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 .
DETAILED ACTION
This Office Action responds to the Request for Continued Examination filed
11/25/2025.
Claims 2-23 are pending.
Response to Applicant’s Remarks
4. With respect to Applicant’s remarks, the following are addressed:
Applicant’s arguments with respect to claim(s) 2-6 and 9-23 have been considered but are moot because the new ground of rejection(s) as cited below.
Applicant’s remarks (Pages 9-10) with respect to claims 7 and 8 are persuasive – therefore claims 7 and 8 are now objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Double Patenting rejections of the claims are withdrawn in view of Terminal Disclaimer filed by Applicant.
The rejection(s) of the claims under new ground of rejection(s) are as cited below. This office action is Non-Final.
Claim Rejections - 35 USC § 103
5. 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.
6. Claim(s) 2-6, 9-11 and 13-23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ohmori et al. (U.S. Pub. No. 2019/0064751 A1) in view of Kaushal et a. (U.S. Pub. No. 2013/0151447 A1).
As per claim 2, Ohmori discloses:
A method comprising:
performing a plurality of experiments for processing and forming a semiconductor device, each experiment controlled by a semiconductor device process recipe from a plurality of semiconductor device process recipes that identifies parameters for manufacturing equipment used for the processing of the semiconductor device (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0080]-[0100], See Para [0305]) ;
obtaining a machine-learning (ML) model by training an ML algorithm using experimental results from the plurality of semiconductor device process recipes (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0081]-[0086], i.e. generate a prediction model…input parameter prepared as learning model –[Prior art generate prediction using measured from process as learning data is considered as the obtaining as cited above], See Para [0114]);
receiving specifications for a desired processing of the semiconductor device (See Para [0083], i.e. input of a target, See Para [0087]-[0088], i.e. gives the target value … to the prediction model, See Para [0080]-[0100], See Para [0102]-[0127]); and
creating, by the ML model, a new recipe for processing the semiconductor device based on the specifications (See Para [0088]-[0089], i.e. acquire a value of an input parameter…corresponding from the prediction model…determine a target value corresponding to a predicted value is closer to a target value, See Para [0080]-[0100], See Para [0102]-[0127] –[prior art use predicted model (ML model), in order to predict setting (new recipe) (See Figure 4, i.e. 402 , 404 and setting unit 405 ).
Ohmori does not teach the limitations: at least one experiment of the plurality of experiments performed with a semiconductor device process recipe that is modified based on a result of a previous experiment.
However, Kaushal teach the limitations: at least one experiment of the plurality of experiments performed with a semiconductor device process recipe that is modified based on a result of a previous experiment (See Para [0161], i.e. At act 2030, a set of relationships associated with one or more product output metrics is learned through drifting… an adjusted recipe is generated to accomplish the target product output based at least in part on the set of learned relationships, See Figure 20, i.e. 2020 – initial recipe…learn…product output…2040- generate an adjusted recipe).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Kaushal into
the teaching of Ohmori because it would allow manufacturer to dynamically optimize
tool performance in a process (See Para [0006]).
As per claim 3, Ohmori and Kaushal discloses all of the features of claim 2 as discloses above wherein Ohmori also discloses wherein the ML model is based on a plurality of features that includes at least one parameter selected from parameters including recipe features, experimental-results features, virtual-result features, and metrology features (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0081]-[0086], i.e. generate a prediction model…input parameter prepared as learning model).
As per claim 4, Ohmori and Kaushal discloses all of the features of claim 3 as discloses above wherein Ohmori also discloses wherein the metrology features include one or more of imaging methods, transmission electron microscopy, typical-thickness measurement, sheet resistance, surface resistivity, stress measurement, and analytical methods used to determine at least one characteristic selected from characteristics including layer thickness, composition, grain, and orientation (See Para [0009], i.e. thickness of a deposited film, See Para [0083], See also Figures 15-19).
