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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on March is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is considered by examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 19 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 19 recites “wherein the processing device is further to train a machine learning model to determine a number of epitaxial defects of a target substrate, by providing the first image data as training input and the number of epitaxial defects as target output.” It is unclear if “to determine a number of epitaxial defects” refers to “a number” as in a plurality of types of defects or a specific quantified count of how many defects are counted. Thus, Applicant has failed to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
No claims are dependent on claim 19.
Claim Rejections - 35 USC § 103
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 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 1-2, 7, 11, 17 are rejected under 35 U.S.C. 103 as being unpatentable over Trepka (Quantifying epitaxial growth using a purely topographical signal) in view of Official Notice of Facts.
Regarding Claim 1, Trepka teach a method (method to quantify epitaxy thin film growth using quantifier algorithm), comprising:
obtaining,(an AFM image is taken of a epitaxial film substrate; Fig 3, 4 and II. Topographical Crystallinity ¶ 6, 7, III. Investigating holmium oxide thin film growth ¶ 3, IV. Quantifying Epitaxial Growth ¶ 4);
applying a frequency domain filter to the first image data to obtain filtered image data ( a fast Fourier transform (FFT) is applied to the AFM image to transform the data from real space to k-space; Fig 3, 4 and II. Topographical Crystallinity ¶ 6, 7, III. Investigating holmium oxide thin film growth ¶ 1-3, IV. Quantifying Epitaxial Growth ¶ 5);
determining a number of epitaxial defects represented in the first image data by performing feature detection on the filtered image data (the thin films imaged and transformed to k-space with the FFT can be analyzed for the given growth of the thin film by quantifying alignment of the pattern (determining a number of defects based on feature detection and alignment) between the given FFT image compared to a quantifier image (quantified as a q-score, with a high q-score correlated with low errors (defects by growth of given film not aligning to a quantifier (ideal) image); IV. Quantifying Epitaxial Growth ¶ 4-6); and
performing a corrective action in view of the number of epitaxial defects (the quantifier may be rotated to determine the best match relationship (via highest q-score) to determine best match of tessellations between quantifier ideal image and corresponding FFT test image of the epitaxial film and may additionally include an internal calibration should the q-score = 1; IV. Quantifying Epitaxial Growth ¶ 7-10).
Trepka does not explicitly disclose a processing device.
Official notice is taken as to the fact that it is well known in the art to use a computer apparatus with a memory to store instructions, which instructions are executed on a processor, a processing device. One or ordinary skill in the art would recognize the signal processing described by Trepka are obtained and processed on a computing apparatus and the algorithms to transform the digital discrete image data to k-space is performed using a processor with such algorithms that are stored in a computer (1. Introduction, II. Topographical Crystallinity, III. Qualitatively Investigating Thin Film Growth, IV. Quantifying Epitaxial Growth). Therefore, it would have been obvious to one of ordinary skill in the art that Trepka would have performed the image data gathering, transformations and analysis using a computer apparatus with processor executing instructions stored on the memory.
Regarding Claim 2, Trepka et al in view of Official Notice of Facts teach the method of claim 1 (as described above), wherein performing the feature detection comprises performing one or more of a Hough transform or contour counting (the pattern (feature) is determined based on the tessellation of triangles creating the hexagonal FT pattern (contour counting); Fig 3, 4 and II. Topographical Crystallinity ¶ 6, 7, III. Investigating holmium oxide thin film growth ¶ 1-3).
Regarding Claim 7, Trepka et al in view of Official Notice of Facts teach the method of claim 1 (as described above), wherein the corrective action comprises screening the substrate for additional processing (the quantifier may be rotated (screening) to determine the best match relationship (via highest q-score) to determine best match of tessellations between quantifier ideal image and corresponding FFT test image of the epitaxial film; IV. Quantifying Epitaxial Growth ¶ 7-10).
Regarding Claim 11, Trepka in view of Official Notice of Facts teach a non-transitory machine-readable storage medium storing instruction which, when executed, cause a processing device (Official Notice of Facts taken the algorithm described by Trepka is stored on a memory and executed by processor, as discussed above) to perform operations (Trepka, method to quantify epitaxy thin film growth using quantifier algorithm) comprising: steps identical to claim 1 (as described above).
