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 .
Notice to Applicants
This communication is filed in response to the action filed on 05/21/2024.
Claims 1-20 are pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 03/19/2025 has been considered.
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 non-obviousness.
Claims 1-7, and 9-20 are rejected under 35 § U.S.C. 103 as being obvious over US 2016/0125583 A1 to AMANULLAH et al. (hereinafter “AMANULLAH”) in view of US 2020/0133257 A1 to CELLA et al. (hereinafter “CELLA”), in view of US 2022/0044389 A1 to XU et al. (hereinafter “XU”).
As per claim 1, AMANULLAH discloses a system comprising: a non-transitory memory storing instructions (a computing system and image processing method associated with it comprising a memory adapted to store instructions and data; abstract; fig 5D; paragraphs [0076], [0136]); and one or more hardware processors coupled to the non-transitory memory and configured to read the instructions from the non-transitory memory to cause the system to perform operations comprising (the computing system further including computer processors coupled to the memory to execute programs data and instructions related to the method; abstract; fig 5D; paragraphs [0076], [0136]): providing a training dataset comprising historical image data generated by a tape and reel machine, wherein the historical image data includes images of wafers comprising dies having integrated circuits (providing a training set which would be historical based on the machine vision algorithm which is trained by images captured using a tape and reel method to capture semiconductor wafer images of integrated chip dies and to identify misalignments which include mispicks; paragraphs [0017], [0107-0109]). AMANULLAH fails to disclose a convolutional neural network comprising feature layers and fully connected layers to identify die mispicks in the images of the wafers, training, using the training dataset, wherein the feature layers and the fully connected layers comprise neurons associated with corresponding weights and wherein the training comprises: passing each image in the images of the wafers through the feature layers and the fully connected layers to generate a corresponding prediction indicating whether a die in the each image is a mispick die or not a mispick die determining a prediction error for the each image, wherein the prediction error indicates the prediction is a true prediction or a false prediction; and modifying the weights of the neurons in the convolutional neural network until prediction errors are minimized.
CELLA discloses a convolutional neural network comprising feature layers and fully connected layers to identify die mispicks in the images of the wafers (the system comprises a CNN which would be applied to train the machine vision algorithm of AMANULLAH; paragraphs [1026], [2321-2322]), training, using the training dataset, wherein the feature layers and the fully connected layers comprise neurons associated with corresponding weights and wherein the training comprises (a training dataset is provided and would include defects such as mispicks and misalignments among a plurality of other trainable defects to recognize; paragraphs [1026], [2321-2322], [2742]): determining a prediction error for the each image, wherein the prediction error indicates the prediction is a true prediction or a false prediction (determining via the computing system and substantially acting as an error prediction rate the system provides determining achieving or not achieving a desired goal, such as a specified/threshold output production rate, a specified/threshold generation rate, an operational efficiency/ failure rate; paragraphs [1104-1105], [2742], [2775]); and modifying the weights of the neurons in the convolutional neural network until prediction errors are minimized (and adjusting the weighted parameters of the CNN in order to properly train the algorithm to identify mispicks and to reduce the error rate of the misidentified images; fig 178; paragraphs [0724], [1230], [1782], [2295], [4293]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify AMANULLAH to have modifying the weights of the neurons in the convolutional neural network until prediction errors are minimized of CELLA reference. The Suggestion/motivation for doing so would have been to provide the ability to train a CNN to predict if the system has or will be achieving or not achieving a desired goal, such as a specified/threshold output production rate, a specified/threshold generation rate, an operational efficiency/ failure rate, among a plurality of other trainable scenarios as suggested by paragraph [1104]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine CELLA with AMANULLAH to obtain the invention as specified in claim 1.
XU discloses passing each image in the images of the wafers through the feature layers and the fully connected layers to generate a corresponding prediction indicating whether a die in each image is a mispick die or not a mispick die (the computing system is adapted to determine if the analyzed image frame comprises a mispick die or a non-mispick die using the trained model; paragraphs [0021], [0029], [0097], [0107]; CLAIM 10).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify AMANULLAH to have passing each image in the images of the wafers through the feature layers and the fully connected layers to generate a corresponding prediction indicating whether a die in each image is a mispick die or not a mispick die of XU reference. The Suggestion/motivation for doing so would have been to provide the ability to determine if the image frame comprises a mispick as suggested by paragraphs [0021], and [0029] of XU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine XU with AMANULLAH to obtain the invention as specified in claim 1.
