DETAILED ACTION
This office action addresses Applicant’s response filed on 9 March 2026. Claims 1-7, 9-19, and 22 are pending.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim(s) 1-3, 7, 9-11, and 15-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tripodi (US 2019/0378012) in view of Ravid (US 2008/0243433) and Lindeman (US 2021/0081698.)
Regarding claim 1, Tripodi discloses, a method comprising: obtaining spectral data associated with a first portion of a first prior substrate at a manufacturing system and additional spectral data associated with at least one of a second portion of the first prior substrate or a third portion of a second prior substrate at the manufacturing system (¶¶76-77, 86); identifying one or more metrology measurement values measured for the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate (¶¶74, 83); determining a metrology measurement value associated with the first portion of the first prior substrate (¶83); generating training data for training a machine learning model to predict metrology measurement values of a current substrate at the manufacturing system (¶76), wherein generating the training data comprises: generating a first training data set comprising the spectral data associated with the first portion of the first prior substrate (¶¶76-77, 86) and the determined metrology measurement value associated with the first portion of the first prior substrate (¶¶74, 83); generating a second training data set comprising the additional spectral data associated with the at least one of a second portion of the first prior substrate or a third portion of a second prior substrate at the manufacturing system (¶¶76-77, 86) and the identified one or more metrology measurement values measured for the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate (¶¶74, 83); and providing the training data to train the machine learning model on the first training data set and the second training data set (¶¶79, 83).
Tripodi does not appear to explicitly disclose determining a metrology measurement value associated with the first portion of the first prior substrate based on the identified one or more metrology measurement values and generating a second training . Ravid discloses the obtaining spectral data associated with a first portion of a first prior substrate at a manufacturing system and additional spectral data associated with at least one of a second portion of the first prior substrate or a third portion of a second prior substrate at the manufacturing system (Fig. 3, step 304; Fig. 7; ¶58); identifying one or more metrology measurement values obtained for the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate (Fig. 3, step 302; Fig. 5); determining a metrology measurement value associated with the first portion of the first prior substrate based on the identified one or more metrology measurement values (¶67); generating a first training data set comprising the spectral data associated with the first portion of the first prior substrate (Fig. 7; ¶58) and the determined metrology measurement value associated with the first portion of the first prior substrate (¶67); generating a second training data set comprising the additional spectral data associated with the at least one of a second portion of the first prior substrate or a third portion of a second prior substrate at the manufacturing system (Fig. 7; ¶58) and the identified one or more metrology measurement values measured for the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate (Fig. 5)
It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Tripodi and Ravid, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of establishing relationships between measured data while needing fewer measurements. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Tripodi discloses training data comprising spectral data and measurement values for various portions of a substrate. Ravid teaches that measurement values for additional portions can be interpolated from existing measurement data, and that spectral data and measurement values are associated with particular locations to define a library establishing relationships between all of the collected data. The teachings of Ravid are directly applicable to Tripodi in the same way, so that Tripodi would similarly use spectral data and measurement data at particular locations to establish relationships between measured data while requiring fewer measurements.
Tripodi does not appear to explicitly disclose updating the trained machine learning model such that one or more model weights associated with the one or more metrology measurement values measured for the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate are higher than one or more additional weights associated with the determined metrology measurement value associated with the first portion of the first prior substrate. Lindeman discloses these limitations (¶80). Furthermore, if Tripodi and/or Ravid are found to be unclear regarding first and second data sets, Lindeman also discloses the same (¶¶79-80). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Tripodi, Ravid, Werkman, and Lindeman, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of emphasizing measured data over synthetic data. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Tripodi discloses datasets for training a machine learning model, and Ravid teaches interpolated/synthetic datasets. Lindeman teaches that real and synthetic datasets are weighted in the machine learning model to emphasize one over the other. The teachings of Lindeman are directly applicable to Tripodi and Ravid, so that Tripodi would similarly weight real and synthetic datasets to emphasize measured data over synthetic data.
Regarding claim 2, Tripodi does not appear to explicitly disclose that determining the metrology measurement value associated with the first portion of the first prior substrate comprises: providing, as input to a function, an indication of one or more first coordinates associated with the first portion of the first prior substrate, one or more second coordinates associated with at least one of the second portion of the first prior substrate or the third portion of the second prior substrate, and the one or more metrology measurement values measured for the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate, wherein the metrology measurement value associated with the first portion of the substrate is determined based on one or more outputs of the function. Ravid discloses these limitations (¶67). Motivation to combine remains consistent with claim 1.
