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 § 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-15 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta US PGPUB No. 2018/0293721 in view of Lee US PGPUB No. 2017/0329889.
In reference to claim 1, Gupta teaches a layout method for a semiconductor chip, the method comprising:
designing a layout (Figure 7, 700, Paragraph [0107] design);
generating an aerial image based on the layout (Figure 7, 708 Paragraph [0111] image);
determining a predicted scanning electron microscope (SEM) image based on the aerial image using a first machine learning model (Figure 7, 714, Paragraph [0111], extracted contours based upon second learning model);
determining a target SEM image based on the layout using a second machine learning model (Figure 7, 706, Paragraph [0107] simulated contours based upon the first learning model);
predicting a defect in the semiconductor chip based on a result of comparing the predicted SEM image with the target SEM image (Figure 7, 716 comparison, 718a, 718b Paragraph [0114] flagging deviations and Paragraph [0123] detecting defects).
Gupta does not explicitly teach correcting the layout based upon the detected defect. Lee teaches correcting a layout due to a predicted defect (Paragraph [0031]. Accordingly, it would have been obvious for one of ordinary skill in the art at the time of invention to incorporate the step of correcting a layout due to a predicted defect into the method of Gupta such that the detected defects in the layout are corrected because it would correct the pattern layout prior to a fabrication process for a semiconductor device in which the pattern layout is transcribed on the wafer, therefore easily preventing the occurrence of the defect (Lee, Paragraph [0031]).
In reference to claim 2, Gupta in view of Lee teaches generating the first machine learning model by performing machine learning using a plurality of sample aerial images and a plurality of first sample SEM images, wherein each of the plurality of first sample SEM images corresponds to a respective sample aerial image from among the plurality of sample aerial images (Gupta Figure 5, 502, Paragraph [0105] training).
In reference to claim 3, Gupta in view of Lee teaches generating the second machine learning model by performing machine learning using a plurality of sample layouts and a plurality of second sample SEM images, wherein each of the plurality of second sample SEM images corresponds to a respective sample layout from among the plurality of sample layouts (Gupta Figure 4, 402, Paragraph [0100] training).
In reference to claim 4, Gupta in view of Lee teaches wherein the plurality of first sample SEM images are the same as the plurality of second sample SEM images (Figures 4 and 5, 408, 508, Image contour ground truth).
In reference to claim 5, Gupta in view of Lee teaches wherein the plurality of first sample SEM images are of a region different from a region of the plurality of second sample SEM images are of different regions of the semiconductor chip (Paragraphs [0100] and [0105] images vs design).
In reference to claim 6, Gupta in view of Lee teaches wherein the plurality of second sample SEM images comprise SEM images having no defects (Gupta, Paragraph [0101] defect free patterns).
In reference to claim 7, Gupta in view of Lee teaches wherein a region of the semiconductor chip comprises a plurality of unit regions, and wherein each of the plurality of sample aerial images, each of the plurality of sample layouts, each of the plurality of first sample SEM images, and each of the plurality of second sample SEM images corresponds to a respective unit region from among the plurality of unit regions (Gupta, paragraph [0092] hot spot/area detection in view of Figure 5, 502, Paragraph [0105] training and Gupta Figure 4, 402, Paragraph [0100] training).
In reference to claim 8, Gupta in view of Lee teaches obtaining each of the plurality of sample aerial images by measuring an optical pattern irradiated to a wafer through a mask generated based on a respective sample layout from among the plurality of sample layouts (Gupta Paragraph [0107] SEM image generated for a wafer).
In reference to claim 9, Gupta in view of Lee teaches obtaining each of the plurality of sample aerial images by performing a simulation based on a respective sample layout from among the plurality of sample layouts (Gupta Paragraph [0104] multiple similar designs).
In reference to claim 10, Gupta in view of Lee teaches wherein the first machine learning model and the second machine learning model comprise a generative adversarial network (GAN) model (Gupta Paragraph [0081].
In reference to claim 11, Gupta in view of Lee teaches wherein the predicting the defect in the semiconductor chip comprises: allowing the predicted SEM image and the target SEM image to overlap each other; and predicting a position of the predicted SEM image which does not overlap the target SEM image as a defect position (Gupta, Paragraph [0113]).
In reference to claim 12, Gupta in view of Lee teaches claim 1 as described above. They do not teach wherein the aerial image comprises grayscale information in each position of the aerial image. However aerial image comprising grayscale information in each position of the aerial image is notoriously well know in the art. OFFICIAL NOTICE IS TAKEN. Accordingly, it would have been obvious for one of ordinary skill in the art at the time of invention to incorporate aerial image comprises grayscale information in each position of the aerial image as the aerial images of Gupta in view of Lee because it would impart more information per pixel which would increase the fidelity of the images and therefore the comparisons when predicting defects.
In reference to claim 13, Gupta in view of Lee teaches claim 1 as described above. They further teach target patterns formed in the same position as patterns of the layout. (Gupta, Figure 7, 700 the patterns are the target patterns. They do not teach wherein the aerial image comprises: diffraction patterns formed around the target patterns. However, diffraction patterns formed around target patterns for performing optical corrections is notoriously well known in the art. OFFICIAL NOTICE IS TAKEN. Accordingly, it would have been obvious for one of ordinary skill in the art at the time of invention to include diffraction patterns formed around the target patterns in the aerial image because the layout could include diffraction patterns around the target layouts to provide improved lithography at smaller sizes.
In reference to claim 14, Gupta in view of Lee teaches wherein the correcting the layout based on the predicted defect comprises adjusting a position, a size or a shape of a pattern of the layout around a position in which the defect is predicted (Lee, paragraph [0029]).