As per claim 2, AMANULLAH in view of CELLA in view of XU discloses the system of claim 1. Modified AMANULLAH further discloses wherein the images of the wafers in the historical image data are taken by a camera at the tape and reel machine (the computing system comprising the tape and reel component includes a camera of the tape and reel system in order to capture images of the wafers and store them to the system as historical images; paragraphs [0024-0025], [0028-0030], [0048]).
As per claim 3, AMANULLAH in view of CELLA in view of XU discloses the system of claim 1. Modified AMANULLAH further discloses wherein an image in the images of the wafers is a ground truth image that includes an alignment die placed in a center of image (a predetermined first/start die pixel location can correspond to the center of the first/start die, or a given comer of the first/start die wherein the start die acts as the ground truth reference die having the alignment based on the center points; fig 12; paragraphs [0164-0165], [0184], [0222-0225]).
As per claim 4, AMANULLAH in view of CELLA in view of XU discloses the system of claim 1. Modified AMANULLAH further discloses wherein an image in the images of the wafers includes an alignment die shifted by a shifted distance from a center of the wafer as compared to an alignment die in a ground truth image (pixel position corresponding to the center of the first/start die, or a predetermined comer of the start die, and provides selection of a next/successive die position can include moving or shifting a predetermined image space die separation distance, i.e., a predetermined number of pixels, away from a current or most recent composite image die position; figs 12-13; paragraphs [0222-0228]).
As per claim 5, AMANULLAH in view of CELLA in view of XU discloses the system of claim 1. Modified AMANULLAH further discloses wherein an image in the images of the wafers includes a missing alignment die (the inspection of the images Is provided in order to eliminate misalignment issues form the skeleton wafer and the printout wafer die; paragraph [0038]).
As per claim 6, AMANULLAH in view of CELLA in view of XU discloses the system of claim 1. Modified AMANULLAH further discloses wherein the training dataset further comprises a synthetic dataset having images created synthetically from the historical image data (the databases comprising data for retraining the algorithm comprise data sets related to sorting error such as the causes of die sorting errors can be categorized as (A) reference die detection and retraining errors; (B) general die detection failures; (C) die edge translation errors; (D) other translation errors; and (E) other causes of errors; figs 9-10a; paragraphs [0021-0022] [0205-0209], [0214]).
As per claim 7, AMANULLAH in view of CELLA in view of XU discloses the system of claim 6. Modified AMANULLAH further discloses further comprising: identifying, in a text log file, an indication of an image having a shifted alignment die (pixel position corresponding to the center of the first/start die, or a predetermined comer of the start die, and provides selection of a next/successive die position can include moving or shifting a predetermined image space die separation distance, i.e., a predetermined number of pixels, away from a current or most recent composite image die position; figs 12-13; paragraphs [0222-0228]); identifying, using the indication, the image in an image log file (die 20 are removed from the kth diced wafer 5 and transferred to a tape reel by way of a tape and reel assembly 120, a die ID and a diced wafer location/grid position corresponding to each such die 20 can be stored ( e.g., in a memory or database, such as in the form of a picked die data file acting as the log file); paragraph [0264-0265]); determining a shift distance using the shifted alignment die and an alignment die in a ground truth image (provides selection of a next/successive die position can include moving or shifting a predetermined image space die separation distance, i.e., a predetermined number of pixels, away from a current or most recent composite image die position; figs 12-13; paragraphs [0222-0228]); and generating, using the shifted distance, a plurality of synthetic images using the image in the log file or the ground truth image (and further generating the shifted die based on the determined shift from the original reference file with the die centered at its center point; figs 12-13; paragraphs [0222-0228]).
As per claim 9, AMANULLAH in view of CELLA in view of XU discloses the system of claim 1. Modified AMANULLAH further discloses further comprising: incorporating the trained convolutional neural network into a machine learning system communicatively connected to a die processing system (the CNN of CELLA is used to train the machine vision algorithm of AMANULLAH which is trained to identify misalignments including mispicks in die/wafer image alignment; paragraphs [0023-0026], [0030-0031], [0046], [0222-0228]); receiving, at the machine learning system, an image of a wafer having a plurality of dies from the die processing system (segmental image capture sequences corresponding to one or multiple sets of segmental images (which are input images of a wafer having a plurality of die locations) and includes the appropriate illumination conditions or parameters associated therewith can be determined (e.g., experimentally) for batches of wafers for a given type and size of manufactured device/integrated circuit chip this info can be saved to a file that can be retrieved for use in a skeleton wafer inspection recipe, or input by a technician by way of a menu selection or manual input directed to a graphical user interface; paragraphs [0155]); and determining, using the convolutional neural network, the image to include a die mispick (and determining based on the algorithm trained using the CNN of CELLA the ability to identify misalignments as mispicks based on the trained model; paragraphs [0023-0026], [0030-0031], [0046], [0222-0228]).
