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
2. This Office Action responds to the Application filed on 9/11/2023 and IDS filed on 9/11/2023. Claims 1-20 are pending.
Claim Rejections - 35 USC § 103
3. 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.
4. Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Fonseca et al. (U.S. Pub. No. 2020/0135592 A1) in view of Mos et al. (U.S. Pub. No. 2011/0196646 A1).
As per claim 1, Fonseca discloses:
A method for determining models for describing measurement data relating to a parameter of interest over at least two substrate portions of a substrate, the method comprising:
obtaining the measurement data (See Figure 2, i.e. 210 – Obtain in-line measurements of baseline wafer & Para [0038], i.e. o obtain in-line measurements of one or more characteristics (e.g., resist CD of lines in a pattern of dense lines) of semiconductor wafers processed in accordance with a baseline fabrication process flow)
obtaining a plurality of substrate portion models comprising at least a first substrate portion model for describing the parameter of interest across one or more first substrate portions on the substrate and a second substrate portion model for describing the parameter of interest across one or more second substrate portions on the substrate, the one or more first substrate portions and the one or more second substrate portions being different substrate portions (See Para [0044]-[0046], i.e. FP model may be generated for each measurement type of in-line fabrication metrology data obtained at the process step, See Para [0072]-[0073], i.e. two types of measurements are performed at each of the four process… lowest level FP models may be generated from the in-line wafer fabrication metrology data at each step, for example, at step A there may be two measurement FP's –[prior art measurement type (See Para [0026]) correspond to the different portions, each type measure different feature on the substrate correspond to the different portions as cited above]);
and performing steps 1 to 4 for each of the plurality of substrate portion models, the steps comprising: 1. selecting a candidate basis function from a plurality of candidate basis functions (See Para [0044], i.e. elects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data, See Para [0044]-[0077]) ; 2. updating the substrate portion model by adding the candidate basis function into the substrate portion model; 3. evaluating, by a hardware computer system, the updated substrate portion model using the measurement data; and 4. determining whether to include the basis function within the substrate portion model based on the evaluation (See Para [0044], i.e. selects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data…are chosen such that the in-line measurement type of interest may be accurately modeled without having to use a very long series requiring long computation times, and the characteristics of the model are comparable to some physical components of the in-line measurement step, See Para [0044]-[0077] [prior art chose basis function , which result in low computation time, therefore prior art perform the step of 1-4 as cited above]).
Fonseca does not teach specifically the limitations: performing steps 1 to 4 iteratively for each of the plurality of substrate portion models until a stopping criterion is met, the step comprising: 1. selecting a candidate basis function from a plurality of candidate basis functions; 2. updating the substrate portion model by adding the candidate basis function into the substrate portion model; 3. evaluating, by a hardware computer system, the updated substrate portion model using the measurement data; and 4. determining whether to include the basis function within the substrate portion model based on the evaluation.
However, Mos teach the limitations: performing steps 1 to 4 iteratively for each of the plurality of substrate portion models until a stopping criterion is met (See Figure 16, i.e. s1602-s1604), the step comprising: 1. selecting a candidate basis function from a plurality of candidate basis functions (See Para [0055], i.e. choice of basis function or functions will be influenced by the form of the wafer); 2. updating the substrate portion model by adding the candidate basis function into the substrate portion model (See Figure 16, i.e. s1602 – fit alignment to refined model); 3. evaluating, by a hardware computer system, the updated substrate portion model using the measurement data; and 4. determining whether to include the basis function within the substrate portion model based on the evaluation (See Figure 16, i.e. s1604 – decide whether residuals … less than the residual…s1605 further refine model & Para [0070]-[0076]).
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 Mos into the
teaching of Fonseca because it would allow engineer to take measurement of alignment
and/or overlay on a wafer using intermediate points (See Para [0053]).
