DETAILED ACTION
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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 17-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 17-36 are directed to a method for determining a measure of overlay and a non-transitory machine-readable medium having instructions therein (i.e., a process on a computer). Thus, each of the claims falls within one of the four statutory categories including process and machine.
Step 2A, Prong 1: Claims 17 and 32 are recite the abstract idea identified in bold:
obtain a measure of asymmetry, wherein the measure of asymmetry is based, at least in part, on an electromagnetic measurement of a target structure; and
determine, by a hardware computer and based at least in part on a trained machine learning model, a measure of overlay for the target structure based on the measure of asymmetry.
The abstract idea falls into the category of using mathematical calculations. Determining a measure of overlay based on a measure of asymmetry using a trained machine learning model, under a broadest reasonable interpretation, includes neural network optimization algorithms such as Gradient Descent, Levenberg-Marquardt, and Backpropogation that minimize a loss function and improve accuracy using specific mathematical calculations (see example 47, claim 2, of the July 2024 Subject Matter Eligibility Examples).
Step 2A, Prong 2 and Step 2B: The step of obtaining a measure of asymmetry, wherein the measure of asymmetry is based, at least in part, on an electromagnetic measurement of a target structure, represents mere data gathering and output, and thus an insignificant extra-solution activity and not a practical application (see MPEP 2106.05(g)). The type of data including a measure of overlay and asymmetry associated with a target structure generally links the data to a technological environment of metrology, but does not provide significantly more, because there is no practical application of the determination step and the field of use only confines the data to metrology without adding an inventive concept (see MPEP 2106.05(h)).
Claims 17 and 32 recite additional elements that includes: “a hardware computer" and "a non-transitory machine-readable medium having instructions therein". The claimed additional elements do not make the claim a practical application because they are performing to recite at a high level of generality and generic computer functions or software routinely used in generic computer components or software in the claim. The computer additional elements can be implemented as generic computer components which are merely used as tools to perform the abstract idea (see MPEP § 2106.05(f)). There is no particular machine (discounting the generic computer components) for applying the abstract idea (see MPEP § 2106.05(b)), and there is no real-world transformation in the claim (see MPEP § 2106.05(c)).
The claims does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements when considered separately and in combination, as a whole, do not add significantly more to the exception.
Dependent claims 18-31 and 33-36 are dependent on their respective base claims 17 and 32, and include all the limitations and abstract idea of their respective base claims. The additional limitations recited in claims 18-31 and 33-36 are each functional generic/conventional processing steps performed by computer components and comprise data gathering and processing steps which correspond to concepts identified as an abstract idea, or ideas, in the form of a mental process or mathematical calculation. Claims 18-21, 25-27, 31, 33, 34, and 36 include limitations that are associated with the type of data including overlay measurement associated with a target structure that represents mere data gathering and output, and thus an insignificant extra-solution activity and not a practical application (see MPEP 2106.05(g)). Additionally, type of data including overlay measurement generally links the data to a technological environment of metrology, but does not add significantly more, because there is no practical application of the determination step and the field of use only confines the data to metrology without adding an inventive concept (see MPEP 2106.05(h)). Claims 22-24, 28-30, and 35 include limitations that are directed to the abstract idea by including further determination steps and generation steps that incorporate training data and further model data. Claims 18-31 and 33-36 are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitations fail to establish that the claims are not directed to an abstract idea without significantly more. Therefore, claims 18-31 and 33-36 are rejected under 101 U.S.C. 101 as being directed to non-statutory subject matter.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claims 17, 18, 23-28, 31, 32, and 36 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tripodi et al. [2019/0378012].
