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 .
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.
Claims 16-22 and 27 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pandev (US 2018/0252514).
As per claim 16, Pandev teaches, a method comprising: obtaining metrology data comprising at least one asymmetry signal (Pandev, ¶[0053] “because the measurement model is trained based on scatterometry data collected from a particular metrology system” this represents obtaining metrology data, and ¶[0089] “he SCOL difference signal representative of measured asymmetry.” Represents at least one asymmetry signal), the at least one asymmetry signal comprising a difference or imbalance in a measurement parameter from at least one target on a substrate (Pandev, ¶[0089] “the SCOL difference signal representative of measured asymmetry.” This represents asymmetry signal comprising a difference); obtaining a trained model having been trained or configured to relate the at least one asymmetry signal to a parameter of interest relating to the at least one target on the substrate (Pandev, ¶[0099] “In block 205, a measurement model is trained based on the estimated values of actual overlay and the amount of training data. The measurement model is structured to receive measurement data generated by a metrology system at one or more measurement sites, and directly determine the overlay associated with each measurement target.” And ¶[0015] “FIG. 3 depicts a plot 20 of several inaccuracy landscapes, each associated with a different value of line profile asymmetry. Plotline 21 depicts inaccuracy with no line profile asymmetry. Plotline 22 depicts inaccuracy with line profile asymmetry of 2 nanometers. Plotline 23 depicts inaccuracy with line profile asymmetry of 4 nanometers. Plotline 24 depicts inaccuracy with line profile asymmetry of 8 nanometers. As illustrated in FIG. 3, as structural asymmetry increases, for example, due to printing errors, the amplitude of the induced inaccuracy of the overlay measurement increases. In this example, the increase in inaccuracy is linearly proportional to the amplitude of the line profile asymmetry.” This represents obtaining a trained model having been trained or configured to relate the at least one asymmetry signal, and fig.5 showing the substrate), the trained model comprising at least one proxy for at least one nuisance component of the at least one asymmetry signal (Pandev, ¶[0053] “By using only raw scatterometry data to create the measurement model, as described herein, the errors and approximations associated with traditional model based metrology methods are reduced. In addition, the measurement model is not sensitive to systematic errors, asymmetries, etc., because the measurement model is trained based on scatterometry data collected from a particular metrology system and used to perform measurements based on scatterometry data collected from the same metrology system.” Approximations is the proxy and nuisance the error of at least , asymmetries as disclosed above); and inferring the parameter of interest for the at least one target from the at least one asymmetry signal using the trained model ( Pandev, ¶[0060] “For instance, a measurement model or an overlay parameter 121 determined by computer system 130 may be communicated and stored in an external memory. In this regard, measurement results may be exported to another system.” parameter 121 represents Inferring since is the process of drawing logical conclusions or educated guesses about information that isn’t directly stated. Since it is that measurement and ¶[0069] “In this example, computing system 130 is further configured to determine cross-wafer variations of one or more process parameters and cross-wafer variations of one or more structural parameters that induce variations in overlay measurement. In this manner, the process variations and structural parameter variations are determined as a function of location on a DOE wafer (e.g., {x,y} coordinates).”).
As per claim 17, Pandev teaches, the method of claim 16, wherein the metrology data comprises after-develop metrology data, measured prior to an etch step for the layer just exposed (Pandev, ¶[0035] “In another aspect, the measurement model results described herein can be used to provide active feedback to a process tool (e.g., lithography tool, etch tool, deposition tool, etc.). In a similar way etch parameters (e.g., etch time, diffusivity, etc.) or deposition parameters (e.g., time, concentration, etc.) may be included in a measurement model to provide active feedback to etch tools or deposition tools, respectively.” This represents the metrology data comprises after-develop metrology data, measured prior to an etch step for the layer just exposed, since this is a feedback tool ).
As per claim 18, Pandev teaches, the method of claim 16, wherein the at least one asymmetry signal comprises at least one asymmetry signal measured at an image plane or conjugate thereof of a metrology tool used to obtain the metrology data (Pandev, ¶ [0042] “FIG. 6 illustrates a method suitable for implementation by a metrology system such as metrology system 100 illustrated in FIG. 5 of the present invention.” metrology system would represent the tool as such ).
