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 § 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, 27, 28 and 32 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Izikson [US 2009/0063378]
For claims 17 and 32, Izikson teaches a non-transitory, machine-readable media having instructions (network embodied on computer hardware and software, see [0051] and [0067]) 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 extracted from a signal generated by a sensor from electromagnetic radiation captured by the sensor during an electromagnetic measurement of a physical target structure (quality metric including asymmetry metric derived from an optical imaging tool, see [0028], [0029], and [0044]-[0046], optical tools also include scatterometry, see [0041] and [0080]); and
determine, by a hardware computer (network embodied on computer hardware and software, see [0051] and [0067]) and based at least in part on input of the measure of asymmetry (asymmetry metrics of the target quality metric of sample wafer used to determine overlay error, see [0030]) to a trained machine learning model trained on asymmetry and overlay data of training target structures (asymmetry metrics of the target quality metric and overlay error value used for training, see [0029]), a measure of overlay (predicted overlay, see [0030]) for the target structure.
For claim 18, Izikson teaches the measure of overlay is an overlay error value or an overlay value (overlay error values, see [0029]).
For claim 27, Izikson 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 (electromagnetic measurement from system 1020 used determining input metrics 106, see [0070]-[0074]).
For claim 28, Izikson 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 (other parameters including critical dimensions, see [0031]).
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 23-26 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Izikson in view of Tripodi et al. [US 2019/0378012].
For claim 23-26 and 36, Izikson teaches incorporating process metrics including values corresponding to critical dimension, resist thickness, sidewall angle and wafer flatness (see [0049]), but fails to teach 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, wherein 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, wherein the stack structure comprises the target structure and the set of perturbation parameters comprises overlay and/or critical distance; and generate the set of perturbations of the target structure based, at least in part, on the set of perturbation parameters, wherein the measure of asymmetry is determined based on a simulation of the electromagnetic measurement of the set of perturbations of the target structure, wherein 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.
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]) 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]) 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]), 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]).
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 generated training data and reference perturbation information as taught by Tripodi in training the neural network of Izikson in order to incorporate other known parameters that influence overlay error and to provide simulated training data for more quickly training the neural network on large amounts of data.
Claims 19-22 and 33-35 are rejected under 35 U.S.C. 103 as being unpatentable over Izikson in view of Bhattacharyya et al. [US 2019/0004437].
For claims 19-22 and 33-35, Izikson teaches providing asymmetry information for determining a characteristic of interest based at least in part on the trained machine learning model (asymmetry metric used for determining overlay, see [0028]-[0031] and Fig. 1) and Izikson teaches measuring overlay using scatterometry techniques incorporating by reference to Mieher (US 2004/0233440, see [0041]), 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 measure of asymmetry is one or more selected from: an asymmetric intensity ratio, an asymmetric intensity difference, a set of offset angle values, and/or an offset angle difference value, 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 line between compared sets, see Figs. 14-25), the measure of asymmetry is one or more selected from: an asymmetric intensity ratio, an asymmetric intensity difference (asymmetry difference in ratio form and slope, see [0098] and [0132]), a set of offset angle values, and/or an offset angle difference value, 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 Izikson, because the relationship between two measurement recipes allows for providing a more accurate determination of overlay by reducing sensitivity to the feature asymmetry effect and provide measurement of asymmetry with a scatterometry overlay tool.
Claims 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Izikson in view of Tripodi and Su et al. [US 2019/0147127].
For claims 29 and 30, Izikson teaches perturbation data can be used for training the neural network to determine overlay (see [0042]-[0049]), but fails to teach 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 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, and obtain a measure of overlay corresponding to the at least one perturbation of the target structure.
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]).
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 generated training data and reference perturbation information as taught by Tripodi in training the neural network of Izikson in order to incorporate other known parameters that influence overlay error and to provide simulated training data for more quickly training the neural network on large amounts of data.
Izikson 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 Izikson in order to provide further information for correcting overlay data that may include error associated with unknown or unseen training data.
Claim 31 is rejected under 35 U.S.C. 103 as being unpatentable over Izikson in view of Mieher et al. [US 2004/0233440].
For claim 31, Izikson teaches measuring overlay using scatterometry techniques incorporating by reference to Mieher (see [0041]) , but fails to explicitly teach the electromagnetic measurement is performed by an optical metrology apparatus and the target comprises one or more layers of diffraction gratings.
Mieher teaches the electromagnetic measurement is performed by an optical metrology apparatus and the target comprises one or more layers of diffraction gratings (target gratings in two layers, see Figs. 2(a)-2(f)).
It would have been obvious to one of ordinary skill in the art prior to the effective filing date of the claim invention to incorporate the diffraction grating target as taught by Mieher as the target of Izikson in order to optimize the target structure for measurement with a scatterometry overlay tool.
Response to Arguments
Applicant’s arguments with respect to the prior art rejections of claims 17 and 32 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant’s arguments, see page 7-14, filed on August 4, 2026, with respect to claims 17-36 have been fully considered and are persuasive. The rejection under 35 U.S.C. 101 of claims 17-36 has been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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