As per claim 10, AMANULLAH discloses a method comprising (a computing system and image processing method associated with it comprising a memory adapted to store instructions and data; abstract; fig 5D; paragraphs [0076], [0136]): receiving, at an error detection system communicatively coupled to a die processing service (providing a training set which would be historical based on the machine vision algorithm which is trained by images captured using a tape and reel method to capture semiconductor wafer images of integrated chip dies and to identify misalignments which include mispicks; paragraphs [0017], [0107-0109]), text data in a text log file and an image data in an image log file, wherein the image data comprises a plurality of images of wafers (die 20 are removed from the kth diced wafer 5 and transferred to a tape reel by way of a tape and reel assembly 120, a die ID and a diced wafer location/grid position corresponding to each such die 20 can be stored ( e.g., in a memory or database, such as in the form of a picked die data file acting as the log file); paragraph [0264-0265]); processing, using at least one first rule, the text data (applied to the system as a first rule for image processing and trained into the model in order to enhance throughput, only a limited number of zones (e.g., Z=S die zones) are considered in association with an automated skeleton wafer sampling algorithm; paragraph [0046]). AMANULLAH fails to disclose wherein the processing generates a first alert indicating a die mispick in the image data when the at least one first rule is satisfied; processing, using at least one second rule, the image data, wherein the processing generates a second alert indicating a die mispick when the at least one second rule is satisfied.
CELLA discloses wherein the processing generates a first alert indicating a die mispick in the image data when the at least one first rule is satisfied (the system comprises a CNN which would be applied to train the machine vision algorithm of AMANULLAH and would train it based on a set of one or more rules in order to determine predictions; paragraphs [1026], [1154], [1162], [2321-2322]); processing, using at least one second rule, the image data, wherein the processing generates a second alert indicating a die mispick when the at least one second rule is satisfied (feedback to various intelligent and/or expert systems, control systems including remote and local systems, autonomous systems, which may comprise rule-based systems comprising multiple rules such as a first and second rule for identifying defects, misalignments, and mispicks; paragraphs [1026], [1154], [1162-1164], [1254], [2321-2322]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify AMANULLAH to have first and second rules applied as mispick identification criteria of CELLA reference. The Suggestion/motivation for doing so would have been to provide the ability to train a CNN to predict if the system has or will be achieving or not achieving a desired goal, such as a specified/threshold output production rate, a specified/threshold generation rate, an operational efficiency/ failure rate, among a plurality of other trainable scenarios as suggested by paragraph [1104]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine CELLA with AMANULLAH to obtain the invention as specified in claim 10.
XU discloses and processing, using a machine learning system having a neural network trained on historical image data and synthetic image data, the image data to generate a prediction, wherein the processing generates a third alert when the prediction indicates the die mispick (the computing system is adapted to determine if the analyzed image frame comprises a mispick die or a non-mispick die using the trained model; paragraphs [0021], [0029], [0097], [0107]; CLAIM 10).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify AMANULLAH to have using a machine learning system having a neural network trained on historical image data and synthetic image data, the image data to generate a prediction, wherein the processing generates a third alert when the prediction indicates the die mispick of XU reference. The Suggestion/motivation for doing so would have been to provide the ability to determine if the image frame comprises a mispick as suggested by paragraphs [0021], and [0029] of XU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine XU with AMANULLAH to obtain the invention as specified in claim 10.
As per claim 11, AMANULLAH in view of CELLA in view of XU discloses the method of claim 10. Modified AMANULLAH further discloses wherein the plurality of images of wafers are taken at a tape and reel machine in the die processing service (the computing system comprising the tape and reel component includes a camera of the tape and reel system in order to capture images of the wafers and store them to the system as historical images; paragraphs [0024-0025], [0028-0030], [0048]).
