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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 01/28/2026 has been entered.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sasaki et al. (US 2020/0098653) in view of Hao (US 8,840,754).
Regarding claim 1, Sasaki et al discloses: An abnormality detection apparatus (paragraph 0006) for detecting lifting abnormality of a substrate in a substrate processing apparatus (see fig. 4) including a lifting mechanism (#218) for lifting the substrate (see fig. 8, where a measurement value collection step is detailed. A plurality of devices has a measuring part that receives measurement values mentioned in paragraph 0019. In the plurality of devices, an elevating part #218 is included mentioned in paragraph 0084, The CPU #260a then controls elevating operation of #218 according to a process recipe. The measurement value of the elevating part #218 is received in the controller #260 and used as teaching data for a learning program for then setting a parameter for the substrate mentioned in paragraphs 0114-0115. The result obtained from the learning process is used as an update parameter and transmitted to the controller to each device [which includes the elevating part #218] mentioned in paragraph 0117. Paragraph 0118 explains an example of how the updated parameter is transmitted to the controller. The updated parameter is compared with a pre-learned parameter to determine if it’s within a predetermined range, and if it is within that range, it is deemed correct and the parameter updating step is performed mentioned in paragraphs 0125-0126), comprising: a measurement section configured to measure a parameter having a correlation with the lifting mechanism (measurement value of the elevating part #218 is received in the controller #260 mentioned in paragraphs 0082, 0114-0115. The elevating part #128 raise/lowers substrate #200 mentioned in paragraph 0063); and a detection section configured to detect lifting abnormality of the substrate (measurement values of elevating device #218 has a corresponding pre-learned parameter and is compared with updated parameter of the measurement value obtained through the learning process. After comparison and if it is correct/incorrect, the updated parameter is sent to the elevating mechanism device as explained above. Comparison is explained in paragraphs 0125-0128 and updated parameter obtained is explained paragraphs 0114-0117), wherein the detection section includes a learning model generated by using machine learning (a learning process is used to for generating an updated parameter with the measurement values obtained from the elevating mechanism #218 mentioned in paragraphs 0113-0117), where the learning model receives, as input, a plurality of measurements of the parameter continuously measured by the measurement section during lifting-up of the substrate by the lifting mechanism (measurement values of the elevating device #218 is input as learning update for the learning process mentioned in paragraph 0115. the learning timing of the learning process may be changed to be performed throughout the substrate processing as mentioned in paragraph 0129), and outputs a value as an indication of a lifting abnormality of the substrate (based on the results of the learning process a setting parameter corresponding to the process recipe is generated as an updated parameter and the controller will transmit the update parameter to the elevating mechanism #218 mentioned in paragraph 0116-0117. An example of how the process would work for the heating part would work is explained in paragraph 0118, where setting of temperature regulator is updated after learning process and temperature regulator parameters are updated. As mentioned in paragraph 0082, the device update can be the elevating mechanism and thus the learning process would output elevating mechanism #218 parameter to be updated and thus the elevating mechanism is set according to learning process comparison with pre learned data).
Sasaki fails to directly disclose: a measurement section configured to measure a parameter having a correlation with load applied to the lifting mechanism.
In the same field of endeavor, namely substrate processor devices, Hao teaches: a measurement section (pneumatic sensor #205, which can be a strain gauge or other substitute mentioned in column 5, lines 25-28) configured to measure a parameter having a correlation with load applied to the lifting mechanism (pneumatic sensor #205 is connected to pneumatic cylinder #204 and measures air pressure inside cylinder beneath piston #302 that moves the pneumatic lift pin #203 of the lifting mechanism #203a, #203b, #203c mentioned in column 4, lines 53-67. The pneumatic cylinder #204 uses gas (air pressure) to raise and lift the pins #203 mentioned in column 5, lines 42-53), a plurality of measurements of the parameter continuously measured by the measurement section during lifting-up of the substrate by the lifting mechanism and outputs a level of lifting abnormality of the substrate (sensors #205a, #205b, and #205c provide data to the sensor feedback module #210. In turn, sensor feedback module #210 transmits data based on this data to the activation state logic #208, which might employ a threshold circuit #209, in an example embodiment. So for example, if the data transmitted by a specific sensor indicates an electrostatic force greater than a specific threshold, the activation state logic #208 might use the received data to cause the pneumatic controller #207 to cease further upward movement of a specific lift pin. This indicates that when the measurement of pneumatic pressure sensors #205 exceeds a value, an abnormality is detected and lift pin movement is modified; further supported in column 11, lines 1-10).
