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 .
1. Claims 13-31 are presented for examination and claims 1-12 are cancelled.
Response to Amendment/Response to Arguments
2. 2.1 The rejection under 112 has been withdrawn since applicant’s amendments and remarks are persuasive and overcome the rejection.
2.2 Applicant’s arguments regarding the amended limitation, an estimated pressure value, that is to be indicated by the vacuum gauge, within the exhaust pipe located between the first and second pumps of an the exhaust system of the first semiconductor manufacturing equipment based on input process data, see page 8-9, filed 6/08/2026, with respect to the rejection(s) of claim(s) 13, 30 and 31-32 under 103 have been fully considered and are persuasive.
Applicant argues that the cited prior art fails to disclose the amended limitations, fully considered and they are persuasive. However, upon further consideration, a new ground(s) of rejection is made in view of Ishii et al. (US 20030158705 A1). Ishii discloses an estimated pressure value ([0019], the adjustment status of the pressure adjustment valve 15), that is to be indicated by the vacuum gauge (a vibration gauge 31), within the exhaust pipe located between the first and second pumps ([0019], mechanical booster pump 18 and dry pump 19) of the exhaust system ([0019], an exhaust pressure gauge 34) of the first semiconductor manufacturing equipment based on input process data ([0019], the real-time failure diagnosis module 533 then determines in real time whether the vacuum pump system (18, 19) is in a normal state or at a state just before failure based on the group of first failure diagnosis data), a mechanical booster pump 18 and a dry pump 19 are connected in series to each other so as to evacuate the inside of the production chamber 521 and the pressure control valve 15, a vacuum pump life expectancy prediction or a prediction of when an irregular shutoff caused by motor current or the temperature of a cooler within the vacuum pump may occur according to experience-based equipment management. As a result, the previous rejection has been withdrawn and a new rejection has been made in its place, Clark et al. (US 20200083080 A1) in view of Sakamoto et al. (US 20080208385 A1) further in view of Ishii et al. (US 20030158705 A1). See the rejection below.
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.
3. 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.
3.1 Claim(s) 13-14 and 16-32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark et al. (US 20200083080 A1) in view of Sakamoto et al. (US 20080208385 A1) further in view Ishii et al. (US 20030158705 A1).
Regarding claims 13 and 30, Clark discloses a non-transitory recording medium storing a program causing a computer and an information processing device ([0127], a computer system that a processor executing one or more sequences of one or more instructions contained in a program in memory), to execute processing of:
inputting the process data ([0128], [0321], input data for control of the processing chambers and tools) to a learning model to output an estimated pressure value ([0076], [0128], deep learning algorithms, to understand the relationships between equipment and process control parameters, and the process result on the substrate or wafer); and
the learning model being trained so as to output an estimated pressure value within the semiconductor manufacturing equipment if process data is input ([0321], [0327], [0344], [0380]-[0381], learning system 1960 can be trained through one or more training cycles, each training cycle to determine corrective processing and predicted pressure).
Clark fails to specifically disclose acquiring process data including at least two of chamber pressure, a valve opening degree of an automatic pressure control device and a kind and a flow rate of gas supplied to a semiconductor manufacturing equipment; and determining whether or not a state is an abnormal state based on the estimated pressure value.
However, Sakamoto discloses acquiring process data including at least two of chamber pressure ([0049], pressure control valve 25, pressure control device), a valve opening degree of an automatic pressure control device ([0049], [0067], [0070], [0140], The pressure control valve 25 is provided with a driver 26 for controlling the opening of a valve element 20 and an angle detector 27 for detecting an angle of the valve element 20) and a kind and a flow rate of gas supplied ([0048], Fig. 9, a deposition gas supply source and a gas flow rate regulator, are connected to the proximal end of the injector 16 through a gas supply path), to a semiconductor manufacturing equipment (Fig. 1, [0018], [0049],[0050], the semiconductor manufacturing apparatus); and determining whether or not a state is an abnormal state based on the estimated pressure value ([0101] The abnormality detection program (abnormality detection means) 41A detects an abnormality of apparatus based on values of monitoring parameters (for example, internal temperature and valve angle) selected from the apparatus data (apparatus status parameters) loaded into the apparatus data storage unit 32. The abnormality detection program 41A may also be configured so as to determine an abnormality of apparatus based on whether or not at least one of a plurality of detection values of monitoring parameters exceeds a threshold value).
