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 § 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 11 (and by dependency claims 12-20) 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. The terms “acquire a first image of a scanning electron microscope from a first wafer at a first time point; acquire a second image of the scanning electron microscope from the first wafer at a second time point a predetermined period of time after the first time point; estimate a calibration factor by using the first image and the second image; and correct a third image of another scanning electron microscope acquired from a second wafer by using the calibration factor” appear to be claiming taking an image of a SEM equipment using a wafer as the camera when it is the opposite, an SEM is taking an image of a wafer. Applicant likely means “acquire a first image taken by a scanning electron microscope of a first wafer at a first time point; acquire a second image taken by the scanning electron microscope of the first wafer at a second time point a predetermined period of time after the first time point; estimate a calibration factor by using the first image and the second image; and correct a third image taken by another scanning electron microscope acquired of a second wafer by using the calibration factor” or some similar clarifying language.
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.
Claim(s) 1, 2, 11, and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oosaki et al. (IDS: US 7476857 B2).
Regarding claim 1, Oosaki et al. disclose processor-implemented method, the method comprising: acquiring a first image of a first wafer at a first time point and acquiring a second image of the first wafer at a second time point after a predetermined amount of time from the first time point (imaging a reference wafer at a series of times by use of a scanning electron microscope, col. 2, lines 45-46, dimension measurements obtained by the same microscope at a series of times, that is, over time, col. 21, lines 4-5) [series of times interpreted as at least a first and second time after the first]; estimating a calibration factor by using the first image and the second image (quickly estimating a factor(s) that has caused a variation or tool-to-tool disparity in dimension measurements between a plurality of measuring scanning electron microscopes or a variation in dimension measurements obtained by a scanning electron microscope over time, col. 2, lines 10-15, Then, the tool-to-tool disparity calculating section 301a calculates a tool-to-tool disparity from the dimension measurements namely, CD values obtained from the microscopes 10 and stored in the measured dimension storage unit 302a, and stores it in the tool-to-tool disparity storage unit 302b. Further, at step S49, the tool-to-tool disparity causing factor analyzing section 301c determines the correlation between the CD values obtained from the microscopes 10 and stored in the tool-to-tool disparity storage unit 302b and the values of indicators, or image features, (indicating states of the microscopes 10) obtained from the microscopes 10 and stored in the image feature value storage unit 302d to estimate a factor(s) (such as Fa to Fg, etc.) that has caused the tool-to-tool disparity, col. 8, lines 5-20); and correcting a third image of a second wafer by using the calibration factor (Attempts have been made to correct or reduce tool-to-tool disparities in dimension measurements between such measuring scanning electron microscopes or correct or reduce variations in dimension measurements obtained by such a scanning electron microscope over time by correcting the measured values themselves, instead of calibrating the microscopes, retrieving the correction equation matching this sample and measurement condition, and correcting length measurements using the selected correction equation, col. 1, lines 35-55, corrects the measured values of pattern dimensions (instead of calibrating the microscopes, col. 1, lines 64-66).
It is noted the correction to the image as opposed to the device is a feature of the described prior art. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the embodiment of the prior art with the invention described as this was known at the time of filing, the combination would have predictable results, and as “Thus, in order to reduce disparities in dimension measurements between a plurality of measuring scanning electron microscopes or reduce variations in dimension measurements obtained by a scanning electron microscope over time, the technique disclosed in the above patent publication corrects the measured values of pattern dimensions (instead of calibrating the microscopes). The reason for this is that scanning electron microscopes are very sophisticated devices and hence it is not easy to pinpoint a factor that has caused a tool-to-tool disparity of a nanometer or less, which is currently being demanded” (col. 1, line 60 – col. 2, line 5) providing a reason in some circumstances why performing calibration in this manner can be optimal.