Regarding Claim 17, Trepka in view of Official Notice of Facts teach a system, comprising memory and a processing device coupled to the memory (Official Notice of Facts taken the algorithm to quantify epitaxy thin film growth described by Trepka is stored on a memory and executed by processor, as discussed above), wherein the processing device is configured to: execute steps identical to claim 1 (as described above).
Claims 3, 6, 10, 12, 14, 19 are rejected under 35 U.S.C. 103 as being unpatentable over Trepka (Quantifying epitaxial growth using a purely topographical signal) in view of Official Notice of Facts and Frascaroli et al (Automatic Defect Detection in Epitaxial Layers by Micro Photoluminescence Imaging).
Regarding Claim 3, Trepka et al in view of Official Notice of Facts teach the method of claim 1 (as described above).
Trepka et al in view of Official Notice of Facts does not teach wherein the epitaxial defects comprise one or more of etch pits or cross hatching.
Frascaroli et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the epitaxial defects comprise one or more of etch pits or cross hatching (etch defects, such as etch pits, may be detected as epitaxial defects identified in the image; Fig 2 and IV.A. Results – Detection of Epitaxial Defects ¶ 2).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Trepka et al in view of Official Notice of Facts with Frascaroli et al including wherein the epitaxial defects comprise one or more of etch pits or cross hatching. By identifying etch defects early in semiconductor manufacturing, the quality of the device can be ensured, thereby reducing manufacturing costs and improving device performance, as recognized by Frascaroli et al (I. Introduction ¶ 3-4).
Regarding Claim 6, Trepka et al in view of Official Notice of Facts teach the method of claim 1 (as described above), including wherein generating the first image data comprises: depositing the epitaxial film on a substrate (Trepka et al, the thin film is deposited on the substrate using thermal physical vapor deposition; 1. Introduction ¶ 2).
Trepka et al in view of Official Notice of Facts does not teach performing etching of the epitaxial film; and capturing an image of the substrate using optical microscopy, scanning electron microscopy, or transmission electron microscopy.
Frascaroli et al is analogous art pertinent to the technological problem addressed in the current application and teaches performing etching of the epitaxial film (chemical etching is performed on the thin film silicon surface; Fig 2 and II.B Experimental Selective Defect Etch – SEM and Optical Inspection); and capturing an image of the substrate using optical microscopy, scanning electron microscopy, or transmission electron microscopy (SEM imaging is performed to capture crystal defects on the sample surface; Fig 2-3 and II.B Experimental Selective Defect Etch – SEM and Optical Inspection).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Trepka et al in view of Official Notice of Facts with Frascaroli et al including performing etching of the epitaxial film; and capturing an image of the substrate using optical microscopy, scanning electron microscopy, or transmission electron microscopy. By performing etching on the substrate surface and using SEM to image the substrate, defects on the epitaxial film may be quickly and effectively identified early in semiconductor manufacturing, thereby reducing manufacturing costs, ensuring device quality and improving device performance, as recognized by Frascaroli et al (I. Introduction ¶ 3-4).
Regarding Claim 10, Trepka et al in view of Official Notice of Facts teach the method of claim 1 (as described above).
Trepka et al in view of Official Notice of Facts does not teach further comprising one or more of classifying the epitaxial defects or determining a density of epitaxial defects.
Frascaroli et al is analogous art pertinent to the technological problem addressed in the current application and teaches further comprising one or more of classifying the epitaxial defects or determining a density of epitaxial defects (epitaxial defects detected may be classified between common defect classes, classified by the neural network; Fig 2-3 and IV.A. Results – Detection of Epitaxial Defects ¶ 2-4, IV.C. Automatic Recognition and Classification of Defects ¶ 3-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Trepka et al in view of Official Notice of Facts with Frascaroli et al including further comprising one or more of classifying the epitaxial defects or determining a density of epitaxial defects. By identifying and classifying defect types, the manufacturing process may be quickly addressed to reduce/eliminate the particular defect, thereby reducing manufacturing costs, ensuring device quality and improving device performance, as recognized by Frascaroli et al (I. Introduction ¶ 3-4).
Regarding Claim 12, Trepka et al in view of Official Notice of Facts teach the non-transitory machine-readable storage medium of claim 11 (as described above), with further limitations identical to claim 3 (as described above).
Regarding Claim 14, Trepka et al in view of Official Notice of Facts teach the non-transitory machine-readable storage medium of claim 11 (as described above), with further limitations identical to claim 6 (as described above).