Sakamoto and Clark are analogous art since both are related to semiconductor manufacturing control. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify monitoring parameters the semiconductor manufacturing apparatus status, taught by Sakamoto, incorporated with processing and measuring workpieces in a semiconductor processing, taught by Clark, in order to makes it easy to reliably detect an abnormality, identify the cause of the abnormality, or predict the abnormality.
Sakamoto and Clark fail to disclose that is to be indicated by a vacuum gauge, within an exhaust pipe located between first and second pumps of an exhaust system of with in the semiconductor manufacturing equipment based on the input process data.
However, Ishii discloses an estimated pressure value ([0016], [0019], the adjustment status of the pressure adjustment valve 15), that is to be indicated by the vacuum gauge (a vibration gauge 31), within the exhaust pipe located between the first and second pumps ([0019], mechanical booster pump 18 and dry pump 19) of the exhaust system ([0019], an exhaust pressure gauge 34) of the first semiconductor manufacturing equipment based on input process data ([0019], the real-time failure diagnosis module 533 then determines in real time whether the vacuum pump system (18, 19) is in a normal state or at a state just before failure based on the group of first failure diagnosis data, and a mechanical booster pump 18 and a dry pump 19 are connected in series to each other so as to evacuate the inside of the production chamber 521 and the pressure control valve 15, a vacuum pump life expectancy prediction or a prediction of when an irregular shutoff caused by motor current or the temperature of a cooler within the vacuum pump may occur according to experience-based equipment management).
Ishii, Sakamoto and Clark are analogous art, they relate to semiconductor manufacturing control. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify avoiding irregular shutoff of production equipment, taught by Ishii, incorporated with teaching of Sakamoto and Clark, as state above, for avoiding irregular shutoff of production equipment equipped with a vacuum pump and a system for avoiding irregular shutoff.
Regarding claim 14, Clark discloses causing the computer to execute processing of (Fig. 1, controller 3 executes the process data):
acquiring a measured pressure value measured by a vacuum gauge (312a, 312b sensor and prob) provided in the semiconductor manufacturing equipment (Abstract, [0329], process conducted by tool system 1910, sensors and probes comprising sensor component 1925 of an inspection system can collect data (e.g., data assets) associated with measured pressure, temperature, humidity, mass density, deposition rate, layer thickness, surface roughness, crystalline orientation, doping concentration, etc.); and
determining whether or not a state is an abnormal state based on the estimated pressure value and the measured pressure value ([0343],[0345], [0380], defects in a workpiece observed drift in one or more measured attributes or parameters of a workpiece, predictive functions relating tool parameters, a process step, Q.sub.4 may be the mean difference between a predicted pressure and measured pressure, etc.).
Regarding claims 16, 18-19, 26, 28 and 29, Sakamoto discloses the process data includes an operating state of a vacuum pump [0049], A vacuum pump (VP) 24 is connected to the other end of the exhaust pipe 23).
Regarding claims 17 and 27, Clark discloses causing the computer to execute processing of acquiring the process data during a manufacturing process of supplying gas to the semiconductor manufacturing equipment ([0077], [0088], use the data collected for providing virtual metrology (VM), run-to-run (R2R) control to monitor and control process variations, statistical process control (SPC) to alert operators that equipment and/or process is operating outside control limits, advanced process control (APC), fault detection and classification (FDC), fault prediction, equipment health monitoring (EHM), predictive maintenance (PM), predictive scheduling, yield prediction, gas supply and distribution systems, pumping systems, electrical systems and controllers, etc.).
Regarding claims 20, Clark discloses causing the computer to execute processing of determining that a state is an abnormal state in a case where a difference between the measured pressure value and the estimated pressure value is equal to or more than a predetermined value [0381], [0386]-[0387], Fig. Fig. 20, 27A and 27B, a failure in comparator 2720 when the average difference between predicted pressure values and collected pressure data (e.g., as reported by a pressure sensor residing in sensor component) fails to remain within user-specified bounds).
Regarding claims 21 and 23, the combination of Clark and Sakamoto disclose:
Clark discloses causing the computer to execute processing (Fig. 1, the controller 3 execute the data prosses) the of:
continuously acquiring a difference between the estimated pressure value and the measured pressure value ([0381], Fig, 27A and 27B, comparing a measured value for example, of a workpiece attribute with a predicted value generated through learnt function 2710).
In addition, Sakamoto discloses creating time-series data in which the difference and time are associated with each other (Fig. 12, Fig. 14-Fig. 15, [0067], [0125], [0172], Correlation data can be created based on time series data of first and second monitoring parameters stored in the apparatus data storage unit, and abnormality judgment data, data of change in degree of abnormality with time may be registered together, as shown in FIG. 25), and displaying the time-series data on a display unit ([0055], Fig. 25, Display 34 displaying the abnormality in the time series).