As per claim 12, AMANULLAH in view of CELLA in view of XU discloses the method of claim 10. Modified AMANULLAH further discloses wherein the at least one first rule is satisfied when the text data indicates an error at a tape and reel machine (the tape and reel machines are used to image and track wafers and die position and is adapted to identify die picking errors that cause mis picks; figs 1, 5A, and 9; paragraphs [0016], [0107-0109], [0264]).
As per claim 13, AMANULLAH in view of CELLA in view of XU discloses the method of claim 10. Modified AMANULLAH further discloses wherein the at least one second rule is satisfied when the image data indicates a shift distance in an alignment die image by more than a predefined distance from an alignment die in a ground truth image (pixel position corresponding to the center of the first/start die, or a predetermined comer of the start die, and provides selection of a next/successive die position can include moving or shifting a predetermined image space die separation distance, i.e., a predetermined number of pixels, away from a current or most recent composite image die position; figs 12-13; paragraphs [0222-0228]).
As per claim 14, AMANULLAH in view of CELLA in view of XU discloses the method of claim 10. Modified XU further discloses wherein the prediction comprises a probability that an image in the image data includes the die mispick (processing the probability model, the CIS model can provide a defect identification and/or classification in step 608 including a probability value; fig 6; paragraph [0045]).
As per claim 15, AMANULLAH in view of CELLA in view of XU discloses the method of claim 10. Modified AMANULLAH further discloses wherein the first alert, the second alert, or the third alert is transmitted to a computing device, wherein an application interface executing on the computing device is activated upon receipt of the first alert, the second alert, or the third alert and displays the first alert, the second alert, or the third alert (the computing system includes a graphic interface and display to display information of the interface and would include displaying the notifications/alerts generated by the computing host system related to die misalignments; fig 5D and 10B; paragraphs [0141], [0155], [0183], [0191], [0213]).
As per claim 16, AMANULLAH in view of CELLA in view of XU discloses the method of claim 10. Modified AMANULLAH further discloses wherein the first alert, the second alert, and the third alert are generated in parallel (the computing processes of the system are completed in parallel; fig 5D and 10B; paragraphs [0141], [0155], [0183], [0191], [0213], [0247]).
As per claim 17, AMANULLAH discloses a non-transitory machine-readable medium having stored thereon machine-readable instructions (the computing system further including computer processors coupled to the memory to execute programs data and instructions related to the method; abstract; fig 5D; paragraphs [0076], [0136])executable to cause a machine to perform operations comprising (a computing system and image processing method associated with it comprising a memory adapted to store instructions and data; abstract; fig 5D; paragraphs [0076], [0136]): receiving, at the machine learning system, image data, wherein the image data comprises a plurality of images of wafers generated at a die processing service (providing a training set which would be historical based on the machine vision algorithm which is trained by images captured using a tape and reel method to capture semiconductor wafer images of integrated chip dies and to identify misalignments which include mispicks; paragraphs [0017], [0107-0109]). AMANULLAH fails to disclose receiving, at a machine learning system, weights from a trained neural network model; and processing, using the machine learning system, the image data to generate a prediction, wherein the prediction indicates whether the image data includes an image of a wafer with a die mispick.
CELLA discloses receiving, at a machine learning system, weights from a trained neural network model (the system comprises a CNN which would be applied to train the machine vision algorithm comprising weighted parameters of AMANULLAH; paragraphs [1026], [2321-2322]); incorporating the weights into a neural network model (determining via the computing system and substantially acting as an error prediction rate the system provides determining achieving or not achieving a desired goal, such as a specified/threshold output production rate, a specified/threshold generation rate, an operational efficiency/ failure rate; paragraphs [1104-1105], [2742], [2775]) in the machine learning system (and adjusting the weighted parameters of the CNN in order to properly train the algorithm to identify mispicks and to reduce the error rate of the misidentified images; fig 178; paragraphs [0724], [1230], [1782], [2295], [4293]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify AMANULLAH to have receiving, at a machine learning system, weights from a trained neural network model of CELLA reference. The Suggestion/motivation for doing so would have been to provide the ability to train a CNN to predict if the system has or will be achieving or not achieving a desired goal, such as a specified/threshold output production rate, a specified/threshold generation rate, an operational efficiency/ failure rate, among a plurality of other trainable scenarios as suggested by paragraph [1104]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine CELLA with AMANULLAH to obtain the invention as specified in claim 17.