As per claim 2, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Fonseca also discloses wherein the one or more first substrate portions comprise one or more first exposure fields and the one or more second substrate portions comprise one or more second exposure fields (See Para [0083], i.e. every exposure field,
As per claim 4, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Fonseca also discloses wherein the one or more first substrate portions are located within a central area of the substrate and the one or more second substrate portions are located outside of the central area of the substrate (See Para [0048], i.e. a term that is dominant near the center of the wafer may be weak near the edge of the wafer).
As per claim 4, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Fonseca also discloses wherein step 3 comprises performing a fit of the measurement data based on the updated substrate portion model obtained in step 2 to determine a residual metric (See Para [0044], i.e. selects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data…are chosen such that the in-line measurement type of interest may be accurately modeled without having to use a very long series requiring long computation times, and the characteristics of the model are comparable to some physical components of the in-line measurement step, See Para [0044]-[0077]).
As per claim 5, Fonseca and Mos disclose all of the features of claim 4 as discloses above wherein Fonseca also discloses wherein step 3 comprises comparing the residual metric to a residual threshold limit; and rejecting the associated candidate basis function from the respective substrate portion model if the residual metric is below the residual threshold limit (See Para [0044], i.e. selects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data…are chosen such that the in-line measurement type of interest may be accurately modeled without having to use a very long series requiring long computation times, and the characteristics of the model are comparable to some physical components of the in-line measurement step, See Para [0044]-[0077]).
As per claim 6, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Mos also discloses determining at least a benefit metric for a basis function evaluated in each iteration, the benefit metric quantifying a benefit of including the basis function in the model (See Figure 16, i.e. s1604 – decide whether residuals … less than the residual…s1605 further refine model & Para [0070]-[0076]).
As per claim 7, Fonseca and Mos disclose all of the features of claim 6 as discloses above wherein Mos also discloses wherein the benefit metric comprises a benefit/cost metric having an associated cost associated with the basis function (See Figure 16, i.e. s1604 – decide whether residuals … less than the residual…s1605 further refine model & Para [0070]-[0076]).
As per claim 8, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Mos also discloses wherein step 4 is based on a model uncertainty metric (See Figure 16, i.e. s1604 – decide whether residuals … less than the residual…s1605 further refine model & Para [0070]-[0076]).
As per claim 9, Fonseca and Mos disclose all of the features of claim 8 as discloses above wherein Mos also discloses wherein step 4 comprises: determining the model uncertainty metric; comparing the model uncertainty metric to a model uncertainty threshold limit; and rejecting the associated candidate basis function from the respective substrate portion model if the model uncertainty metric is above the model uncertainty threshold limit (See Figure 16, i.e. s1604 – decide whether residuals … less than the residual…s1605 further refine model & Para [0070]-[0076]).
As per claim 10, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Fonseca also discloses comprising for each substrate portion model, an initial step of determining the plurality of candidate basis functions based on the measurement data and a baseline substrate portion model (See Para [0044], i.e. elects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data, See Para [0044]-[0077], See Para [0086]-[0087], i.e. obtained from wafers processed in accordance with the baseline process flow )
As per claim 11, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Fonseca also discloses a step per substrate portion model of ranking the basis functions based on the evaluations performed at step 3 (See Para [0044], i.e. selects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data…are chosen such that the in-line measurement type of interest may be accurately modeled without having to use a very long series requiring long computation times, and the characteristics of the model are comparable to some physical components of the in-line measurement step, See Para [0044]-[0077] –[prior art determine the most fit basis function, therefore include ranking as cited]).
As per claim 12, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Fonseca also discloses generating a first process control signal for the first substrate portion(s) using the first substrate portion model, and generating a second process control signal for the second substrate portion(s) using the second substrate portion model (See Para [0091]-[0092], i.e. optimize, adjust and/or control one or more of the process steps in order to achieve desired improvements in the manufacturing yield in production of wafers).