Regarding claims 22 and 24, Sakamoto discloses causing the computer to execute processing of predicting a time when the difference becomes equal to or more than a predetermined value based on the time-series data (Fig. 14-Fig. 15, [0116], an abnormality is detected. It is assumed that a detection value (detection value of external temperature) detected by one of the plurality of the external temperature sensors 10a has exceeded a threshold value at a point of time t1. Then, the abnormality detection program 41A determines that an abnormality has occurred).
Regarding claims 31 and 32, Clark discloses model generation method for generating a learning model (Fig. 11 and 19-20, [0076], [0128], processing engines to analyze the measured data and in-situ processing data and to implement algorithms, such as deep learning networks, machine learning algorithms, autonomous learning algorithms) comprising:
acquiring a first and second pressure value of a vacuum gauge (312a, 312b sensor and prob) provided in a first semiconductor manufacturing equipment (Abstract, [0329], process conducted by tool system 1910, sensors and probes comprising sensor component 1925 of an inspection system can collect data (e.g., data assets) associated with measured pressure, temperature, humidity, mass density, deposition rate, layer thickness, surface roughness, crystalline orientation, doping concentration, etc.); and
wherein the second training data is smaller in quantity than the first training data ([0343], [0343] Self-awareness component 2150 can determine a level of tool system degradation between a first acceptable operating state of the tool system 1910 and a subsequent state, at a later time, in which tool system has degraded);
fine-tuning the first learning model based on the acquired second training data ([0319], the autonomous learning system 1960 improves its performance over time—the autonomous system 1960 delivers improved results at a faster rate and with fewer resources consumed); and
generating, based on the acquired first and second training data (Fig. 11, Fig. 19, [0327], autonomous learning system 1960 can be trained through one or more training cycles), a first and second learning model that outputs an estimated pressure value if process data including at least two of chamber pressure, a valve opening degree of an automatic pressure control device and a kind and a flow rate of gas supplied to the first semiconductor manufacturing equipment is input ([0322], [0327], [0343], [0344], Autonomous learning system 1960 can be trained through one or more training cycles. Self-conceptualization component 2160 can learn the behavior of pressure in a deposition chamber of a given volume, in the presence of a specific gas flow, a temperature, exhaust valve angle, time, and the like).
Clark fails to disclose acquiring first and second training data including process data including at least two of chamber pressure a valve opening degree of an automatic pressure control device a kind gas and a flow rate of gas supplied to the first and second semiconductor manufacturing equipment and the acquired first and second pressure value.
However, Sakamoto discloses acquiring first and second training data including process data including at least two of chamber pressure ([0049], pressure control valve 25, pressure control device), a valve opening degree of an automatic pressure control device ([0049], [0067], [0070], [0140], The pressure control valve 25 is provided with a driver 26 for controlling the opening of a valve element 20 and an angle detector 27 for detecting an angle of the valve element 20) and a kind and a flow rate of gas supplied to the first and second ([0048], Fig. 9, a deposition gas supply source and a gas flow rate regulator, are connected to the proximal end of the injector 16 through a gas supply path), semiconductor manufacturing equipment and the acquired first and second pressure value (Fig. 1, [0018], [0049],[0050], the semiconductor manufacturing apparatus; predicting an opening of a pressure control valve corresponding to the cumulative film thickness at the end of the present deposition process based on previous process data including, as items, values of cumulative film thickness in deposition processes that have previously been performed and opening values of the pressure control valve corresponding to the cumulative film thickness).
Sakamoto and Clark are analogous art since both are related to semiconductor manufacturing control. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify monitoring parameters the semiconductor manufacturing apparatus status, taught by Sakamoto, incorporated with processing and measuring workpieces in a semiconductor processing, taught by Clark, in order to makes it easy to reliably detect an abnormality, identify the cause of the abnormality, or predict the abnormality.
Sakamoto and Clark fail to disclose that is to be indicated by a vacuum gauge, within an exhaust pipe located between first and second pumps of an exhaust system of with in the semiconductor manufacturing equipment based on the input process data.
