DETAILED ACTION
Claim Rejections - 35 USC § 112
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 3 and 10 are 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 3 recites “indicating that the processed acquired image as a conditioned image if image quality characteristics of the processed acquired image the predetermined imaging criteria”. The sentence does not follow standard English grammar rules, and the meaning is not clear. For purpose of applying art, the sentence is interpreted as “indicating that the processed acquired image as a conditioned image if image quality characteristics of the processed acquired image COMPLY TO the predetermined imaging criteria”
Claim 10 recites the limitation "the first neural network and the second neural network" . There is insufficient antecedent basis for this limitation in the claim. For purpose of applying art, the claim is interpreted recite the limitations in claim 4, instead of claim 1 as stipulated.
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.
(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.
Claim(s) 1-3, 7-9, 15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by PU ( US 20200074610, cited from IDS).
Regarding claim 1, PU teaches a system for processing images for metrology using a charged particle beam tool comprising:
a memory storing a set of instructions( 440 in Fig. 4); and
at least one processor ( 430 in Fig. 4) configured to execute the set of instructions to cause the system to perform:
obtaining, from the charged particle beam tool, an image of a portion of a sample( 510 in Fig. 5);
processing the image using a first image processing module to generate a processed image( 520 in Fig. 5);
determining image quality characteristics of the processed image( 520, 530 in Fig. 5);
determining whether the image quality characteristics of the processed image satisfy predetermined imaging criteria( [0063], critical dimension of a pattern determined (e.g., using image analysis module 420 of FIG. 4, processor 430 of FIG. 4, etc.) may fall outside the acceptable range for the process, the acceptable range identified herein as one the quality metrics); and
in response to the image quality characteristics of the processed image not satisfying the predetermined imaging criteria:
updating a tuning condition of the charged-particle beam tool( 550 in Fig. 5);
acquiring an image of the portion of the sample using the charged-particle beam tool that has the updated tuning condition( 510 in Fig. 5; [0063], If the critical dimension of a pattern in its 310 is determined to be out-of-range, image 310 may be re-analyzed in step 520); and
processing the acquired image using the first image processing module to enable the processed acquired image to satisfy the predetermined imaging criteria( [0063], suggest re-acquisition of a representative image, or re-work of the sample, Metrology guidance system 320 may also suggest flagging electron beam tool 104, image acquirer 260, or EBI system 100 for further inspection and performance verification).
Regarding claim 2, PU teaches the system of claim 1, wherein the set of instructions that are executable by the at least one processor to cause the system to further perform:
iteratively updating a tuning condition of the charged-particle beam tool, acquiring an image of the portion of the sample using the charged-particle beam tool that has the updated tuning condition ( [0063], metrology guidance system 320 may suggest re-acquisition of a representative image, or re-work of the sample, Metrology guidance system 320 may also suggest flagging electron beam tool 104, image acquirer 260, or EBI system 100 for further inspection and performance verification), and
processing the acquired image using the first image processing module, until the image quality characteristics of the processed image satisfy the predetermined imaging criteria([0063], If the critical dimension of a pattern in its 310 is determined to be out-of-range, image 310 may be re-analyzed in step 520.).
Regarding claim 3, PU teaches the system of claim 2, wherein the set of instructions that are executable by the at least one processor to cause the system to further perform:
indicating that the processed acquired image as a conditioned image if image quality characteristics of the processed acquired image the predetermined imaging criteria ( [0056], to receive image 310, perform analysis of image 310, and infer a set of image parameters based on the analysis of image 310. The set of image parameters may comprise noise levels, pattern pitch, pattern yield, line roughness, etc.).
Regarding claim 7, PU teaches the system of claim 5, wherein the set of instructions that are executable by the at least one processor to cause the system to further perform: performing metrology on the metrology-ready image ( [0078]-[0079], metrology guidance system 320 may perform measurements of critical dimensions on simulated images 655).
Regarding claim 8, PU teaches the system of claim 1, wherein the image quality characteristics of the processed image comprises at least one of a noise level, an image resolution value, or ellipse fitting confidence value([0006], quality metrics may include local noise level, global noise level, edge profile statistics, or pattern structure).