Regarding claims 2 and 12, Oosaki et al. disclose the method and apparatus of claims 1 and 11. Oosaki et al. further indicate acquiring the first image comprises acquiring the first image by using a first scanning electron microscope, and acquiring the second image comprises acquiring the second image by using a second scanning electron microscope (The present invention relates to a tool-to-tool matching control system for scanning electron microscopes and a method therefor capable of quickly estimating a factor(s) that has caused a variation (or tool-to-tool disparity) in dimension measurements between a plurality of measuring scanning electron microscopes, The present invention provides a system and method for controlling a variation in dimension measurements (or a tool-to-tool disparity) between a plurality of scanning electron microscopes for pattern dimension measurement, the system comprising: measuring means for, at regular intervals, measuring a tool-to-tool disparity between microscopes based on secondary electron image, and, measuring indicators indicating states of the microscopes (that is, image features in the secondary electron image data and/or device state parameters of the microscopes), the secondary electron image data being captured by imaging a reference wafer by use of the microscopes, col. 2, lines 5-35).
Regarding claim 11, Oosaki et al. disclose an electronic apparatus, comprising: processors configured to execute instructions; and a memory storing the instructions, wherein execution of the instructions configures the processors to: acquire a first image of a scanning electron microscope from a first wafer at a first time point and acquire a second image of the scanning electron microscope from the first wafer at a second time point a predetermined period of time after the first time point (imaging a reference wafer at a series of times by use of a scanning electron microscope, col. 2, lines 45-46, dimension measurements obtained by the same microscope at a series of times, that is, over time, col. 21, lines 4-5) [series of times interpreted as at least a first and second time]; estimate a calibration factor by using the first image and the second image (quickly estimating a factor(s) that has caused a variation or tool-to-tool disparity in dimension measurements between a plurality of measuring scanning electron microscopes or a variation in dimension measurements obtained by a scanning electron microscope over time, col. 2, lines 10-15, Then, the tool-to-tool disparity calculating section 301a calculates a tool-to-tool disparity from the dimension measurements namely, CD values obtained from the microscopes 10 and stored in the measured dimension storage unit 302a, and stores it in the tool-to-tool disparity storage unit 302b. Further, at step S49, the tool-to-tool disparity causing factor analyzing section 301c determines the correlation between the CD values obtained from the microscopes 10 and stored in the tool-to-tool disparity storage unit 302b and the values of indicators, or image features, (indicating states of the microscopes 10) obtained from the microscopes 10 and stored in the image feature value storage unit 302d to estimate a factor(s) (such as Fa to Fg, etc.) that has caused the tool-to-tool disparity, col. 8, lines 5-20); and correct a third image of another scanning electron microscope acquired from a second wafer by using the calibration factor (Attempts have been made to correct or reduce tool-to-tool disparities in dimension measurements between such measuring scanning electron microscopes or correct or reduce variations in dimension measurements obtained by such a scanning electron microscope over time by correcting the measured values themselves, instead of calibrating the microscopes, retrieving the correction equation matching this sample and measurement condition, and correcting length measurements using the selected correction equation, col. 1, lines 35-55, corrects the measured values of pattern dimensions (instead of calibrating the microscopes, col. 1, lines 64-66).
It is noted the correction to the image as opposed to the device is a feature of the described prior art. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the embodiment of the prior art with the invention described as this was known at the time of filing, the combination would have predictable results, and as “Thus, in order to reduce disparities in dimension measurements between a plurality of measuring scanning electron microscopes or reduce variations in dimension measurements obtained by a scanning electron microscope over time, the technique disclosed in the above patent publication corrects the measured values of pattern dimensions (instead of calibrating the microscopes). The reason for this is that scanning electron microscopes are very sophisticated devices and hence it is not easy to pinpoint a factor that has caused a tool-to-tool disparity of a nanometer or less, which is currently being demanded” (col. 1, line 60 – col. 2, line 5) providing a reason in some circumstances why performing calibration in this manner can be optimal.
Claim(s) 3 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oosaki et al. (IDS: US 7476857 B2) as applied to claims 1 and 11 above, further in view of Fang et al. (US 20220319805 A1).