As per claim 5, Ohmori and Kaushal discloses all of the features of claim 3 as discloses above wherein Ohmori also discloses wherein the recipe features include one or more of the following parameters including workflow, gas flows, chamber temperature, chamber pressure, step durations, and radio-frequency (RF) values (See Para [0009], i.e. gas flow rate…heating temperature, See Para [0082], i.e. gas flow rate).
As per claim 6, Ohmori and Kaushal discloses all of the features of claim 2 as discloses above wherein Ohmori also discloses wherein the ML model includes active process control to determine process parameters to satisfy control objectives, the input to the ML model including the control objectives for the recipe and desired active process control (See Para [0088]-[0089], i.e. acquire a value of an input parameter…corresponding from the prediction model…determine a target value corresponding to a predicted value is closer to a target value, See Para [0080]-[0100], See Para [0102]-[0127] –[prior art use predicted model (ML model), in order to predict setting (new recipe) (See Figure 4, i.e. 402 , 404 and setting unit 405 ).
As per claim 9, Ohmori and Kaushal discloses all of the features of claim 2 as discloses above wherein Ohmori also discloses wherein the experimental results include values measured from the processing of the semiconductor device, the values including one or more of lateral ratio, isotropic ratio, deposition depth, global sticking coefficient, surface dependent sticking coefficient, delay thickness, neutral-to-ion ratio, and ion angular-distribution function (See Para [0059]-[0060], i.e. deposition monitor in a treatment, Para [0083], Figures 15-19).
As per claim 10, Ohmori and Kaushal discloses all of the features of claim 2 as discloses above wherein Ohmori also discloses wherein each of the plurality of experiments is performed on a semiconductor-manufacturing apparatus based on the recipe for the experiment, wherein one experiment is performed to measure effects of changing a value of one parameter from a previous recipe used in a previous experiment (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0080]-[0100]).
As per claim 11, Ohmori and Kaushal discloses all of the features of claim 2 as discloses above wherein Ohmori also discloses wherein the processing of the semiconductor device is for a deposition process using an inhibition profile (See Para [0009], i.e. thickness of a deposited film, See Para [0083], See also Figures 15-19)
As per claim 13, Ohmori discloses:
A system comprising: a memory comprising instructions; and one or more computer processors, the instructions, when executed by the one or more computer processors, cause the system to perform operations (See Para [0055], i.e. computer and database) comprising:
performing a plurality of experiments for processing and forming a semiconductor device, each experiment controlled by a semiconductor device process recipe from a plurality of semiconductor device process recipes that identifies parameters for manufacturing equipment used for the processing of the semiconductor device (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0080]-[0100], See Para [0305]);
obtaining a machine-learning (ML) model by training an ML algorithm using experimental results from the plurality of semiconductor device process recipes (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0081]-[0086], i.e. generate a prediction model…input parameter prepared as learning model –[Prior art generate prediction using measured from process as learning data is considered as the obtaining as cited above]), See Para [0114];
receiving specifications for a desired processing of the semiconductor device (See Para [0083], i.e. input of a target, See Para [0087]-[0088], i.e. gives the target value … to the prediction model, See Para [0080]-[0100], See Para [0102]-[0127]); and
creating, by the ML model, a new recipe for processing the semiconductor device based on the specifications (See Para [0088]-[0089], i.e. acquire a value of an input parameter…corresponding from the prediction model…determine a target value corresponding to a predicted value is closer to a target value, See Para [0080]-[0100], See Para [0102]-[0127] –[prior art use predicted model (ML model), in order to predict setting (new recipe) (See Figure 4, i.e. 402 , 404 and setting unit 405 ).
Ohmori does not teach the limitations: at least one experiment of the plurality of experiments performed with a semiconductor device process recipe that is modified based on a result of a previous experiment.