Regarding Claim 19, Trepka et al in view of Official Notice of Facts teach the system of claim 17 (as described above).
Trepka et al in view of Official Notice of Facts does not teach wherein the processing device is further to train a machine learning model to determine a number of epitaxial defects of a target substrate, by providing the first image data as training input and the number of epitaxial defects as target output.
Frascaroli et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the processing device is further to train a machine learning model to determine a number of epitaxial defects of a target substrate, by providing the first image data as training input and the number of epitaxial defects as target output (a CNN is used for detection, classification and quantification of epitaxial defects based on image analysis, with the CNN trained using a pixelwise defect detection methodology and image segmentation; Fig 1-3 and II.D. Experimental Automatic Defect Detection, IV.A. Results – Detection of Epitaxial Defects ¶ 2-5, IV.C. Automatic Recognition and Classification of Defects ¶ 3-4).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Trepka et al in view of Official Notice of Facts with Frascaroli et al including wherein the processing device is further to train a machine learning model to determine a number of epitaxial defects of a target substrate, by providing the first image data as training input and the number of epitaxial defects as target output. By identifying and quantifying defects and associate defect quantities to classification groups, the manufacturing process may be quickly addressed to reduce/eliminate the particular defect, thereby reducing manufacturing costs, ensuring device quality and improving device performance, as recognized by Frascaroli et al (I. Introduction ¶ 3-4).
Claims 4-5, 13, 20 are rejected under 35 U.S.C. 103 as being unpatentable over Trepka (Quantifying epitaxial growth using a purely topographical signal) in view of Official Notice of Facts and Li et al (Growth and Selective Etch of Phosphorus-Doped Silicon/Silicon-Germanium Multilayers Structures for Vertical Transistors Application).
Regarding Claim 4, Trepka et al in view of Official Notice of Facts teach the method of claim 1 (as described above).
Trepka et al in view of Official Notice of Facts does not teach wherein the epitaxial film comprises one or more of: silicon; carbon; boron; arsenic; antimony; tin; phosphorus; silicon-germanium; silicon carbide; gallium nitride; aluminum nitride; gallium arsenide; gallium aluminum nitride; indium; or silicon arsenide.
Li et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the epitaxial film comprises one or more of: silicon; carbon; boron; arsenic; antimony; tin; phosphorus; silicon-germanium; silicon carbide; gallium nitride; aluminum nitride; gallium arsenide; gallium aluminum nitride; indium; or silicon arsenide (Si/SiGe/Si multilayers were grown on Si wafers; Fig 2 and Methods ¶ 1).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Trepka et al in view of Official Notice of Facts with Li et al including wherein the epitaxial film comprises one or more of: silicon; carbon; boron; arsenic; antimony; tin; phosphorus; silicon-germanium; silicon carbide; gallium nitride; aluminum nitride; gallium arsenide; gallium aluminum nitride; indium; or silicon arsenide. By using Si/SiGe/Si multilayers, the gate length may be better controlled and variation in manufacturing is reduced, thereby improving the fabrication process with uniformity and improved quality, as recognized by Li et al (Introduction ¶ 1).
Regarding Claim 5, Trepka et al in view of Official Notice of Facts and Li et al teach the method of claim 4 (as described above), wherein the epitaxial film comprises a silicon/silicon-germanium superlattice (Li et al, the epitaxial film is composed of Si/SiGe/Si multilayers (superlattice); Fig 2 and Methods ¶ 1).
Regarding Claim 13, Trepka et al in view of Official Notice of Facts teach the non-transitory machine-readable storage medium of claim 11 (as described above), with further limitations identical to claim 4 (as described above).
Regarding Claim 20, Trepka et al in view of Official Notice of Facts teach the system of claim 17 (as described above), with further limitations identical to claim 4 (as described above).
Claims 8, 9, 15, 16, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Trepka (Quantifying epitaxial growth using a purely topographical signal) in view of Official Notice of Facts and Fan et al (Key Parameter Identification and Defective Wafer Detection of Semiconductor Manufacturing Processes Using Image Processing Techniques).
Regarding Claim 8, Trepka et al in view of Official Notice of Facts teach the method of claim 1 (as described above).
Trepka et al in view of Official Notice of Facts does not teach wherein the corrective action comprises one or more of: scheduling maintenance of a process chamber associated with the epitaxial film; updating a process recipe; updating one or more manufacturing parameters of the process chamber; or providing an alert to a user.