For claims 17 and 32, Tripodi teaches a non-transitory, machine-readable media having instructions therein, the instructions, when executed by one or more processors, configured to cause the one or more processors to at least:
obtain a measure of asymmetry, wherein the measure of asymmetry is based, at least in part, on an electromagnetic measurement of a target structure (input image 1030 of target structure, where input image includes asymmetry images gathered from scatterometer, see [0059], [0061]-[0064], [0078], and [0081] and Figs. 5(a)-10); and
determine, by a hardware computer (see [0015] and [0074]) and based at least in part on a trained machine learning model (trained model or overlay, see Fig. 9 and [0078]), a measure of overlay (output a value for the characteristic of interest 1050 includes overlay, see [0078]-[0079] and [0082]) for the target structure based on the measure of asymmetry.
For claim 18, Tripodi teaches the measure of overlay is an overlay error value or an overlay value (value output of overlay extent, see [0062] and [0082]).
For claim 23 and 36, Tripodi teaches the instructions are further configured to cause the one or more processors to generate training data, wherein the trained machine learning model is trained based at least in part on the training data and wherein the training data comprises a measure of asymmetry associated with a measure of overlay for a set of perturbations of the target structure (training data includes reference data including target and stack variations, see [0099]-[0104] and [0127]-[0131]).
For claim 24, Tripodi teaches the instructions are further configured to cause the one or more processors to: determine a set of perturbation parameters based at least in part on a stack structure (target variations and stack perturbations, see [0099]-[0104] and [0128]-[0129]), wherein the stack structure comprises the target structure and the set of perturbation parameters comprises overlay and/or critical distance (overlay and CD, see [0128]); and generate the set of perturbations of the target structure based, at least in part, on the set of perturbation parameters (simulated training measurement data on simulated or reference stacks, see [0127]).
For claim 25, Tripodi teaches the measure of asymmetry is determined based on a simulation of the electromagnetic measurement of the set of perturbations of the target structure (simulated training measurement data on simulated or reference stacks to create a reference measurement set, see [0127]).
For claim 26, Tripodi teaches the measure of overlay is determined based on a model of a perturbation of the target structure, and wherein the set of perturbation parameters comprises critical distance and/or overlay (the target geometries may be simulated, see [0098]-[0104]).
For claim 27, Tripodi teaches the trained machine learning model is configured to output the measure of overlay based on an input, the input based at least in part on the electromagnetic measurement of the target structure (input 1030 and output 1050, see Fig. 10).
For claim 28, Tripodi teaches the instructions are further configured to cause the one or more processors to identify, based at least in part on the trained machine learning model, a conformation of the target structure based on the measure of asymmetry (characteristic of interest includes one or more other 3D reconstruction parameters or 2D contour, see [0079]).
For claim 31, Tripodi teaches the electromagnetic measurement is performed by an optical metrology apparatus and the target comprises one or more layers of diffraction gratings (see [0062] and [0063]).
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.
Claims 19-22 and 33-35 are rejected under 35 U.S.C. 103 as being unpatentable over Tripodi in view of Bhattacharyya et al. [US 2019/0004437].
For claims 19, 20, 22, 33, 34, and 35, Tripodi teaches providing asymmetry information for determining a characteristic of interest based at least in part on the trained machine learning model (see Figs. 9 and 10), but fails to teach the electromagnetic measurement comprises a first electromagnetic measurement at a first wavelength and a second electromagnetic measurement at a second wavelength and wherein the measure of asymmetry is determined based on a relationship between the first electromagnetic measurement and the second electromagnetic measurement, wherein the measure of asymmetry is a distance-to-origin, and wherein the distance-to-origin corresponds to a distance between a line, wherein the line is through a point corresponding to the first wavelength and a point corresponding to the second wavelength, and an origin point for a plot of asymmetric amplitude, wherein the instructions configured to cause the one or more processors to determine the measure of overlay for the target structure are further configured to cause the one or more processors to: obtain a measure of symmetric overlay for the target structure based, at least in part, on the electromagnetic measurement of the target structure; determine, based at least in part on the trained machine learning model, a measure of asymmetry-adjusted overlay based on the measure of asymmetry; and determine the measure of overlay for the target structure based at least in part on the measure of symmetric overlay and the measure of asymmetry-adjusted overlay.