However, Kaushal teach the limitations: at least one experiment of the plurality of experiments performed with a semiconductor device process recipe that is modified based on a result of a previous experiment (See Para [0161], i.e. At act 2030, a set of relationships associated with one or more product output metrics is learned through drifting… an adjusted recipe is generated to accomplish the target product output based at least in part on the set of learned relationships, See Figure 20, i.e. 2020 – initial recipe…learn…product output…2040- generate an adjusted recipe).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Kaushal into
the teaching of Ohmori because it would allow manufacturer to dynamically optimize
tool performance in a process (See Para [0006]).
As per claim 14, Ohmori and Kaushal discloses all of the features of claim 13 as discloses above wherein Ohmori also discloses wherein the ML model is based on a plurality of features that includes at least one parameter selected from parameters including recipe features, experimental-results features, virtual-result features, and metrology features (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0081]-[0086], i.e. generate a prediction model…input parameter prepared as learning model).
As per claim 15, Ohmori and Kaushal discloses all of the features of claim 14 as discloses above wherein Ohmori also discloses wherein the metrology features include one or more of imaging methods, transmission electron microscopy, typical-thickness measurement, sheet resistance, surface resistivity, stress measurement, and analytical methods used to determine at least one characteristic selected from characteristics including layer thickness, composition, grain, and orientation (See Para [0009], i.e. thickness of a deposited film, See Para [0083], See also Figures 15-19).
As per claim 16, Ohmori and Kaushal discloses all of the features of claim 14 as discloses above wherein Ohmori also discloses wherein the recipe features include one or more of the following parameters including workflow, gas flows, chamber temperature, chamber pressure, step durations, and radio-frequency (RF) values (See Para [0009], i.e. gas flow rate…heating temperature, See Para [0082], i.e. gas flow rate).
As per claim 17, Ohmori and Kaushal discloses all of the features of claim 13 as discloses above wherein Ohmori also discloses wherein the experimental results include values measured from the processing of the semiconductor device, the values including one or more of lateral ratio, isotropic ratio, deposition depth, global sticking coefficient, surface dependent sticking coefficient, delay thickness, neutral-to-ion ratio, and ion angular-distribution function (See Para [0059]-[0060], i.e. deposition monitor in a treatment, Para [0083], Figures 15-19).
As per claim 18, Ohmori and Kaushal discloses all of the features of claim 13 as discloses above wherein Ohmori also discloses wherein each of the plurality of experiments is performed on a semiconductor-manufacturing apparatus based on the recipe for the experiment, wherein one experiment is performed to measure effects of changing a value of one parameter from a previous recipe used in a previous experiment (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0080]-[0100]).
As per claim 19, Ohmori discloses:
A tangible machine-readable storage medium including instructions that, when executed by a machine, cause the machine to perform operations (See Para [0055], i.e. computer and database) comprising:
performing a plurality of experiments for processing and forming a semiconductor device, each experiment controlled by a semiconductor device process recipe from a plurality of semiconductor device process recipes that identifies parameters for manufacturing equipment used for the processing of the semiconductor device (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0080]-[0100], See Para [0305]);
obtaining a machine-learning (ML) model by training an ML algorithm using experimental results from the plurality of semiconductor device process recipes (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0081]-[0086], i.e. generate a prediction model…input parameter prepared as learning model –[Prior art generate prediction using measured from process as learning data is considered as the obtaining as cited above], See Para [0114]);
receiving specifications for a desired processing of the semiconductor device (See Para [0083], i.e. input of a target, See Para [0087]-[0088], i.e. gives the target value … to the prediction model, See Para [0080]-[0100], See Para [0102]-[0127]); and
creating, by the ML model, a new recipe for processing the semiconductor device based on the specifications (See Para [0088]-[0089], i.e. acquire a value of an input parameter…corresponding from the prediction model…determine a target value corresponding to a predicted value is closer to a target value, See Para [0080]-[0100], See Para [0102]-[0127] –[prior art use predicted model (ML model), in order to predict setting (new recipe) (See Figure 4, i.e. 402 , 404 and setting unit 405 ).
Ohmori does not teach the limitations: at least one experiment of the plurality of experiments performed with a semiconductor device process recipe that is modified based on a result of a previous experiment.