As per claim 13, Fonseca and Mos disclose all of the features of claim 1 as discloses above wherein Fonseca also discloses a non-transitory computer-readable medium comprising program instructions therein, the instructions, when executed by one or more processors, configured to cause the one or more processors to perform at least the method of claim 1 (See Para [0097], i.e. computer-readable storage medium).
As per claim 14, Fonseca and Mos disclose all of the features of claim 13 as discloses above wherein Fonseca also discloses a processing arrangement comprising: the medium of claim 13; and one or more processor operable to run the instructions comprised on the medium (See Para [0097], i.e. processor).
As per claim 15, Fonseca and Mos disclose all of the features of claim 14 as discloses above wherein Fonseca also discloses a metrology apparatus comprising the processing arrangement of claim 14 (See Para [0097], i.e. processor).
As per claim 16, Fonseca discloses:
A method for determining at least one substrate model for describing at least a first measurement dataset and a second measurement dataset, each measurement dataset relating to a performance parameter, the method comprising:
obtaining a plurality of candidate basis functions and/or model parameters for the at least one substrate model (See Para [0044], i.e. elects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data, See Para [0044]-[0077]);
obtaining a plurality of measurement datasets comprising at least the first measurement dataset and the second measurement dataset (See Figure 2, i..e 210 – Obtain in-line measurements of baseline wafer & Para [0038], i.e. o obtain in-line measurements of one or more characteristics (e.g., resist CD of lines in a pattern of dense lines) of semiconductor wafers processed in accordance with a baseline fabrication process flow, See Para [0072]-[0073], i.e. two types of measurements are performed at each of the four process… lowest level FP models may be generated from the in-line wafer fabrication metrology data at each step, for example, at step A there may be two measurement FP's); and
performing steps 1 to 4 for at least the first measurement dataset and the second measurement dataset so as to determine the at least one substrate model for each of the plurality of measurement datasets, said steps comprising: 1. selecting a candidate basis function and/or model parameter from the plurality of candidate basis functions and/or model parameters based on at least one of the plurality of datasets (See Para [0044], i.e. elects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data, See Para [0044]-[0077]) 2. updating a substrate model by adding the candidate basis function and/or model parameter into this substrate model to obtain an updated substrate model; 3. evaluating, by a hardware computer system, the updated substrate model based on at least one of the plurality of datasets; and 4. determining whether to include the basis function and/or model parameter within the substrate model based on the evaluation (See Para [0044], i.e. selects an appropriate mathematical function of the 2D spatial coordinates (x, y), or (r, θ) to model the data…are chosen such that the in-line measurement type of interest may be accurately modeled without having to use a very long series requiring long computation times, and the characteristics of the model are comparable to some physical components of the in-line measurement step, See Para [0044]-[0077] [prior art chose basis function , which result in low computation time, therefore prior art perform the step of 1-4 as cited above]).
Fonseca does not teach specifically the limitations: performing steps 1 to 4 iteratively for at least the first measurement dataset and the second measurement dataset until at least one stopping criterion is met so as to determine the at least one substrate model for each of the plurality of measurement datasets, said steps comprising: 1. selecting a candidate basis function and/or model parameter from the plurality of candidate basis functions and/or model parameters based on at least one of the plurality of datasets 2. updating a substrate model by adding the candidate basis function and/or model parameter into this substrate model to obtain an updated substrate model; 3. evaluating, by a hardware computer system, the updated substrate model based on at least one of the plurality of datasets; and 4. determining whether to include the basis function and/or model parameter within the substrate model based on the evaluation.
