DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This Non-Final office action is in response to application 18/681,613, application filed on 02/06/2024, and Preliminary Amendment subsequently filed on 02/06/2024. In the Preliminary Amendment, Applicant has amended claims 1-3, 5, 8-14 and 16, and has presented claims 17-20 as new. Claims 1-20 are currently pending in this application.
Priority
3. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on 02/06/2024 and 01/21/2026, respectively, is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
5. 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
6. Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Gupta et al. (US PG Pub No. 2018/0293721).
7. With respect to independent claim 1, Gupta teaches:
obtain (i) a first pattern set resulting from a pattern selection process (acquiring images of patterns formed on a specimen, para 15; see feature selection and classification, para 79; see selected features, para 79), (ii) first pattern data associated with the first pattern set (see data for a given pattern, para 11), (iii) characteristic data associated with the first pattern data (whether data for a given pattern can predict/classify pattern as containing a defect, para 11), and (iv) second pattern data associated with a second pattern set (see learning-based data for images of patterns for quantitative pattern characterization, para 31);
train a machine learning model based on the characteristic data associated with the first pattern data, the machine learning model being configured to predict pattern data for a pattern input into the machine learning model (see training deep learning model for image patterns, para 32; see using learning algorithms to determine whether a given pattern is a hot spot, to classify or predict defect in pattern, para 11; see predicting contours for image pattern using deep learning approach, para 76);
generate predicted second pattern data of the second pattern set by input of the second pattern set to the trained machine learning model (see learning models to determine predicted vs actual contours based on trained machine learning model, Abstract); and
evaluate the first pattern set by comparison of the second pattern data and the predicted second pattern data (comparing actual contour to simulated contour, Abstract).
8. With respect to independent claim 17, Gupta teaches:
obtain second pattern data associated with a second pattern set (acquiring images of patterns formed on a specimen, para 15; see feature selection and classification, para 79; see selected features, para 79);
generate predicted second pattern data of the second pattern set by input of the second pattern set to a trained machine learning model (whether data for a given pattern can predict/classify pattern as containing a defect, para 11),
the trained machine learning model trained based on characteristic data associated with first pattern data (see learning-based data for images of patterns for quantitative pattern characterization, para 31),
wherein the first pattern data is associated with a first pattern set resulting from a pattern selection process (see data for a given pattern, para 11; whether data for a given pattern can predict/classify pattern as containing a defect, para 11), and
wherein the machine learning model is configured to predict pattern data for a pattern input into the machine learning model (see training deep learning model for image patterns, para 32; see using learning algorithms to determine whether a given pattern is a hot spot, to classify or predict defect in pattern, para 11; see predicting contours for image pattern using deep learning approach, para 76); and
evaluate the first pattern set by comparison of the second pattern data and the predicted second pattern data (comparing actual contour to simulated contour, Abstract).
9. With respect to independent claim 18, Gupta teaches:
obtain (i) a first pattern set resulting from a pattern selection process (acquiring images of patterns formed on a specimen, para 15; see feature selection and classification, para 79; see selected features, para 79), (ii) first pattern data associated with the first pattern set (see data for a given pattern, para 11), (iii) characteristic data associated with the first pattern data (whether data for a given pattern can predict/classify pattern as containing a defect, para 11), and (iv) second pattern data associated with a second pattern set (see learning-based data for images of patterns for quantitative pattern characterization, para 31);
training a machine learning model based on the characteristic data associated with the first pattern data (see learning-based data for images of patterns for quantitative pattern characterization, para 31; deep-learning model can be trained, para 32),
the machine learning model configured to predict pattern data for a pattern input into the machine learning model (see data for a given pattern, para 11; whether data for a given pattern can predict/classify pattern as containing a defect, para 11);
generating predicted second pattern data of the second pattern set by inputting the second pattern set to the trained machine learning model (see training deep learning model for image patterns, para 32; see using learning algorithms to determine whether a given pattern is a hot spot, to classify or predict defect in pattern, para 11; see predicting contours for image pattern using deep learning approach, para 76); and
evaluating the first pattern set by comparing the second pattern data and the predicted second pattern data (comparing actual contour to simulated contour, Abstract).
10. With respect to claim 2, Gupta teaches:
The medium of claim 1, wherein the instructions configured to cause the computer system to obtain the first pattern data are further configured to cause the computer system to generate first contours or first images by execution of a reference model configured to simulate a patterning process using the first pattern set as input see predicting contours for image pattern using deep learning approach, para 76.
11. With respect to claim 3, Gupta teaches:
The medium of claim 1, wherein the instructions configured to cause the computer system to obtain the first pattern data and the second pattern data are further configured to cause the computer system to obtain contours or images from metrology images of a patterned substrate comprising the first pattern set and the second pattern set (see metrology tools, providing high image data for contours for/from deep learning model inspection tool, para 58).
12. With respect to claim 4, Gupta teaches:
The medium of claim 1, wherein the first pattern set is a subset of the second pattern set (acquiring images of patterns formed on a specimen, para 15; see feature selection and classification, para 79; see selected features, para 79).
13. With respect to claim 5, Gupta teaches:
The medium of claim 2, wherein the second pattern data comprises second contours or second images generated by execution of the reference model using the second pattern set as input (see reference model for determining defects using learning models for classification of defect detection, para 32).
