DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a1).
Claim Rejections - 35 USC § 102
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.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a1) as being anticipated by US Publication 2022/0090912 to Sanko et al.
In regards to claims 1-20, Sanko discloses and shows in Figures 1-3, a metrology system and method (par. 3) comprising:
an illumination source (102) configured to generate a beam of radiation incident on a semiconductor structure (101) disposed on a semiconductor wafer under measurement, the semiconductor structure under measurement including a plurality of structural features (Figure 1) (par. 43-44);
a detector (104) configured to detect radiation from the semiconductor structure under measurement in response to the incident beam of radiation and generate a set of actual measurement signals indicative of the detected radiation (par. 43-44); and
a computing system (130) (par. 45-46, 108, 112-113) configured to:
estimate a first value of a parameter of interest characterizing the structure under measurement based on the detected radiation (par. 4, 7-8, 12), wherein the estimating of the value of the parameter of interest involves a trained measurement model of the structure under measurement (par. 4, 7-9, 22, 36, 38), the measurement model trained based at least in part on a training set of component measurement signals and corresponding Design Of Experiment (DOE) values of the parameter of interest (par. 9, 11, 40, 47, 50, 63), wherein the training set of component measurement signals is indicative of a measurement response of a subset of the plurality of structural features of the semiconductor structure to measurement by the metrology system (par. 23, 47, 49, 62; wherein a subset of the training data may be comprised of the measured and/or simulated data from a particular target from a plurality of targets on a wafer);
[claims 2 and 13] wherein the estimating of the value of the parameter of interest involves: extracting a set of component measurement signals from the set of actual measurement signals (par. 7, 9, 11, 16, 22-23, 36, 47, 49, 62-63; wherein a plurality of physical and machine learning models are generated by iteratively adjusting one or more machine or specimen parameters); and fitting the trained measurement model to the set of component measurement signals (par. 7, 11, 19, 53; wherein training data and measurement data are compared to determine a quality metric);
[claim 3] wherein the training set of component measurement signals includes component measurement signals extracted from a training set of measurement signals indicative of a measured response of the plurality of structural features of the semiconductor structure to measurement by the metrology system (par. 16, 19, 22-23, 36, 47, 62; wherein the training data includes measurement data, simulated data, or both);
[claim 4 and 14] wherein the training set of component measurement signals includes synthetic component measurement signals generated by a component measurement model evaluated at a range of values of the parameter of interest and a range of values of one or more measurement system parameters (par. 7, 11, 16, 19, 22-23, 36, 47, 62; wherein the training data includes measurement data, simulated data, or both; and wherein a plurality of physical and machine learning models are generated by iteratively adjusting one or more machine or specimen parameters);
[claim 5 and 15] wherein the training set of component measurement signals includes component measurement signals generated by a measurement of the semiconductor structure at a prior process state, wherein the subset of the plurality of structural features of the semiconductor structure are present at the prior process state (par. 7, 23, 49, 107; wherein a library of various parameter models may be searched; and training or measurement data may be collected from multiple different metrology systems);
[claim 6] the measurement model also trained based at least in part on a training set of measurement signals and corresponding Design of Experiment (DOE) values of the parameter of interest, wherein the training set of measurement signals are indicative of a measurement response of the plurality of structural features of the semiconductor structure to measurement by the metrology system (par. 9, 11, 40, 47, 50, 63);
[claim 7 and 16] the measurement model also trained based at least in part on a training set of historical component measurement signals, wherein the historical component measurement signals are indicative of a measurement response of a historical version of the subset of the plurality of structural features of the semiconductor structure to measurement by the metrology system (par. 7, 23, 49, 107; wherein a library of various parameter models may be searched; and training or measurement data may be collected from multiple different metrology systems) (par. 9, 11, 40, 47, 50, 63; wherein previously obtained reference data and reference models, having known DOE values, may be utilized);
[claim 8] wherein the historical version of the subset of the plurality of structural features of the semiconductor structure differs from the subset of the plurality of structural features of the semiconductor structure in a design revision, a process recipe, or both (par. 25-27, 40, 42; wherein an active feedback process may be performed to determine necessary changes in process parameters or recipe);
[claim 9 and 17] wherein the trained measurement model is a machine learning based measurement model or a library-based measurement model (par. 4, 7-8, 12);
[claim 10 and 18] the computing system further configured to: estimate a second value of the parameter of interest characterizing the structure under measurement, wherein the estimating of the second value of the parameter of interest involves fitting a second measurement model to the set of actual measurement signals, wherein the first value of the parameter of interest is employed as a seed value in the fitting of the second measurement model to the set of actual measurement signals (par. 7, 11, 16, 19, 22-23, 36, 47, 62; wherein the training data includes measurement data, simulated data, or both; and wherein a plurality of physical and machine learning models are generated by iteratively adjusting one or more machine or specimen parameters; and wherein the generated models are iteratively compared to measurement data to determine a quality metric model and optimize semiconductor manufacturing);
[claim 11 and 19] wherein the amount of radiation includes electron radiation, electromagnetic radiation in an x-ray range, electromagnetic radiation in an optical range, or any combination thereof (par. 3, 43).
Conclusion
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JONATHAN M. HANSEN
Primary Examiner
Art Unit 2877
/JONATHAN M HANSEN/Primary Examiner, Art Unit 2877
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