However, Ishii discloses an estimated pressure value ([0016], [0019], the adjustment status of the pressure adjustment valve 15), that is to be indicated by the vacuum gauge (a vibration gauge 31), within the exhaust pipe located between the first and second pumps ([0019], mechanical booster pump 18 and dry pump 19) of the exhaust system ([0019], an exhaust pressure gauge 34) of the first semiconductor manufacturing equipment based on input process data ([0019], the real-time failure diagnosis module 533 then determines in real time whether the vacuum pump system (18, 19) is in a normal state or at a state just before failure based on the group of first failure diagnosis data), a mechanical booster pump 18 and a dry pump 19 are connected in series to each other so as to evacuate the inside of the production chamber 521 and the pressure control valve 15, a vacuum pump life expectancy prediction or a prediction of when an irregular shutoff caused by motor current or the temperature of a cooler within the vacuum pump may occur according to experience-based equipment management).
Ishii, Sakamoto and Clark are analogous art, they relate to semiconductor manufacturing control. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify avoiding irregular shutoff of production equipment, taught by Ishii, incorporated with teaching Sakamoto and Clark, as state above, for avoiding irregular shutoff of production equipment equipped with a vacuum pump and a system for avoiding irregular shutoff.
3.2 Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Clark et al. (US 20200083080 A1) in view of Sakamoto et al. (US 20080208385 A1) further in view of Ishii et al. (US 20030158705 A1). and furthermore, in view of Lijun (CN 111197157 A).
Regarding claims 15 and 25, the combination of Clark and Sakamoto discloses the limitation of claim 13 and claim 14, but fails to disclose the limitations of claim 15 and 25. However, Lijun discloses causing the computer to execute processing of acquiring the process data during an equipment idle state before a manufacturing process of supplying gas to the semiconductor manufacturing equipment (Page 6, Par. 6, Page 3, Par.7, collect data at a pumping section where the process chamber is converted from a run state to an idle (idle) state, analyze data, and the gas supply pipeline is communicated with the process chamber and is used for supplying the process gas to be processed).
Lijun, Sakamoto and Clark are analogous art. They relate to semiconductor manufacturing control. Therefore, before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to modify semiconductor manufacturing device, taught by Lijun, incorporated with the teaching of Ishii, Sakamoto and Clark , as state above, in order to optimize the semiconductor process by taking measure of interrupting the process when an abnormal state occur to reduce the defect rate.
Citation Pertinent prior art
4. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Goto (US 20180373233 A1) related to machine learning device included in a failure predicting apparatus includes a state observing unit that observes, as state variables indicating a current environmental state.
Ozeki (US 20200338677 A1) related to A machine learning device includes: an input data acquisition unit that acquires input data including arbitrary machining conditions for an arbitrary work in machining by an arbitrary machine tool and state information indicating a state of a cutting fluid before machining is performed under the machining conditions.
A reference to specific paragraphs, columns, pages, or figures in a cited prior art reference is not limited to preferred embodiments or any specific examples. It is well settled that a prior art reference, in its entirety, must be considered for allthat it expressly teaches and fairly suggests to one having ordinary skill in the art. Stated differently, a prior art disclosure reading on a limitation of Applicant's claim cannot be ignored on the ground that other embodiments disclosed wereinstead cited. Therefore, the Examiner's citation to a specific portion of a single prior art reference is not intended to exclusively dictate, but rather, to demonstrate an exemplary disclosure commensurate with the specific limitations being addressed. In re Heck, 699 F.2d 1331, 1332-33,216 USPQ 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1 009, 158 USPQ 275, 277 (CCPA 1968)). In re: Upsher-Smith Labs. v. Pamlab, LLC, 412 F.3d 1319, 1323, 75 USPQ2d 1213, 1215 (Fed. Cir. 2005); In re Fritch, 972 F.2d 1260, 1264, 23 USPQ2d 1780, 1782 (Fed. Cir. 1992); Merck& Co. v. Biocraft Labs., Inc., 874 F.2d804, 807, 10 USPQ2d 1843, 1846 (Fed. Cir. 1989); In re Fracalossi, 681 F.2d 792,794 n.1, 215 USPQ 569, 570 n.1 (CCPA 1982); In re Lamberti, 545 F.2d 747, 750, 192 USPQ 278, 280 (CCPA 1976); In re Bozek, 416 F.2d 1385, 1390, 163USPQ 545, 549 (CCPA 1969).
Conclusion
5. Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Kidest Worku whose telephone number is 571-272-3737. Examiner can be reached Mon-Fir (9am-5pm ET).
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Ali Mohammad, can be reached on 571-272-4105. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Examiner interviews are available via telephone and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
Information regarding the status of an application may be obtained from the Patent Application information Retrieval IPAIRI system. Status information for published applications may be obtained from either Private PMR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAG system, contact the Electronic Business Center (EBC) at 866-217- 9197.
/KIDEST WORKU/Primary Examiner, Art Unit 2119
Prosecution Insights