As per claim 19, Pandev teaches, the method of claim 16, wherein the trained model comprises a configured training parameter for each of the at least one proxies (Pandev, ¶[0053] “By using only raw scatterometry data to create the measurement model, as described herein, the errors and approximations associated with traditional model based metrology methods are reduced. In addition, the measurement model is not sensitive to systematic errors, asymmetries, etc., because the measurement model is trained based on scatterometry data collected from a particular metrology system and used to perform measurements based on scatterometry data collected from the same metrology system.” Approximations is the proxy and nuisance the error of at least , asymmetries as disclosed above ).
As per claim 20, Pandev teaches, the method of claim 16, wherein the at least one proxy comprises one or both of: at least one symmetric proxy for a symmetric nuisance component of each asymmetry signal of the at least one asymmetry signal; and/or at least one asymmetric proxy for an asymmetric nuisance component of each asymmetry signal of the at least one asymmetry signal (Pandev, ¶[0053] “By using only raw scatterometry data to create the measurement model, as described herein, the errors and approximations associated with traditional model based metrology methods are reduced. In addition, the measurement model is not sensitive to systematic errors, asymmetries, etc., because the measurement model is trained based on scatterometry data collected from a particular metrology system and used to perform measurements based on scatterometry data collected from the same metrology system.” Approximations is the proxy, examiner only has to address one here).
As per claim 21, Pandev teaches, the method of claim 20, wherein the at least one asymmetry signal comprises: a measurement parameter asymmetry between each diffraction order of a pair of complementary diffraction orders (Pandev, ¶[0078] “In some embodiments, system 100 collects pupil images of light diffracted at the +1/−1 diffraction orders from each measured metrology target.” This represents a measurement parameter asymmetry between each diffraction order of a pair of complementary diffraction orders by diffraction orders from each measured metrology target being disclosed).
As per claim 22, Pandev teaches, the method of claim 21, wherein the measurement parameter is intensity or a related parameter (Pandev, ¶ [0136] “An effective target design or measurement structure propagates a non-zero diffraction order between the first pattern and the second pattern such that the relative positions of the two patterns affect the intensity of the out-going diffraction beam detected in the far field.” This represents the measurement parameter is intensity, then being measured after being effected).
As per claim 27, Pandev teaches, the method of claim 16, wherein each said at least one target comprises only a single sub-target type, or a single sub-target type per measurement direction (Pandev, ¶ [0131] “In general, signals from multiple targets each measured by multiple metrology techniques increases the information content in the combined set of signals and reduces the overlay correlation to process or structural parameter variations.” a single sub-target type by having multiple targets ).
Allowable Subject Matter
Claims 23-26 and 28-35 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. The reason being is that while interpreting data from multiple targets simultaneously is a known concept in advanced process control, applying it directly via trained machine learning models to extract parameters represents a highly specialized, and specific approach as in claim 35 for example. As far as claim 23, the specific limitations are new because it defines a semiconductor metrology method to decouple structural asymmetry from tool-induced measurement errors. In scatterometry and optical overlay metrology (such as monitoring wafer alignment during chip fabrication), asymmetric profile anomalies in target gratings cause severe measurement inaccuracies. By defining a symmetric proxy that mathematically sums the parameters of complementary diffraction orders (e.g., \(+1\) and \(-1\)), the system isolates pure geometric tool errors from actual wafer anomalies. The Symmetric Proxy Function works by combining or summing their parameter values creates a virtual baseline indicator (the proxy). Because a sum acts as an even (symmetric) mathematical function, any structural asymmetry on the wafer naturally cancels out, all of which was not found in the prior art with a broad and also specific search. As per claim 24, the subject matter decouples structural process-induced errors from intentional positional parameters, correcting for asymmetric target deformation that traditional metrology tools mistakenly misinterpret as alignment errors, and this was not found in the prior art in the related art that would have the rest of the subject matter that this depends on.
Note regarding additional prior art
Tinnemans (US 2019/0265028) “¶[0001] The present invention relates to a metrology apparatus or an inspection apparatus for determining a characteristic of structures on a substrate. The present invention also relates to a method for determining a characteristic of structures on a substrate.” This reference should be considered also in terms of reading on the rejected claims when considering amendments. The rest of the prior arts are co-owned by applicant however do not read on the current claim inventions only on some parts.
Conclusion
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/SANTIAGO GARCIA/Primary Examiner, Art Unit 2673
/SG/
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