Fan et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the corrective action comprises one or more of: scheduling maintenance of a process chamber associated with the epitaxial film; updating a process recipe (interpreted as any step of manufacturing); updating one or more manufacturing parameters of the process chamber; or providing an alert to a user (a number of manufacturing parameters may be identified and manufacturing parameters may be amended to improve the process by reducing the number of defective wafers; Fig 2, 10 and V.A. Experimental Study of Width Parameter, V.B. Number of Good/Bad Wafers Ratio).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Trepka et al in view of Official Notice of Facts with Fan et al including wherein the corrective action comprises one or more of: scheduling maintenance of a process chamber associated with the epitaxial film; updating a process recipe; updating one or more manufacturing parameters of the process chamber; or providing an alert to a user. By analyzing a plurality of processes influencing manufacturing defects, the key parameter may be adjusted to improve manufacturing results, leading to optimized quality and efficiency in production thereby reducing costs, as recognized Fan et al (I. Introduction ¶ 1, 4).
Regarding Claim 9, Trepka et al in view of Official Notice of Facts teach the method of claim 1 (as described above).
Trepka et al in view of Official Notice of Facts does not teach wherein the frequency domain filter comprises one of: a two-dimensional high-pass filter; or a low-pass filter.
Fan et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the frequency domain filter comprises one of: a two-dimensional high-pass filter; or a low-pass filter. (the high-energy frequency components of the Fourier spectrum image are set to zero; Fig 2 and III. Defective Wafer Detection Using Fourier Transform ¶ 7).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Trepka et al in view of Official Notice of Facts with Fan et al including wherein the frequency domain filter comprises one of: a two-dimensional high-pass filter; or a low-pass filter. By filtering the high-energy frequency component, background pattern data may be removed allowing for the defective elements to be retained and analyzed, thereby leading to more effective analysis to identify good and defective wafers, as recognized by Fan et al (III. Defective Wafer Detection Using Fourier Transform ¶ 8).
Regarding Claim 15, Trepka et al in view of Official Notice of Facts teach the non-transitory machine-readable storage medium of claim 11 (as described above).
Trepka et al in view of Official Notice of Facts does not teach wherein the corrective action comprises one or more of: scheduling maintenance of a process chamber associated with the epitaxial film; updating a process recipe; updating one or more manufacturing parameters of the process chamber; or providing an alert to a user.
Fan et al is analogous art pertinent to the technological problem addressed in the current application and teaches wherein the corrective action comprises one or more of: screening the substrate for performance of additional process operations; scheduling maintenance of a process chamber associated with the epitaxial film; updating a process recipe (interpreted as any step of manufacturing); updating one or more manufacturing parameters of the process chamber; or providing an alert to a user (a number of manufacturing parameters may be identified and manufacturing parameters may be amended to improve the process by reducing the number of defective wafers; Fig 2, 10 and V.A. Experimental Study of Width Parameter, V.B. Number of Good/Bad Wafers Ratio).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the current application to combine the teachings of Trepka et al in view of Official Notice of Facts with Fan et al including wherein the corrective action comprises one or more of: screening the substrate for performance of additional process operations; scheduling maintenance of a process chamber associated with the epitaxial film; updating a process recipe; updating one or more manufacturing parameters of the process chamber; or providing an alert to a user. By analyzing a plurality of processes influencing manufacturing defects, the key parameter may be adjusted to improve manufacturing results, leading to optimized quality and efficiency in production thereby reducing costs, as recognized Fan et al (I. Introduction ¶ 1, 4).
Regarding Claim 16, Trepka et al in view of Official Notice of Facts teach the non-transitory machine-readable storage medium of claim 11 (as described above), with further limitations identical to claim 9 (as described above).
Regarding Claim 18, Trepka et al in view of Official Notice of Facts teach the system of claim 17 (as described above), with further limitations identical to claim 15 (as described above).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Clark et al (US 2020/0081423) teach a method and system for inspecting manufacturing processes of semiconductor apparatus including corrective processing to correct non-conformity identified in image analysis.
Sawlani et al (US 2022/0270237) teach a system and method for defect detection and classification for semiconductor equipment based on multiple image data inputs to multiple neural networks to identify and quantify the various types of defects detected.
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/KATHLEEN M BROUGHTON/Primary Examiner, Art Unit 2661
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