Regarding claim 3, Tripodi does not appear to explicitly disclose that the function comprises at least one of a linear interpolation function, an extrapolation function, a nearest-neighbor interpolation function, or a Euclidean distance function. Ravid discloses these limitations (¶67). Motivation to combine remains consistent with claim 1.
Regarding claim 7, Tripodi does not appear to explicitly disclose that determining the metrology measurement value associated with the first portion of the first prior substrate comprises: determining a first radial distance between a center portion of the first prior substrate and the first portion of the first prior substrate; and determining a second radial distance between at least one of the center portion of the first prior substrate and the second portion of the first prior substrate or a center portion of the second prior substrate and the third portion of the second prior substrate, responsive to determining that the first radial distance corresponds to the second radial distance, determining that the metrology measurement value associated with the first portion of the first prior substrate corresponds to at least one of the identified one or more metrology measurement values obtained for the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate. Ravid discloses these limitations (¶¶79, 107). Motivation to combine remains consistent with claim 1.
Claims 9-11 and 15 are directed to a system comprising a memory and a processing device coupled to the memory, the processing device to perform the methods of claims 1-3 and 7, and are rejected under the same reasoning. Tripodi discloses a system comprising a memory and a processing device coupled to the memory for performing the claimed methods (¶74).
Claims 16-18 are directed to non-transitory computer readable storage media for performing the methods of claims 1-3, and are rejected under the same reasoning. Tripodi discloses a non-transitory computer readable storage media for performing the claimed methods (¶¶15, 74).
Claim(s) 4, 6, 12, 14, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tripodi in view of Ravid, Lindeman, Werkman (US 2021/0405544), and McNeil (US 5,867,276).
Regarding claims 4, 12, and 19, Tripodi discloses that determining the metrology measurement value associated with the first portion of the substrate comprises: providing the obtained spectral data associated with the first portion of the first prior substrate and additional contextual data associated with the first prior substrate as input to an machine learning model, wherein the machine learning model is trained to predict, based on given spectral data and additional contextual data for prior substrates at the manufacturing system, metrology measurement values of the prior substrates, and wherein the machine learning model is trained using a dataset comprising the spectral data associated with the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate and the one or more metrology measurement values measured for at least one of the second portion of the first prior substrate or the third portion of the second prior substrate; and extracting the metrology measurement value from one or more outputs of the additional machine learning model (¶¶74, 79, 83, 86). Werkman also discloses additional contextual data (¶¶67-72). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Tripodi, Ravid, Lindeman, and Werkman, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way, or the combination of known elements according to known techniques, to achieve the predictable results of enhancing a metrology dataset with contextual information to provide additional data. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Tripodi discloses a training dataset comprising spectral data and measurement values for various portions of a substrate. Werkman teaches that the dataset should be enhanced to also include contextual information, such as process information. The teachings of Werkman are directly applicable to Tripodi in the same way, so that Tripodi would similarly enhance the training dataset using contextual information.
Tripodi does not appear to explicitly disclose that the machine learning model is an additional machine learning model, but such a limitation constitutes merely obvious duplication of parts, since both the machine learning model and additional machine learning model are trained to determine metrology measurement values from input spectral data. See MPEP § 2144.04(VI)(B).
Nevertheless, McNail discloses determining the metrology measurement value associated with the first portion of the substrate comprises: providing the obtained spectral data associated with the first portion of the first prior substrate and contextual data associated with the first prior substrate as input to an additional machine learning model, wherein the additional machine learning model is trained to predict, based on given spectral data and contextual data for prior substrates at the manufacturing system, metrology measurement values of the prior substrates, and wherein the additional machine learning model is trained using a dataset comprising the spectral data associated with the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate and the one or more metrology measurement values measured for at least one of the second portion of the first prior substrate or the third portion of the second prior substrate; and extracting the metrology measurement value from one or more outputs of the additional machine learning model (col. 8, lines 55-63; col. 9, lines 29-35).
