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 .
OFFICE ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4 and 22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4 and 22 are rejected because the claimed acronyms are not spelled out. The first occurrence of acronyms in a claim group must be spelled out.
Claims 12 and 30 are rejected because the limitation “different fabrications tools, which are all of the same type” as presented is unclear, conflicting and contradicting regarding how different fabrications tools, which all can be of the same type. This limitation has no meets and bounds and is indefinite
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.
(a)(2) The claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-30 are rejected under 35 U.S.C. 102(a) (2) being anticipated by the prior art of record Li (US 2022/0283082)
Regarding claims 1, 3, 10, and 14-15, the prior art discloses:
A method of producing a (machine learning) ML model (title), the method comprising:
(a) receiving a first training set generated from a first set of wafers,
(abstract, summary), the first training set comprising (i) ex situ metrology data or wafer structure parameter values (see one or more of ex-situ/ stand-alone metrology system/data/dataset in one or more of par 23, 74, 76, 81, 88, 91, and/or fig 5-8 and related text), obtained from the first set of wafers after the first set of wafers has been processed, and (ii) in situ wafer-level, optical sensor data (see one or more of in-situ spectrographic monitoring, in-situ measurement / monitoring systemin one or more of abstract, background, summary, fig 1, 3-8 and related text) obtained from the first set of wafers while the first set of wafers was being processed;
(b) training a first ML model using the first training set
wherein the first ML model is configured to perform principal component analysis or utilizes a neural-network-based autoencoder ( see one or more of PCA or auto encoding in one or more of par in par 58-62), wherein the first ML model is configured to receive in situ wafer-level optical sensor data generated from a wafer undergoing processing and predict wafer structure parameter values (see one or more of in-situ spectrographic monitoring, in-situ measurement / monitoring systemin one or more of abstract, background, summary, fig 1, 3-8 and related text);
(c) using the first ML model to generate predicted wafer structure parameter values for a second set of wafers, wherein the second set of wafers are production wafers (see one or more of par 2-10, and/or fig 1-8 and related text), wherein the second set of wafers has associated in situ chemical composition data and associated in situ wafer-level optical sensor data obtained while the second set of wafers was being processed wafers (see one or more of par 2-10 and/or 1-8 and related text); and
(d) training a second ML model using a second training set comprising (i) the predicted wafer structure parameter values from (c), and (ii) the associated in situ chemical composition data obtained while the second set of wafers was being processed (see one or more of abstract, background summary and/or fig 1, 3-9 and related text) , wherein the second ML model is configured to receive in situ chemical composition data for a process wafer being processed and predict wafer structure parameter values of the process wafer at one or more times while the process wafer is being processed or after processing is completed (see one or more of abstract, background summary and/or fig 1, 3-9 and related text),
wherein the second ML model is configured to reduce a dimensionality of the in situ chemical composition data and/or perform feature extraction on in situ chemical composition data (see dimensional reduction in fig 4 and/or chemical composition data extraction in technical field or background and/or fig 7-8 and related text)
(Claim 2) wherein wafers of the first set of wafers do not have associated chemical composition data (see one or more of fig 1-6).
(Claim 4), wherein the standalone metrology tool is a CD-SAXS tool, a CD-SEM tool, or an optical metrology tool (fig 7).
(Claim 5) wherein the in situ wafer-level optical sensor data comprises optical intensity values at multiple wavelengths and multiple times (par 56-57, 66).
(Claim 6) wherein the in situ chemical composition data obtained while the second set of wafers was being processed is generated from an optical emission spectrometer (par 31, 33-34).
(Claim 7) wherein the second set of wafers does not have associated ex situ metrology data or wafer structure parameter values (fig 1-8 and related text).
(Claim 8) wherein the first set of wafers are pilot wafers (pilot wafers in terms one or more of test substrate, desirable/sample/model substrate, progress of substrates or substrate during processing, substrates under training/ learning process, etc. (abstract, background, summary, par 2, 22-23, 36, 77, 79)),
(Claim 9), wherein the first set of wafers was processed by an etch process (par 91)
(Claim 11) wherein the first set of wafers and second set of wafers were processed using the same type of fabrication tool (fig 1-4, 7-9)
(Claim 12) wherein the second ML model is configured to predict the wafer structure parameter values for multiple different fabrications tools, which are all of the same type, in an IC fabrication facility (fig 1-9, insofar the limitations are understood and given broadest reasonable interpretation)
(Claim 13) wherein the first ML model is configured to produce a reduced dimensional representation of the in situ wafer-level optical sensor data obtained from the first set of wafers and/or perform feature extraction on in situ wafer-level optical sensor data obtained from the first set of wafers (see fig 4 and/or 7-8 and related text).
(Claim 16) wherein the second ML model is configured to indicate when an etch process has reached an end point (one or more of par 5, 10, 38, 39, 73)
(Claim 17) wherein at least some wafers of the first of wafers are also in the second set of wafers (fig 1-8 and related).