Bhattacharyya teaches the electromagnetic measurement comprises a first electromagnetic measurement at a first wavelength and a second electromagnetic measurement at a second wavelength (different wavelengths for at least two different recipes of subset, see Figs. 14-25) and wherein the measure of asymmetry is determined based on a relationship between the first electromagnetic measurement and the second electromagnetic measurement threshold (slope between two measurements, see Figs. 14-25), wherein the measure of asymmetry is a distance-to-origin, and wherein the distance-to-origin corresponds to a distance between a line (recipes are used where average distance is above a threshold, see [0167]-[0168]), wherein the line is through a point corresponding to the first wavelength and a point corresponding to the second wavelength, and an origin point for a plot of asymmetric amplitude (slope of lint between compares sets, see Figs. 14-25), wherein the instructions configured to cause the one or more processors to determine the measure of overlay for the target structure are further configured to cause the one or more processors to: obtain a measure of symmetric overlay for the target structure based (OV=0, see Fig. 14), at least in part, on the electromagnetic measurement of the target structure; determine, based at least in part on the trained machine learning model, a measure of asymmetry-adjusted overlay based on the measure of asymmetry (adjusted asymmetry based on distance from the origin but highly parallel, see [0164]-[0168]); and determine the measure of overlay for the target structure based at least in part on the measure of symmetric overlay and the measure of asymmetry-adjusted overlay (overlay determined from combination of recipes, see [0170]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claimed invention to provide the measure of asymmetry between two recipes having different wavelengths for determining overlay as taught by Bhattacharyya in the training and using a machine learning model for determining overlay using asymmetry measurements as taught by Tripodi, because the relationship between two measurement recipes allows for providing a more accurate determination of overlay by reducing sensitivity to the feature asymmetry effect.
For claim 21, Tripodi teaches the measure of asymmetry is one or more selected from: an asymmetric intensity ratio, an asymmetric intensity difference (intensity ratio and difference, see [0063]-[0064]), a set of offset angle values, and/or an offset angle difference value.
Claims 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Tripodi in view of Su et al. [US 2019/0147127].
For claims 29 and 30, Tripodi teaches the instructions are further configured to cause the one or more processors to: obtain a measure of asymmetry corresponding to at least one perturbation of a target structure for generating training data (using measurement data associated with various target asymmetries or variations, see [0099]-[0104] and [0127]-[0134]) determine as training data based, at least in part, on the measure of asymmetry corresponding to the at least one perturbation of the target structure (asymmetry gathered from measurement data related to target variations and stack perturbations, see [0127]-[0131]), and obtain a measure of overlay corresponding to the at least one perturbation of the target structure (the relationship between the simulated input data and simulated output data of the simulations (and any measured data included in the data set) and the corresponding set overlay values, see [0131]).
Tripodi fails to teach determine a feature vector as training data based, at least in part, on the measure of asymmetry corresponding to the at least one perturbation of the target structure, wherein the instructions are further configured to cause the one or more processors to: obtain a measure of overlay corresponding to the at least one perturbation of the target structure to determine a supervisory signal based, at least in part, on the measure of overlay corresponding to the at least one perturbation of the target structure; and label the feature vector for the at least one perturbation of the target structure with the supervisory signal.
Su teaches determining a feature vector as training data, wherein the instructions are further configured to cause the one or more processors to: obtain a measure to determine a supervisory signal based, at least in part, on the measure; and label the feature vector with the supervisory signal (associated output value of the supervisory signal based on input training feature vector, see [0040]-[0041]).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claim invention to generate a relationship between input data and output data using supervised learning as taught by Su in the machine learning model used to find characteristics of interest output based on asymmetry measurements input as taught by Tripodi in order to provide further information for correcting overlay data that may include error associated with unknown or unseen training data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven H Whitesell whose telephone number is (571)270-3942. The examiner can normally be reached Mon - Fri 9:00 AM - 5:30 PM (MST).
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/Steven H Whitesell/Primary Examiner, Art Unit 1759