first substrate and the second substrate.
In the same field of endeavor, Sundar teaches wherein the plot indicates values of a first substrate generation parameter and a second substrate generation parameter associated with the first substrate and the second substrate ([0062] GUI 500 for detecting outliers at a manufacturing system; GUI 500 can be configured to display data associated with one or more sensor health ratings determined for a particular sensor, in accordance with previously described embodiments; it include a first portion 510 configured to provide a graphical representation of the received health ratings for a sensor during one or more processes such as process P1, process P2, etc. of the manufacturing system. See fig. 5 - it shows the plot showing health rating for different processes/ indicating generation parameter associated with the substrate) .
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to have incorporated wherein the plot indicates values of a first substrate generation parameter and a second substrate generation parameter associated with the first substrate and the second substrate, as taught by Sundar into Honda. Doing so would be desirable because it would allow the operator of the manufacturing system to identify a defective sensor that is generating inaccurate data for substrates of the manufacturing system (Sundar [0003]).
Claim 12 is a medium claim corresponding to the method claim 4 above and therefore, rejected for the same reasons.
Claim 18 is a system claim similar to the method claim 4 above and therefore, rejected for the same reasons.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 CFR § 1.111(c) to consider these references fully when responding to this action.
Venkataraman et al. (US 2016/0358041 A1) teaches: receiving a signal from a user interface device indicative of a first manual classification of a selected number of defects from each of the two or more groups of defect types; generating a classifier based on the received first manual classification and the attributes of the defects; classifying, with the classifier, one or more defects not manually classified by the manual classification; identifying a selected number of defects classified by the classifier having the lowest confidence level; receiving a signal from the user interface device indicative of an additional manual classification of the selected number of the defects having the lowest confidence level; determining whether the additional manual classification identifies one or more additional defect types not identified in the first manual classification; responsive to the identification by the additional manual classification of one or more defect types not identified by the first manual classification, the method generates an additional classifier and repeats the classification and analysis process (see [0007]).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REJI KARTHOLY whose telephone number is (571)272-3432. The examiner can normally be reached on Monday - Thursday from 7:30 am to 3:30 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jennifer Welch, can be reached at telephone number 571-272-7212. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/REJI KARTHOLY/Primary Examiner, Art Unit 2143
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