14. With respect to claim 6, Gupta teaches:
The medium of claim 1, wherein the characteristic data comprises data of gauges derived from the first pattern data, the gauges being configured to quantify one or more physical characteristic of patterns (see gauges/measurement for patterns using deep learning, monitoring of pattern fidelity for error//defect detection, para 31).
15. With respect to claim 7, Gupta teaches:
The medium of claim 6, wherein the gauges comprise:
edge placement gauges located at a plurality of locations along a contour of the first pattern data; critical dimension (CD) gauges configured to measure CD values of the first pattern set; gauges configured to measure lines in the first pattern set; gauges configured to measure spaces between features of the first pattern set; gauges configured to measure tip-to-tip structures; and/or gauges configured to measure contour differences between a model predicted contour and a design contour (see gauges/measurement for patterns using deep learning, monitoring of pattern fidelity for error//defect detection with contour measurements, para 31).
16. With respect to claim 8, Gupta teaches:
The medium of claim 1, wherein the instructions configured to cause the computer system to evaluate the first pattern set are further configured to cause the computer system to determine an absolute pattern coverage as a function of an absolute error associated with the trained machine learning model trained using the first pattern set (see flagging errors, learning model trained from relatively large number of image patterns, new examples added to training set, para 85-89).
17. With respect to claim 9, Gupta teaches:
The medium of claim 1, wherein the instructions configured to cause the computer system to evaluate the first pattern set are further configured to cause the computer system to determine a relative pattern coverage as a function of a relative error, the relative error being a comparison between a first error range associated with the trained machine learning model trained using the first pattern set, and a second error range associated with another pattern set (see flagging errors, learning model trained from relatively large number of image patterns, new examples added to training set, para 85-89).
18. With respect to claim 10, Gupta teaches:
The medium of claim 1, wherein the instructions are further configured to cause the computer system to:
based on the evaluation of the first pattern set, determine risk patterns within a design layout, the risk patterns being associated with model prediction errors breaching an error threshold (see defect/hot-spot determination based on threshold, para 11);
supplement the first pattern set with the risk patterns (see flagging potential errors, para 87).
19. With respect to claim 11, Gupta teaches:
The medium of claim 1, wherein the instructions are further configured to cause the computer system to identify, based on the evaluation of the first pattern set, a list of patterns to be inspected by a metrology tool (see metrology tools, providing high image data for contours for/from deep learning model inspection tool, para 58).
20. With respect to claim 12, Gupta teaches:
The medium of claim 1, wherein the instructions are further configured to cause the computer system to:
identify locations of the second pattern set corresponding to breach in a threshold of difference between the second pattern data and the predicted second pattern data (see flagging errors, learning model trained from relatively large number of image patterns, new examples added to training set, para 85-89);
supplement the first pattern set with one or more patterns associated with the identified locations, the supplemented first pattern set having a higher pattern coverage compared to the first pattern set (see specific locations for characterizing pattern fidelity, area of interest for pattern coverage, para 33); and
train another machine leaning model using the supplemented first pattern set (training learning model to extract contours, builds on deep learning application, classification ,and image detection, para 32).
21. With respect to claim 13, Gupta teaches:
The medium of claim 2, wherein the reference model comprises one or more models characterizing the patterning process, and wherein the reference model comprises one or more selected from:
of a source model, an optics model, a resist model, an etch model (see contours for etch process, para 87; see optical data for prediction/classification of detected defect, para 11).
22. With respect to claim 14, Gupta teaches:
The medium of claim 1, wherein the first pattern data, the second pattern data, and the predicted second pattern data comprise at least one selected from:
an aerial image or contours extracted therefrom, a mask image or contours extracted therefrom (see contours extracted, para 22);
a resist image or resist contours extracted therefrom; and an etch image or contours extracted therefrom (images, resist/eth data, see para 19-22 and 30-35).
23. With respect to claim 15, Gupta teaches:
The medium of claim 2, wherein the reference model is a calibrated non-machine learning model (see untrained first learning model, Fig 4, para 100).
24. With respect to claim 16, Gupta teaches:
The medium of claim 1, wherein the instructions are further configured to cause the computer system to:
determine, via simulation of a patterning process using the trained machine learning model, optical proximity corrections for a mask pattern associated with the patterning process (see learning model for OPC SEM contours, para 32);
determine, via simulation of the patterning process using the trained machine learning model, source mask optimization associated with the patterning process (see optimization of image subsystem for manufacture of mask, para 53-55); and/or
improve, via simulation of the patterning process using the trained machine learning model, pattern fidelity matching of patterns printed on the substrate with patterns of a design layout (see improvement of pattern fidelity on substrate, pattern characterization, para 12).
25. With respect to claim 19, Gupta teaches:
The method of claim 18, wherein obtaining the first pattern data comprises generating first contours or first images by executing a reference model configured to simulate a patterning process using the first pattern set as input (see reference model for contours for SEM image for learning model, para 32).
26. With respect to claim 20, Gupta teaches:
The method of claim 18, wherein obtaining the first pattern data and the second pattern data comprise obtaining contours or images from metrology images of a patterned substrate comprising the first pattern set and the second pattern set (see metrology tools, providing high image data for contours for/from deep learning model inspection tool, para 58).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUCHIN PARIHAR whose telephone number is (703)756-1970. The examiner can normally be reached on M-F 8am-5pm.
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/SUCHIN PARIHAR/
Primary Examiner, Art Unit 2851
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