Regarding claims 3 and 13, Oosaki et al. disclose the method and apparatus of claims 1 and 11. Oosaki et al. do not disclose the acquiring of the second image comprises: determining the second time point as a specific time point that requires the correction of the first image after the first time point.
Fang et al. teach acquiring of the second image comprises: determining the second time point as a specific time point that requires the correction of the first image after the first time point (During a current operating period of the multi-beam SEM in which the parameter value is the history-based baseline value, the controller may determine the parameter value using performance data of a previous operating period of the multi-beam SEM, such as an “initial period” or “test run” period during when the multi-beam SEM operates immediately after maintenance or calibration, [0058], “In some embodiments, for determining whether the abnormality of the beam occurs, the controller may determine the parameter value as a history-based baseline value of the performance metric for the beams based on historical values of the record. In some embodiments, the controller may determine the baseline value after a predetermined event. For example, the controller may determine the baseline value based on initial records of the performance metric received after the predetermined event. In some embodiments, the predetermined event may include completion of one of maintenance of the multi-beam system or calibration of the multi-beam system. For example, after completion of maintenance or calibration, the multi-beam system may be run again and record values of the performance metric for each beam of the beams. Such values may be used as references for the performance metric and may be referred to as the “initial records.” In some embodiments, the predetermined event may include that each value of the records changes by a shift value, and the shift value does not deviate from an average value of the values of the records exceeding a predetermined range. For example, after the maintenance or calibration, for each beam, the controller may store a value of a record corresponding to the beam, and the value may fall within a predetermined tolerance range. The tolerance range may be configured by a user of the multi-beam system, for example. In some embodiments, the controller may also determine a test value of the beam using at least one value of a record of the beam received at multiple time points of the imaging process”, [0059]).
Oosaki et al. and Fang et al. are in the same art of SEM (Oosaki et al., abstract; Fang et al., [0058]). The combination of Fang et al. with Oosaki et al. will enable determining the second time point as a specific time point that requires the correction of the first image after the first time point. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the time point of Fang et al. with the invention of Oosaki et al. as this was known at the time of filing, the combination would have predictable results, and as Fang et al. indicate “One component of improving yield is monitoring the chip making process to ensure that it is producing a sufficient number of functional integrated circuits. One way to monitor the process is to inspect the chip circuit structures at various stages of their formation. Inspection can be carried out using a scanning electron microscope (SEM). A SEM can be used to image these extremely small structures, in effect, taking a “picture” of the structures of the wafer. The image can be used to determine if the structure was formed properly and also if it was formed in the proper location. If the structure is defective, then the process can be adjusted so the defect is less likely to recur” ([0020]) which will provide a commercial benefit to the combination of references.
Claim(s) 5, 6, 9-10, 15, 16, 18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Oosaki et al. (IDS: US 7476857 B2) as applied to claims 1 and 11 above, further in view of Khedekar et al. (US 20230273529 A1).
Regarding claims 5 and 15, Oosaki et al. disclose the method and apparatus of claims 1 and 11. Oosaki et al. do not disclose the acquiring of the first image comprises: specifying the first time point as a time point before performing a preventive maintenance of a scanning electron microscope that acquires one or more of the first image, the second image, and the third image, and wherein the acquiring of the second image comprises: specifying the second time point as a time point after performing the preventive maintenance.
Khedekar et al. teach specifying the first time point as a time point before performing a preventive maintenance of a scanning electron microscope that acquires one or more of the first image, the second image, and the third image, and wherein the acquiring of the second image comprises: specifying the second time point as a time point after performing the preventive maintenance (The updated/tuned/re-trained machine learning model may then be used 820 for continued high volume production 802. In initial training or calibration 810, performance-critical parameters of the machine learning model are calibrated before high-volume production by a customer, for example. Updating/tuning/re-training 818 may comprise a drift calibration, where the machine learning model parameters are re-calibrated after a period of high volume production. This may occur during a scheduled maintenance, for example, [0144]) [the time points before and after are the tuning before and after maintenance].