In reference to claim 15, Gupta in view of Lee teaches claim 1 as described above. They do not teach using the corrected layout, repeating the generating the aerial image, the determining the predicted SEM image, the determining the target SEM image, and the predicting the defect in the predicted SEM image. However simulated annealing (repeating steps until an equilibrium is reached) is notoriously well know in the art. OFFICIAL NOTICE IS TAKEN. Accordingly, it would have been obvious for one of ordinary skill in the art at the time of invention to repeat the steps of the aerial image, determining the predicted SEM image, determining the target SEM image, and predicting the defect in the predicted SEM image because it would correct further errors that may come to light in the corrected layout.
In reference to claim 18, drawn to a computing device for all of the functional limitations as found in claim 1, the same rejection applies
Claim(s) 16 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta US PGPUB No. 2018/0293721 in view of Lee US PGPUB No. 2017/0329889 and Makami, USPGPUB No. 2015/0177609.
In reference to claim 16, Gupta teaches a method of manufacturing a semiconductor chip, the method comprising: designing a layout (Figure 7, 700, Paragraph [0107] design); determining, using a first machine learning model, a predicted scanning electron microscope (SEM) image based on an aerial image generated based on the layout (Figure 7, 714, Paragraph [0111], extracted contours based upon second learning model from Figure 7, 708 Paragraph [0111] image); predicting a defect in the semiconductor chip based on the predicted SEM image (Figure 7, 716 comparison, 718a, 718b Paragraph [0114] flagging deviations and Paragraph [0123] detecting defects).
Gupta does not teach generating a corrected layout based on the predicted defect. Lee teaches correcting a layout due to a predicted defect (Paragraph [0031]. Accordingly, it would have been obvious for one of ordinary skill in the art at the time of invention to incorporate the step of correcting a layout due to a predicted defect into the method of Gupta such that the detected defects in the layout are corrected because it would correct the pattern layout prior to a fabrication process for a semiconductor device in which the pattern layout is transcribed on the wafer, therefore easily preventing the occurrence of the defect (Lee, Paragraph [0031]).
Gupta in view of Lee do not teach generating a final layout by performing optical proximity correction (OPC) on the corrected layout; manufacturing a mask using the final layout; and manufacturing the semiconductor chip using the mask. Mikami teaches generating a final layout by performing optical proximity correction (OPC) on a layout (Paragraph [0031]); manufacturing a mask using the final layout (Paragraph [0019] master mask); and manufacturing the semiconductor chip using the mask (Paragraph [0019]). Accordingly, it would have been obvious for one of ordinary skill in the art at the time of invention perform the method of Makami of generating a final layout by performing optical proximity correction (OPC) on the corrected layout of Gupta in view of Lee; manufacturing a mask using the final layout; and manufacturing the semiconductor chip using the mask because it would fabricate the chip having easily preventing the occurrence of the defect (Lee, Paragraph [0031]).
In reference to claim 17, Gupta in view of Lee and Makami teaches wherein the predicting the defect in the semiconductor chip based on the predicted SEM image comprises: determining a target SEM image based on the layout using a second machine learning model (Figure 7, 706, Paragraph [0107] simulated contours based upon the first learning model); predicting a defect in the semiconductor chip based on a result of comparing the predicted SEM image with the target SEM image (Gupta, Figure 7, 716 comparison, 718a, 718b Paragraph [0114] flagging deviations and Paragraph [0123] detecting defects).
Claim(s) 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta US PGPUB No. 2018/0293721 in view of Lee US PGPUB No. 2017/0329889 and Goldstein, US PGPUB No. 2021/0097344.
In reference to claims 19 and 20, Gupta in view of Lee teaches claim 18 as described above. They further teach that the first and second machine learning models can be GANs (Gupta Paragraph [0081]). They do not explicitly state wherein the first or second machine learning model comprises: a generator configured to receive a sample aerial image from a first external entity and to output a fake SEM image; and a discriminator configured to receive a first sample SEM image from a second external entity or to receive the fake SEM image from the generator, and to determine whether the received SEM image is a real image or a fake image, and wherein the first machine learning model is trained such that the probability that the fake SEM image generated by the generator is determined as a real image by the discriminator converges to 50%, and outputs the fake SEM image as the predicted SEM image.
Goldstein teaches GANs create a two-player game between a discriminator network and a generator network. The generator creates synthetic or fake examples and the discriminator decides whether these examples are real or fake examples created by the generator. The game continues as the generator learns to produce more realistic examples and the discriminator improves its ability to separate real from fake examples. Ideally, the system is optimized when the discriminator is approximately 50% (e.g., 45-55% or 40-60%) confident that the generator's examples are fake. Accordingly, it would have been obvious for one of ordinary skill in the art at the time of invention to incorporate the GANs of Goldstein into the first or second machine learning models of Gupta in view of Lee such that the first or second machine learning model comprises: a generator configured to receive a sample aerial image from a first external entity and to output a fake SEM image; and a discriminator configured to receive a first sample SEM image from a second external entity or to receive the fake SEM image from the generator, and to determine whether the received SEM image is a real image or a fake image, and wherein the first machine learning model is trained such that the probability that the fake SEM image generated by the generator is determined as a real image by the discriminator converges to 50%, and outputs the fake SEM image as the predicted SEM image because (GANs) provide a larger effective sample size through synthetic data (Goldstein Paragraph [0121]).
Conclusion
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/B.B/ Examiner, Art Unit 2851
/JACK CHIANG/ Supervisory Patent Examiner, Art Unit 2851