However, Kaushal teach the limitations: at least one experiment of the plurality of experiments performed with a semiconductor device process recipe that is modified based on a result of a previous experiment (See Para [0161], i.e. At act 2030, a set of relationships associated with one or more product output metrics is learned through drifting… an adjusted recipe is generated to accomplish the target product output based at least in part on the set of learned relationships, See Figure 20, i.e. 2020 – initial recipe…learn…product output…2040- generate an adjusted recipe).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Kaushal into
the teaching of Ohmori because it would allow manufacturer to dynamically optimize
tool performance in a process (See Para [0006]).
As per claim 20, Ohmori and Kaushal discloses all of the features of claim 19 as discloses above wherein Ohmori also discloses wherein the ML model is based on a plurality of features that includes at least one parameter selected from parameters including recipe features, experimental-results features, virtual-result features, and metrology features (See Para [0056]-[0061], i.e. recipe setting controller…monitors or measure a treatment object, See Para [0061], i.e. stores values of various input parameters set by the apparatus control system…stores learning data … input and output learning data, See Para [0081]-[0086], i.e. generate a prediction model…input parameter prepared as learning model).
As per claim 21, Ohmori and Kaushal discloses all of the features of claim 20 as discloses above wherein Ohmori also discloses wherein the metrology features include one or more of imaging methods, transmission electron microscopy, typical-thickness measurement, sheet resistance, surface resistivity, stress measurement, and analytical methods used to determine at least one characteristic selected from characteristics including layer thickness, composition, grain, and orientation (See Para [0009], i.e. thickness of a deposited film, See Para [0083], See also Figures 15-19).
As per claim 22, Ohmori and Kaushal discloses all of the features of claim 20 as discloses above wherein Ohmori also discloses wherein the recipe features include one or more of the following parameters including workflow, gas flows, chamber temperature, chamber pressure, step durations, and radio-frequency (RF) values (See Para [0009], i.e. gas flow rate…heating temperature, See Para [0082], i.e. gas flow rate).
As per claim 23, Ohmori and Kaushal discloses all of the features of claim 19 as discloses above wherein Ohmori also discloses wherein the experimental results include values measured from the processing of the semiconductor device, the values including one or more of lateral ratio, isotropic ratio, deposition depth, global sticking coefficient, surface dependent sticking coefficient, delay thickness, neutral-to-ion ratio, and ion angular-distribution function (See Para [0059]-[0060], i.e. deposition monitor in a treatment, Para [0083], Figures 15-19).
7. Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over
Ohmori et al. (U.S. Pub. No. 2019/0064751 A1) in view of Kaushal et a. (U.S. Pub. No. 2013/0151447 A1) and further in view of Shrestha et al. (U.S. Pub. No. 2019/0067014 A1).
As per claim 12, Ohmori and Kaushal discloses all of the features of claim 2 as discloses above.
Ohmori and Kaushal does not teach the limitations: wherein the processing of the component is for a deposition in a three-dimensional NAND word-line (WL) fill.
However, Shrestha disclose the limitations: wherein the processing of the component is for a deposition in a three-dimensional NAND word-line (WL) fill (See Para [0031]).
Therefore, it would have been obvious to a person of ordinary skill in the
art at the effective filing date of the invention to incorporate the teaching of Shrestha into
the teaching of Ohmari and Kaushal because it would lower effective electrical resistivity
of interconnect in logic application (See Para [0031]).
Allowable Subject Matter
8. Claims 7 and 8 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
9. The following is a statement of reasons for the indication of allowable subject matter: The prior art does not teach the limitations of claim 7, wherein claim 8 depend on claim 7.
Conclusion
10. Any inquiry concerning this communication or earlier communications from the examiner should be directed to NHA T NGUYEN whose telephone number is (571)270-1405. The examiner can normally be reached M-F 8:00AM-5:00PM.
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/NHA T NGUYEN/Primary Examiner, Art Unit 2851