Oosaki et al. and Khedekar et al. are in the same art of SEM (Oosaki et al., abstract; Khedekar et al., [0121]). The combination of Khedekar et al. with Oosaki et al. will enable specifying the first time point as a time point before performing a preventive maintenance. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the time point of Khedekar et al. with the invention of Oosaki et al. as this was known at the time of filing, the combination would have predictable results, and as Khedekar et al. indicate this will compensate drift ([0144]) providing an accuracy benefit to combining inventions.
Regarding claims 6 and 16, Oosaki et al. and Khedekar et al. disclose the method and apparatus of claims 5 and 15. Khedekar et al. further teach the correcting of the third image comprises: correcting the third image acquired from the second wafer through the calibration factor by using a scanning electron microscope after performing the preventive maintenance (In an embodiment, the machine learning model is further configured to be updated over time by re-training the machine learning model with new actual process data from the patterning process, [0015], In an embodiment, the control input comprises a diffraction pattern image, a motion setpoint, or a load sequence for a wafer and/or a reticle, [0023] In an embodiment, training the machine learning model with simulated and/or actual process training data from the patterning process comprises an initial calibration, wherein the machine learning model is configured to be updated over time by re-training the machine learning model with new actual process data from the patterning process, and wherein the re-training comprises configuring the machine learning model to be fine-tuned with one or more drift calibrations configured to account for drift that occurs in the patterning process over time, [0035], Updating/tuning/re-training 818 may comprise a drift calibration, where the machine learning model parameters are re-calibrated after a period of high volume production. This may occur during a scheduled maintenance, for example, [0144]).
Regarding claims 9 and 18, Oosaki et al. disclose the method and apparatus of claims 1 and 11. Oosaki et al. partly indicate the estimating of the calibration factor comprises: obtaining a specific calibration factor that matches a first result of a first system for the first image and a second result of a second system for the second image through neural network (A system for controlling a tool-to-tool matching between a plurality of scanning electron microscopes, abstract, retrieving the correction equation matching this sample and measurement condition, col. 1, lines 54-55, The present invention relates to a tool-to-tool matching control system for scanning electron microscopes and a method therefor capable of quickly estimating a factor(s) that has caused a variation (or tool-to-tool disparity) in dimension measurements between a plurality of measuring scanning electron microscopes or a variation in dimension measurements obtained by a scanning electron microscope over time, col. 2, lines 5-15) but do not make the neural network explicit.
Khedekar et al. teach obtaining a specific calibration factor that matches a first result of a first system for the first image and a second result of a second system for the second image through neural network (In this example, training may be divided into an initial training (e.g., 702 shown in FIG. 7) and a drift calibration (e.g., updating/fine-tuning/re-training 710 shown in FIG. 7). This is illustrated in FIG. 8. FIG. 8 illustrates a patterning process set up 800, high volume production 802, maintenance 804, and continued high volume production 802 over time 806. Initial control inputs and outputs 808 (e.g., iterative learning control data in this example) are provided to the machine learning model to train 810 the machine learning model. The machine learning model is then run 812 during high volume production 802 with initial machine learning model parameters. Actual data from high volume production 802 is provided 814 to the machine learning model along with initial inputs and outputs 808 and updated inputs and outputs 816 to update/tune/re-train 818 the machine learning model. The updated/tuned/re-trained machine learning model may then be used 820 for continued high volume production 802. In initial training or calibration 810, performance-critical parameters of the machine learning model are calibrated before high-volume production by a customer, for example. Updating/tuning/re-training 818 may comprise a drift calibration, where the machine learning model parameters are re-calibrated after a period of high volume production. This may occur during a scheduled maintenance, for example, [0144], As described above, in some embodiments, the machine learning model may be and/or include artificial neural networks (ANN) with many hidden layers, [0158]).
Oosaki et al. and Khedekar et al. are in the same art of SEM (Oosaki et al., abstract; Khedekar et al., [0121]). The combination of Khedekar et al. with Oosaki et al. will enable using a neural network. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the neural network of Khedekar et al. with the invention of Oosaki et al. as this was known at the time of filing, the combination would have predictable results, and as Khedekar et al. indicate this will compensate drift ([0144]) providing an accuracy benefit to combining inventions.