(Claim 18) wherein the wafer structure parameter values comprise an etch depth, a critical dimension, a side-wall angle, a repeating feature pitch, a layer thickness, a layer material property, or any combination thereof (see one or more of summary, background, par 22, 24, 90 or fig 5)
Claims 19-30 recite similar subject matter and are rejected for the same reason.
Claims 1-13 and 15-30 are rejected under 35 U.S.C. 102(a) (1) being anticipated by the prior art of record Lian (US 2007/0249071)
Regarding claims 1, 3, 9-10 the prior art discloses:
A method of producing a (machine learning) ML model (title, abstract, summary, fig 1) , the method comprising:
(a) receiving a first training set generated from a first set of wafers ( abstract, summary, fig 1, 4), the first training set comprising (i) ex situ metrology data or wafer structure parameter values ( metrology tool ex-situ with respect to the etch process (par 56)), obtained from the first set of wafers after the first set of wafers has been processed (see during processing, pre-etch, during etch, and post-etch (i.e., substrate state information) stages of a processing step to train a neural network (fig 1-5 and related text)) the first set of wafers was processed by an etch process (see one or more of background, summary, fig 1-5 and related text) , and (ii) in situ wafer-level, optical sensor (in-situ substrate monitoring/measuring/detection (abstract, par 16, 49, 58, fig 1)) data obtained from the first set of wafers while the first set of wafers was being processed;
(b) training a first ML model using the first training set, wherein the first ML model is configured to receive in situ wafer-level optical sensor data generated from a wafer undergoing processing and predict wafer structure parameter values (fig 1-5 and related text);
(c) using the first ML model to generate predicted wafer structure parameter values for a second set of wafers, wherein the second set of wafers are production wafer (fig 4), wherein the second set of wafers has associated in situ chemical composition data (par 17, 27, 35, 38, 56) and associated in situ wafer-level optical sensor data obtained while the second set of wafers was being processed (fig 1-5 and related); and
(d) training a second ML model using a second training set comprising (i) the predicted wafer structure parameter values from (c), and (ii) the associated in situ chemical composition data obtained while the second set of wafers was being processed,
wherein the second ML model is configured to receive in situ chemical composition data for a process wafer being processed and predict wafer structure parameter values of the process wafer at one or more times while the process wafer is being processed or after processing is completed (fig 1-5 and related text)
(Claim 2) wherein wafers of the first set of wafers do not have associated chemical composition data (fig 1-5 and related text)
(Claim 4) wherein the standalone metrology tool is a CD-SAXS tool, a CD-SEM tool, or an optical metrology tool (abstract, par 8, fig 1)
(Claim 5) wherein the in situ wafer-level optical sensor data comprises optical intensity values at multiple wavelengths and multiple times (par 24-31, 47-50).
(Claim 6) wherein the in situ chemical composition data obtained while the second set of wafers was being processed is generated from an optical emission spectrometer (par 27, 35, 38, 56).
(Claim 7) wherein the second set of wafers does not have associated ex situ metrology data or wafer structure parameter values (fig 1-5 and related text)
(Claim 8) wherein the first set of wafers are pilot wafers (pilot in terms of one or more of relationship between the plurality of the training and other data related to substrate processing, improved method and apparatus for substrate monitoring and process control during the manufacture, acquiring training data related to a substrate disposed in a processing, profile of a substrate feature in a substrate processing system, adjust process time and control the operational status of a substrate processing equipment, variation within the manufacturing tolerances, during the training process, active training may be utilized as new data becomes available… substrate processing technique (e.g., etch) may be repeated to establish a model in order to obtain a set of optimum weighting factors, test product substrate (abstract, par 6, 8-9, 16, 29, 42-43, 56)
(Claim 11) wherein the first set of wafers and second set of wafers were processed using the same type of fabrication tool (par 17, 27, 33)
( Claim12) wherein the second ML model is configured to predict the wafer structure parameter values for multiple different fabrications tools, which are all of the same type, in an IC fabrication facility (fig 1-4, insofar the limitation is understood and given broadest reasonable interpretations)
(Claim 13) wherein the first ML model is configured to produce a reduced dimensional (par 4) representation of the in situ wafer-level optical sensor data obtained from the first set of wafers and/or perform feature extraction on in situ wafer-level optical sensor data obtained from the first set of wafers (fig 1-4).
(claim 15) wherein the second ML model is configured to reduce a dimensionality (par 4)of the in situ chemical composition data and/or perform feature extraction on in situ chemical composition data (fig 1-5)
(claim 16)wherein the second ML model is configured to indicate when an etch process has reached an end point (par 5, 37).
(Claim 17) wherein at least some wafers of the first of wafers are also in the second set of wafers (fig 1-5)
(Claim 18) wherein the wafer structure parameter values comprise an etch depth, a critical dimension, a side-wall angle, a repeating feature pitch, a layer thickness, a layer material property, or any combination thereof (par 4-9, 15-17, 26, 36-52 or fig 4-5 and related text).
Claims 19-30 recite similar subject matter and are rejected for the same reason,
Correspondence Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL DINH whose telephone number is 571-272-1890. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s Supervisor, Jack Chiang can be reached on 571-272-7483. The fax number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PAUL DINH/ Primary Examiner, Art Unit 2851
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