It would be obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the measurement section parameters received by the learning model of Sasaki et al so that the measurement section is configured to measure a parameter having a correlation with load applied to the lifting mechanism as taught by Hao (Hao explicitly states in column 11, lines 60-column 12, lines 26 that: the above embodiments in mind, it should be understood that the invention may employ various computer-implemented operations involving data stored in computer systems. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. Further, the manipulations performed are often referred to in terms, such as producing, identifying, determining, or comparing.” So, measurement parameters of pneumatic sensors can be stored and manipulated in computer systems and embodied as computer readable code, thus using these parameters in the learning model of Sasaki and outputting an abnormality, a threshold value exceeded as explained in the example above, to control force of the lift pins would be obvious for someone of ordinary skill in the art) in order to adjust the force of the lifting mechanism of the substrate and avoid damage (column 2, lines 5-7).
Regarding claim 2, the modified device of Sasaki et al substantially teaches claim 1, the modified device of Sasaki et al further discloses: wherein the learning model is generated by using machine learning (paragraph 0082, 0113-0115 of Sasaki) such that in the case of receipt of a plurality of measurements of the parameter taken when lifting of the substrate is normal as an input of teaching data (input device #261 shows state of substrate processing apparatus and measurement values taken throughout the substrate processing are taken as input data for the learning model mentioned in paragraph 0115 of Sasaki), the learning model outputs the value as an indication of normal lifting of the substrate (a parameter comparison step is performed based on the updated parameter obtained by the learning step and is compared with a pre-updated parameter. This difference between the two parameters is compared to check if it’s within a predetermined range and depending if its correct or incorrect, it will either update the parameter or send an alarm notification to the host device #500 and the display screen #264 mentioned in paragraph 0121-0127 of Sasaki).
Regarding claim 13, Sasaki et al teaches: An abnormality detection apparatus (paragraph 0006) for detecting lifting abnormality of a substrate (#200) lifted by a lifting mechanism (see fig. 8, where a measurement value collection step is detailed. A plurality of devices has a measuring part that receives measurement values mentioned in paragraph 0019. In the plurality of devices, an elevating part #218 is included mentioned in paragraph 0084, The CPU #260a then controls elevating operation of #218 according to a process recipe. The measurement value of the elevating part #218 is received in the controller #260 and used as teaching data for a learning program for then setting a parameter for the substrate mentioned in paragraphs 0114-0115. The result obtained from the learning process is used as an update parameter and transmitted to the controller to each device [which includes the elevating part #218] mentioned in paragraph 0117. Paragraph 0118 explains an example of how the updated parameter is transmitted to the controller. The updated parameter is compared with a pre-learned parameter to determine if it’s within a predetermined range, and if it is within that range, it is deemed correct and the parameter updating step is performed mentioned in paragraphs 0125-0126), comprising: a measurement section configured to measure a parameter having a correlation with the lifting mechanism (measurement value of the elevating part #218 is received in the controller #260 mentioned in paragraphs 0082, 0114-0115. The elevating part #128 raise/lowers substrate #200 mentioned in paragraph 0063); and a detection section configured to detect lifting abnormality of the substrate (measurement values of elevating device #218 has a corresponding pre-learned parameter and is compared with updated parameter of the measurement value obtained through the learning process. After comparison and if it is correct/incorrect, the updated parameter is sent to the elevating mechanism device as explained above. Comparison is explained in paragraphs 0125-0128 and updated parameter obtained is explained paragraphs 0114-0117), wherein the detection section includes a learning model generated by using machine learning (a learning process is used to for generating an updated parameter with the measurement values obtained from the elevating mechanism #218 mentioned in paragraphs 0113-0117), where the learning model receives, as input, a plurality of measurements of the parameter measured in time series by the measurement section during lifting-up of the substrate by the lifting mechanism (the learning timing or cycle may be appropriately changed, where measurement values of the elevating device #218 is input as learning update for the learning process mentioned in paragraph 0115. The learning timing of the learning process may be executed in real time and sequentially perform the updating step throughout the substate processing step as mentioned in paragraph 0129), and outputs a value as an indication of a lifting abnormality of the substrate (based on the results of the learning process a setting parameter corresponding to the process recipe is generated as an updated parameter and the controller will transmit the update parameter to the elevating mechanism #218 mentioned in paragraph 0116-0117. An example of how the process would work for the heating part would work is explained in paragraph 0118, where setting of temperature regulator is updated after learning process and temperature regulator parameters are updated. As mentioned in paragraph 0082, the device update can be the elevating mechanism and thus the learning process would output elevating mechanism #218 parameter to be updated and thus the elevating mechanism is set according to learning process comparison with pre learned data).
Sasaki fails to directly disclose: a measurement section configured to measure a parameter having a correlation with load applied to the lifting mechanism.