It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Tripodi, Ravid, Lindeman, Werkman, and McNeil, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of generating additional measurement data through machine learning. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Tripodi discloses training data comprising spectral and measurement data. Ravid teaches that additional measurement data can be generated from existing spectral and measurement data, and McNeil teaches that such generation can be performed by training a machine learning model to generate the measurement data. The teachings of McNeil are directly applicable to Tripodi and Ravid so that Tripodi would similarly generate additional training measurement data using a machine learning model.
Regarding claims 6 and 14, Tripodi does not appear to explicitly disclose that the contextual data associated with the first prior substrate comprises at least one of one or more first coordinates associated with the first portion of the first prior substrate, a substrate process performed for the first prior substrate, a time period during which the substrate process was performed for the first prior substrate, a time period during which the spectral data for the first portion of the first prior substrate was collected, or an indication of one or more types of equipment used to perform the substrate process. However, Tripodi discloses that the contextual data includes location (¶86), which persons having ordinary skill in the art would understand to include coordinates; Ravid also explicitly discloses coordinates (Figs. 5, 7). Werkman also discloses the claimed additional contextual data (¶¶67-72). Motivation to combine remains consistent with claims 1 and 4.
Claim(s) 5 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tripodi in view of Ravid, Lindeman, Werkman, McNeil, and Ophir (US 2021/0142466).
Regarding claims 5 and 13, Tripodi does not appear to explicitly disclose that the one or more outputs of the additional machine learning model comprise metrology data indicating one or more sets of metrology measurement values and, for each set of metrology measurement values, a level of confidence that a respective set of metrology measurement values corresponds to the first portion of the first prior substrate, and wherein extracting the metrology measurement value from the one or more outputs comprises: identifying the respective set of metrology measurement values having a level of confidence that satisfies a confidence criterion, wherein the identified respective set of metrology measurement values includes the metrology measurement value. Ophir discloses these limitations (Fig. 1, boxes 130, 140; ¶21). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Tripodi, Ravid, Lindeman, Werkman, McNeil, and Ophir, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way to achieve the predictable results of improving accuracy of derived values. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Tripodi discloses training data comprising spectral and measurement data. Ravid teaches that additional measurement data can be generated from existing spectral and measurement data, and McNeil teaches that such generation can be performed by training a machine learning model to generate the measurement data. Outputs of machine learning models are well-known to have corresponding confidence values, as taught by Ophir. The teachings of Ophir are directly applicable to Tripodi, Ravid, and McNeil, so that Tripodi would similarly use generated measurement data having sufficiently high confidence values in order to improve the accuracy of the measurement data set.
Claim(s) 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tripodi in view of Ravid, Lindeman, and Werkman.
Regarding claim 22, Tripodi does not appear to explicitly disclose obtaining contextual data associated with the at least one of the first prior substrate or the second prior substrate, the obtained contextual data comprising one or more of an indication of a substrate process previously performed for the at least one of the first prior substrate or the second prior substrate, an indication of a time period during which the substrate process was performed, an indication of a time period during which the spectral data associated with the first portion of the first prior substrate or the at least one of the second portion of the first prior substrate or the third portion of the second prior substrate was obtained, or an indication of one or more types of equipment associated with the substrate process that was performed, wherein the first training data set further comprises the obtained contextual data. Werkman discloses these limitations (¶¶67-69, 73). It would have been obvious to persons having ordinary skill in the art before the effective filing date of the application to combine the teachings of Tripodi, Ravid, Lindeman, and Werkman, because doing so would have involved merely the routine use of a known technique to improve similar devices in the same way, or the combination of known elements according to known techniques, to achieve the predictable results of enhancing a metrology dataset with contextual information to provide additional data. KSR Int’l Co. v. Teleflex Inc., 82 U.S.P.Q.2d 1385, 1396. Tripodi discloses a training dataset comprising spectral data and measurement values for various portions of a substrate. Werkman teaches that the dataset should be enhanced to also include contextual information, such as process information. The teachings of Werkman are directly applicable to Tripodi in the same way, so that Tripodi would similarly enhance the training dataset using contextual information.
Response to Arguments
Applicant’s arguments have been considered but are moot in view of the new grounds of rejection. Applicant asserts that the prior art fails to teach newly-added limitations, which are addressed above using newly-cited prior art.
Conclusion
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21 March 2026
/ARIC LIN/Examiner, Art Unit 2851