Regarding claims 10 and 20, Oosaki et al. disclose the method and apparatus of claims 1 and 11. Oosaki et al. do not disclose the correcting of the third image comprises: inputting the third image along with the calibration factor to a training model of a neural network.
Khedekar et al. teach correcting of the third image comprises: inputting the third image along with the calibration factor to a training model of a neural network (In an embodiment, the machine learning model is further configured to be updated over time by re-training the machine learning model with new actual process data from the patterning process, [0015], In an embodiment, the control input comprises a diffraction pattern image, a motion setpoint, or a load sequence for a wafer and/or a reticle, [0023] In an embodiment, training the machine learning model with simulated and/or actual process training data from the patterning process comprises an initial calibration, wherein the machine learning model is configured to be updated over time by re-training the machine learning model with new actual process data from the patterning process, and wherein the re-training comprises configuring the machine learning model to be fine-tuned with one or more drift calibrations configured to account for drift that occurs in the patterning process over time, [0035]).
Oosaki et al. and Khedekar et al. are in the same art of SEM (Oosaki et al., abstract; Khedekar et al., [0121]). The combination of Khedekar et al. with Oosaki et al. will enable using a neural network. It would have been obvious at the time of filing to one of ordinary skill in the art to combine the neural network of Khedekar et al. with the invention of Oosaki et al. as this was known at the time of filing, the combination would have predictable results, and as Khedekar et al. indicate this will compensate drift ([0144]) providing an accuracy benefit to combining inventions.
Allowable Subject Matter
Claims 4, 7, 8, 14, 17, and 19 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The following art is cited as relevant but not sufficient to disclose, teach, or fairly suggest the subject matter of the objected claims:
US 11894126 B1 According to some embodiments, the image and sensor data may be stored, then utilized to modify (e.g., correct) an image at a later time. For example, the image data may be captured and stored over a period of time, such as 200 frames over 20 milliseconds, and the sensor data may be collected and stored over the same time period in which the images were captured. Accordingly, in some embodiments, the timing module 128 may analyze an image by comparing determined difference pixel data within the images. For example, determining a difference value between the colors in each in pixel of the images to determine the difference between the images. In some embodiments, the timing module 128 may determine the time range or series of images that have the smallest difference in pixels and use corresponding sensor data, such as the acceleration data, to determine if the object in the image was at the point in time in which the object was most still. Based on this, module 128 can then use the average color of each pixel from the series of images with the smallest difference to modify an existing image stored in the image database 134 and/or create a new, corrected image that may be used later (which can also be stored in database 134).
US 20250061557 A1 For example, signal trace analysis can be used for fault analysis of a sensor exhibiting anomalous behavior, such as a sensor that is mis-calibrated, and can help to identify the root cause of the fault and enable a corrective action to be taken. With respect to tool fingerprinting, signal trace analysis may be used to compare signal traces, for example, before and after preventative maintenance to ensure the tool is behaving correctly and can help to identify any deviations in the behavior of the tool and enable proactive maintenance to be scheduled before the tool malfunctions. For chamber and tool matching, signal trace analysis may involve comparing signal traces for different chambers and/or tools running the same process to ensure that all chambers and/or tools behave the same way. This analysis can help to ensure consistent product quality and enable identification of any differences in the behavior of the chambers and/or tools and allow those differences to be addressed.
US 20250013341 A1 In some embodiments, the plurality of metrology metrics comprise a plurality of critical dimensions (CDs) of the one or more objects of interest. Data shifts can occur due to, for example, changes in equipment or sensor characteristics after maintenance or calibration. In some embodiments, the anomaly information comprises object anomalies that indicate a presence and/or transformed state of one or more objects in one or more metrology images.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHELLE ENTEZARI whose telephone number is (571)270-5084. The examiner can normally be reached 10-7 M-F.
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/MICHELLE M ENTEZARI HAUSMANN/Primary Examiner, Art Unit 2671
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