In the same field of endeavor, namely substrate processor devices, Hao teaches: a measurement section (pneumatic sensor #205, which can be a strain gauge or other substitute mentioned in column 5, lines 25-28) configured to measure a parameter having a correlation with load applied to the lifting mechanism (pneumatic sensor #205 is connected to pneumatic cylinder #204 and measures air pressure inside cylinder beneath piston #302 that moves the pneumatic lift pin #203 of the lifting mechanism #203a, #203b, #203c mentioned in column 4, lines 53-67. The pneumatic cylinder #204 uses gas (air pressure) to raise and lift the pins #203 mentioned in column 5, lines 42-53), a plurality of measurements of the parameter continuously measured by the measurement section during lifting-up of the substrate by the lifting mechanism and outputs a level of lifting abnormality of the substrate (sensors #205a, #205b, and #205c provide data to the sensor feedback module #210. In turn, sensor feedback module #210 transmits data based on this data to the activation state logic #208, which might employ a threshold circuit #209, in an example embodiment. So for example, if the data transmitted by a specific sensor indicates an electrostatic force greater than a specific threshold, the activation state logic #208 might use the received data to cause the pneumatic controller #207 to cease further upward movement of a specific lift pin. This indicates that when the measurement of pneumatic pressure sensors #205 exceeds a value, an abnormality is detected and lift pin movement is modified; further supported in column 11, lines 1-10).
It would be obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the measurement section parameters received by the learning model of Sasaki et al so that the measurement section is configured to measure a parameter having a correlation with load applied to the lifting mechanism as taught by Hao (Hao explicitly states in column 11, lines 60-column 12, lines 26 that: the above embodiments in mind, it should be understood that the invention may employ various computer-implemented operations involving data stored in computer systems. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. Further, the manipulations performed are often referred to in terms, such as producing, identifying, determining, or comparing.” So, measurement parameters of pneumatic sensors can be stored and manipulated in computer systems and embodied as computer readable code, thus using these parameters in the learning model of Sasaki and outputting an abnormality, a threshold value exceeded as explained in the example above, to control force of the lift pins would be obvious for someone of ordinary skill in the art) in order to adjust the force of the lifting mechanism of the substrate and avoid damage (column 2, lines 5-7).
Claims 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sasaki et al. (US 2020/0098653), in view of Hao (US 8,840,754) and in further view of Matsuda et al. (US 6,255,223).
Regarding claim 3, the modified device of Sasaki et al substantially discloses claim 1, except Sasaki et al fails to directly disclose: wherein the substrate processing apparatus includes an electrostatic chuck for electrostatically attracting the substrate when processing the substrate, and the detection section detects lifting abnormality of the substrate attributable to detachment failure of the substrate based on a plurality of measurements of the parameter continuously measured by the measurement section during lifting-up of the substrate by the lifting mechanism after stopping electrostatic attraction of the substrate by the electrostatic chuck.
In the same field of endeavor, namely chuck devices, Matsuda et al teaches: wherein the substrate processing apparatus includes an electrostatic chuck (#3 is an electrostatic attraction type holder) for electrostatically attracting the substrate when processing the substrate, and the detection section detects lifting abnormality of the substrate attributable to detachment failure of the substrate based on a plurality of measurements of the parameter continuously measured by the measurement section during lifting-up of the substrate by the lifting mechanism after stopping electrostatic attraction of the substrate by the electrostatic chuck (when data of attractive force being received by controller #18 is below a predetermined value, it detects that the substrate is completely released form the electrostatic chuck and movement of push-up mechanism #15 is stopped mentioned in column 4, lines 15-25).
It would be obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the mounting stand (#212) and learning model (paragraph 0082) of the modified device of Sasaki et al so that the substrate processing apparatus includes an electrostatic chuck for electrostatically attracting the substrate and the detection section detects lifting abnormality of the substrate attributable to detachment failure of the substrate based on a plurality of measurements of the parameter continuously measured by the measurement section during lifting-up of the substrate by the lifting mechanism after stopping electrostatic attraction of the substrate by the electrostatic chuck as taught by Matsuda et al in order to allow chucking of the substrate and control of chucking of the substrate with the heigh data of the elevating part.
Regarding claim 4, the modified device of Sasaki et al substantially discloses claim 1, except Sasaki et al fails to directly disclose: wherein the lifting mechanism comprises a lift pin which is brought into contact with a rear face of the substrate and lifts the substrate, and a cylinder apparatus which is connected to the lift pin and lifts the lift pin, and wherein the measurement section is a load cell which is attached to a piston rod of the cylinder apparatus and measures load applied to the piston rod.
In the same field of endeavor, namely chuck devices, Matsuda et al teaches: wherein the lifting mechanism comprises a lift pin (see fig. 1, where #15 has two lift pins) which is brought into contact with a rear face of the substrate and lifts the substrate (see fig. 1, where lift pins of #15 go into contact of rear face of substrate #2 to lift the substrate #2), and a cylinder apparatus (see fig. 1, where lift pins #15 have a cylinder apparatus connected below them) which is connected to the lift pin and lifts the lift pin (see fig. 1), and wherein the measurement section is a load cell (measurement section #16 is a load cell mentioned in column 6, line 27) which is attached to a piston rod of the cylinder apparatus (drive means #17 moves push device #15 may be a hydraulic cylinder which has a piston rod mentioned in column 6, line 45) and measures load applied to the piston rod (piston rod of #17 connects directly to push up mechanism #15 and their force produced to push is the same, thus measurement means #16 will detect the load data of both seen in fig. 1).