XU discloses and processing, using the machine learning system, the image data to generate a prediction, wherein the prediction indicates whether the image data includes an image of a wafer with a die mispick (the computing system is adapted to determine if the analyzed image frame comprises a mispick die or a non-mispick die using the trained model; paragraphs [0021], [0029], [0097], [0107]; CLAIM 10).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to modify AMANULLAH to have wherein the prediction indicates whether the image data includes an image of a wafer with a die mispick of XU reference. The Suggestion/motivation for doing so would have been to provide the ability to determine if the image frame comprises a mispick as suggested by paragraphs [0021], and [0029] of XU. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine XU with AMANULLAH to obtain the invention as specified in claim 17.
As per claim 18, AMANULLAH in view of CELLA in view of XU discloses the non-transitory machine-readable medium of claim 17. Modified AMANULLAH further discloses further comprising: collecting predictions from the machine learning system over a predefined time period (the computing system is adapted to identify die picking errors which are mispicks and predict them over subsequent time periods and provide predictions to reduce the number of errors found in practice; figs 1, 9, and 15a-b; paragraphs [0016], [0107-0109], [0264]); and generating a graph indicating state of the die processing system over the predefined time period based on the predictions (the system would be adapted to provide graphical alignment results related to the identified/predicted die picking errors; fig 5D and 10B; paragraphs [0141], [0155], [0183], [0191], [0213]).
As per claim 19, AMANULLAH in view of CELLA in view of XU discloses the non-transitory machine-readable medium of claim 17. Modified AMANULLAH further discloses further comprising: generating, based on the prediction, an alert indicating presence of the die mispick, wherein the alert activates a display of an application interface on a computing device (the computing system includes a graphic interface and display to display information of the interface and would include displaying the notifications/alerts generated by the computing host system related to die misalignments; fig 5D and 10B; paragraphs [0141], [0155], [0183], [0191], [0213]).
As per claim 20, AMANULLAH in view of CELLA in view of XU discloses the non-transitory machine-readable medium of claim 17. Modified CELLA further discloses wherein the neural network model is a convolutional neural network trained on historical image data from a plurality of tape and reel machines (the system comprises a CNN which would be applied to train the machine vision algorithm of AMANULLAH; paragraphs [1026], [2321-2322]).
Claim 8 is rejected under 35 § U.S.C. 103 as being obvious over US 2016/0125583 A1 to AMANULLAH et al. (hereinafter “AMANULLAH”) in view of US 2020/0133257 A1 to CELLA et al. (hereinafter “CELLA”), in view of US 2022/0044389 A1 to XU et al. (hereinafter “XU”) in view of US 2022/0399236 A1 to BRAGANCA, JR. et al. (hereinafter “BRAGANCA”).
As per claim 8, AMANULLAH in view of CELLA in view of XU discloses the system of claim 7. Modified AMANULLAH fails to disclose wherein generating the plurality of synthetic images further comprises: cropping the image in the log file or the ground truth image based on the shifted distance.
BRAGANCA discloses wherein generating the plurality of synthetic images further comprises: cropping the image in the log file or the ground truth image based on the shifted distance (the computing system adapted to generate synthetic shifted die images is adapted to crop the images focusing on a region of interest area based on the applied shift from center of the die of the wafer; abstract; figs 2a-2h; paragraphs [0016], [0031]).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to further modify AMANULLAH to have wherein generating the plurality of synthetic images further comprises: cropping the image in the log file or the ground truth image based on the shifted distance of BRAGANCA reference. The Suggestion/motivation for doing so would have been to provide the ability to individually monitor temperature of the die of the five cropped regions of interest as suggested by BRAGANCA paragraph [0031]. Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine BRAGANCA with modified AMANULLAH to obtain the invention as specified in claim 8.
Conclusion
Examiner's Note: Examiner has cited figures, and paragraphs in the references as applied to the claims above for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested for the applicant, in preparing the responses, to fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner. Examiner has also cited references in PTO892 but not relied on, which are relevant and pertinent to the applicant’s disclosure, and may also be reading (anticipatory/obvious) on the claims and claimed limitations. Applicant is advised to consider the references in preparing the response/amendments in-order to expedite the prosecution.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. These prior arts include the following:
US 2018/0101945 A1
WO 2022/271007 A1
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DEVIN JACOB DHOOGE whose telephone number is (571) 270-0999. The examiner can normally be reached 7:30-5:00.
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/Devin Dhooge/
USPTO Patent Examiner
Art Unit
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
Prosecution Insights