However, Mos teach the limitations: performing steps 1 to 4 iteratively for at least the first measurement dataset and the second measurement dataset until at least one stopping criterion is met (See Figure 16, i.e. s1602-s1604), so as to determine the at least one substrate model for each of the plurality of measurement datasets, said steps comprising: 1. selecting a candidate basis function and/or model parameter from the plurality of candidate basis functions and/or model parameters based on at least one of the plurality of datasets (See Para [0055], i.e. choice of basis function or functions will be influenced by the form of the wafer); 2. updating a substrate model by adding the candidate basis function and/or model parameter into this substrate model to obtain an updated substrate model; (See Figure 16, i.e. s1602 – fit alignment to refined model); 3. evaluating, by a hardware computer system, the updated substrate model based on at least one of the plurality of datasets; and 4. determining whether to include the basis function and/or model parameter within the substrate model based on the evaluation (See Figure 16, i.e. s1604 – decide whether residuals … less than the residual…s1605 further refine model & Para [0070]-[0076]).
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 Mos into the
teaching of Fonseca because it would allow engineer to take measurement of alignment
and/or overlay on a wafer using intermediate points (See Para [0053]).
As per claim 17, Fonseca and Mos disclose all of the features of claim 16 as discloses above wherein Mos also discloses wherein the performing steps 1 to 4 iteratively for each of the plurality of measurement datasets until at least one stopping criterion is met comprises iteratively performing steps 1 to 4 in a plurality of repetitions, each repetition for a respective one of each of the plurality of measurement datasets so as to determine a respective different substrate model for each of the plurality of measurement datasets, the at least one of the plurality of measurement datasets in steps 1 and 3 comprising the measurement dataset for that repetition (See Figure 16, i.e. s1604 – decide whether residuals … less than the residual…s1605 further refine model & Para [0070]-[0076]).
As per claim 18, Fonseca and Mos disclose all of the features of claim 16 as discloses above wherein Fonseca also discloses wherein the at least one substrate model comprises at least a first substrate portion model for describing the performance parameter across one or more first substrate portions on the substrate and a second substrate portion model for describing the performance parameter across one or more second substrate portions on the substrate, the one or more first substrate portions and the second one or more substrate portions being different substrate portions (See Para [0044]-[0046], i.e. FP model may be generated for each measurement type of in-line fabrication metrology data obtained at the process step, See Para [0072]-[0073], i.e. two types of measurements are performed at each of the four process… lowest level FP models may be generated from the in-line wafer fabrication metrology data at each step, for example, at step A there may be two measurement FP's –[prior art measurement type (See Para [0026]) correspond to the different portions, each type measure different feature on the substrate correspond to the different portions as cited above]), and wherein the first measurement dataset relates to the one or more first substrate portions and the second measurement dataset relates to the one or more second substrate portions (See Figure 2, i..e 210 – Obtain in-line measurements of baseline wafer & Para [0038], i.e. o obtain in-line measurements of one or more characteristics (e.g., resist CD of lines in a pattern of dense lines) of semiconductor wafers processed in accordance with a baseline fabrication process flow, See Para [0072]-[0073], i.e. two types of measurements are performed at each of the four process… lowest level FP models may be generated from the in-line wafer fabrication metrology data at each step, for example, at step A there may be two measurement FP's).
As per claim 19, Fonseca and Mos disclose all of the features of claim 16 as discloses above wherein Mos also discloses wherein the at least one substrate model comprises a common substrate model for each of the first measurement dataset and the second measurement dataset, the method comprising: in step 1, selecting the candidate basis function and/or model parameter based alternately on the first measurement dataset and the second measurement dataset for each successive iteration; and in step 3, performing the evaluating based on each of the first measurement dataset and the second measurement dataset in turn, in each iteration (See Para [0055], i.e. choice of basis function or functions will be influenced by the form of the wafer, See Figure 16, i.e. s1604 – decide whether residuals … less than the residual…s1605 further refine model & Para [0070]-[0076]).
As per claim 20, Fonseca and Mos disclose all of the features of claim 16 as discloses above wherein Fonseca also discloses a non-transitory computer-readable medium comprising program instructions therein, the instructions, when executed by one or more processors, configured to cause the one or more processors to perform at least the method of claim 16 (See Para [0097], i.e. computer-readable storage medium).
Conclusion
5. 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
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