It would be obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the elevating mechanism (#218) of the modified device of Sasaki et al so the lifting mechanism comprises a lift pin which is brought into contact with a rear face of the substrate and lifts the substrate, and a cylinder apparatus which is connected to the lift pin and lifts the lift pin, and wherein the measurement section is a load cell which is attached to a piston rod of the cylinder apparatus and measures load applied to the piston rod as taught by Matsuda et al in order to provide two are fastening points that can be released quickly with a pushbutton and replace other lifting gear that remain attached to the workpiece.
Claim 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sasaki et al. (US 2020/0098653), in view of Hao (US 8,840,754) and in further view of Naohara et al. (US 11,908,752).
Regarding claim 5, the modified device of Sasaki et al substantially discloses claim 1, except the modified device of Sasaki et al fails to directly disclose: wherein the learning model is generated by using machine learning based on a k-nearest neighbor algorithm.
In the same field of endeavor, namely substrate processor devices, Naohara et al teaches: wherein the learning model is generated by using machine learning based on a k-nearest neighbor algorithm (machine learning unit #92 uses a neighbor algorithm to process data obtained by sensors of #70; column 26, lines 20-23).
It would be obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer (learning process) of the modified device of Sasaki et al so that the learning model is generated by using machine learning based on a k-nearest neighbor algorithm as taught by Naohara et al in order to allow classification and predictions on how the substrate will behave due to received load force data and improve force efficiency applied to the substrate saving in energy usage.
Claim 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sasaki et al. (US 2020/0098653), in view of Hao (US 8,840,754) and in further view of Son (US 7,770,478).
Regarding claim 7, the modified device of Sasaki et al substantially discloses claim 1, the modified device of Sasaki et al further discloses: wherein the detection section includes an input section configured to input presence or absence of lifting abnormality of the substrate, and stores the presence or absence of lifting abnormality of the substrate input from the input section (input device #261 is a touch panel used as a notifying part for notifying a state of the processing apparatus #100 and used to input an operation command to read a process recipe on the CPU, that process can include elevating operation of the elevating mechanism #218 mentioned in paragraph 0085 of Sasaki).
Sasaki et al fails to directly disclose: wherein the detection section includes an input section configured to input presence or absence of lifting abnormality of the substrate, and stores the presence or absence of lifting abnormality of the substrate input from the input section in association with a plurality of measurements of the parameter continuously measured by the measurement section, and detection result of lifting abnormality of the substrate by the detection section.
In the same field of endeavor, namely chuck control devices, Son teaches: wherein the detection section includes an input section configured to input presence or absence of lifting abnormality of the substrate (controller #160 receives sensor data #102 to see if substrate #S is attached or detached; column 6, lines 39-40), and stores the presence or absence of lifting abnormality of the substrate input from the input section (the controller detects whether the substrate #S is attached or not; column 4, lines 57-58), in association with a plurality of measurements of the parameter continuously measured by the measurement section (controller #160 detects whether substrate is attached or not, it is associated with the plurality of load of the load applying device and adjust the load value mentioned in column 4, lines 57-67), and detection result of lifting abnormality of the substrate by the detection section (the abnormality is when substrate #S is attached or detached).
It would be obvious for someone of ordinary skill in the art before the effective filing date of the claimed invention to modify the computer (#260) of the modified device of Sasaki et al so that the input presence or absence of the lifting abnormality of the substrate from the input section is in association with a plurality of measurements of the parameter continuously measured by the measurement section and detection result of lifting abnormality of the substrate by the detection section as taught by Son in order to add an additional check to detect when substrate is on the chuck besides only measuring of load on lifting pins and prevent an accidental activation of the lifting pins or chuck when there is no substrate present.
Response to Arguments
Applicant's arguments filed 01/28/2026 have been fully considered but they are not persuasive. Applicant argues that Sasaki does not teach the output of a value indicating a lifting abnormality of the substrate. This argument is respectfully traversed.
Applicant argues that, according to paragraphs [0115-0116] of Sasaki, the generated height measurement is generated as an input to the learning model and an update parameter is transmitted. However, the updated height parameter that is generated and applied back to the system is a new value that does provide indication that that is an abnormality to what the expected height value was.
Accordingly, the rejections are maintained.
Conclusion
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/ERIC DANIEL WHITMIRE/Examiner, Art Unit 3722
/SUNIL K SINGH/Supervisory Patent Examiner, Art Unit 3722
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