Regarding claim 9, PU teaches the system of claim 1, wherein the set of instructions that are executable by the at least one processor that cause the system to determine whether the image quality characteristics of the processed image satisfy the predetermined imaging criteria, cause the system to further perform:
comparing the noise level of the processed image to a reference noise level associated with a high-resolution image, comparing the resolution of the processed image to a reference resolution associated with a high-resolution image([0035], , high resolution e-beam image refers to, but is not limited thereto, an image having a resolution high enough to resolve two distinct features in the image having a spacing less than 20 nm. Image 310 may be acquired using image acquirer 260 of image processing system 250 or any such system capable of acquiring high resolution images. Image 310 may be acquired by any e-beam inspection system that may generate an inspection image of a wafer), or comparing the ellipse fitting confidence of the processed image to a reference ellipse fitting confidence associated with a high-resolution image.
Regarding claim 15, PU teaches the system of claim 1, wherein updating the tuning the condition of the charged-particle beam tool further comprises at least one of: adjusting a beam current value of the charged particle beam tool, adjusting a landing current value of the charged particle beam tool, or adjusting a number of frames used to acquire the image ( [0088], The guidance parameter recommendations may include, but are not limited to, adjust field of view, increase number of averaging pixels, increase number of images needed to achieve target precision, threshold for critical dimension uniformity, etc.
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.
Claim(s)4-6, 10-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over PU in view of Ye ( US 20200118306, cited from IDS ) .
Regarding claim 4, PU teaches the system of claim 1.
PU does not expressly teach wherein the first image processing module comprises a first neural network and a second neural network.
However, Ye teaches the first image processing module comprises a first neural network(GAB in Fig. 3) and a second neural network( GBA in Fig. 3)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of PU with that of Ye, by treating the conditioned image and input image in PU as the normal dose and low dose images in Ye, and run through the training processes as taught in Ye, with motivation “to remove noise “ and “to construct … a high-quality image” ( Ye, [0009]).
Regarding claim 5, PU teaches the system of claim 3, wherein the set of instructions that are executable by the at least one processor to cause the system to further perform.
PU does not expressly teach providing the conditioned image to a second image processing module to generate a metrology-ready image.
However Ye teaches teach providing the conditioned image to a second image processing module ( GAB, GBA in Fig. 3) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teaching of PU with that of Ye, by treating the conditioned image and input image in PU as the normal dose and low dose images in Ye, and run through the training processes as taught in Ye and then input the generated image to metrology analysis in PU, with motivation “to remove noise “ and “to construct … a high-quality image” ( Ye, [0009]).
Regarding claim 6, PU in view of Ye teaches the system of claim 4, wherein the second image processing module comprises a third neural network( the generator (Ye, [0063], DA) and a fourth neural network(Ye,[0063], DB) .
Regarding claim 10, PU in view of Ye teaches the system of claim 4(1), wherein the set of instructions that are executable by the at least one processor that cause the system to process the image using the first image processing module further cause the system to perform:
comparing the image to information generated by the first neural network and the second neural network ([0078]-[0079], Equation 0) .
Regarding claim 11, PU in view of Ye teaches the system of claim 10, wherein the first neural network and the second neural network are configured to receive a plurality of noise signals(Ye, [0055], The generators GAB and GBA may be the neural network which reconstructs the low-dose CT image as the routine-dose CT image and the neural network which generate noise in the routine-dose CT image like the low-dose CT image, respectively).
Regarding claim 12, PU in view of Ye teaches the system of claim 11, wherein the first neural network is configured to receive a first noise signal as a first input and to determine a first output provided to a loss calculation function for assisting with a back propagation algorithm implemented by the first neural network ( Ye, [70]-[80], equation [7], [8]; [0073], in Equation 7 above, GAB is trained to reduce a noise).
Regarding claim 13, PU in view of Ye teaches the system of claim 12, wherein the second neural network is configured to receive a second noise signal as a second input and to determine a second output provided to the loss calculation function for assisting with a back propagation algorithm implemented by the second neural network(Ye, [0075], Similar adversarial loss may be added to the generator GBA, which generates noisy images).
Regarding claim 14, PU in view of Ye teaches the system of claim 13, wherein the image is provided to the first output of the first neural network and the second output of the second neural network to interact with the loss calculation function to generate the processed image(Ye, [0078]-[0079], equation [9]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANGENG SUN whose telephone number is (571)272-3712. The examiner can normally be reached 8am to 5pm, EST, M-F.
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671
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