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 .
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments filed 11/04/2025 have been fully considered but they are not persuasive.
Applicant’s arguments on page 7-8, applicant argues “there is no apparent description in the cited portion of Toprac of initiating an adjustment based on one or more future states of the object. For example, there is no reference to a “future” state or anything comparable to make an adjustment”, “all the discussion relates to use of past information to make a current adjustment, e.g., the characteristic model created based on past data and that models the past data, feedback control based on already measured data, adjustment of previous processing steps, etc. There is no description in the cited portions of Toprac of a future state of a wafer, e.g., there is no apparent description in the cited portions of Toprac of any determination of what a state of a wafer will be in the future and then using that future state to make a current adjustment”.
Examiner respectfully disagrees becausemeasuring step j 110 may be input into a characteristic parameter model. The characteristic parameter model may map the one or more characteristic parameters measured in the measuring step j 110 onto one or more parameters that specify the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N)”, Col. 4, Line 25-34 “As shown in FIG. 4, the output signal 125 is sent from the characteristic parameter modeling step 120 and delivered to a target value setting step 130. In the target value setting step 130, the characteristic parameter model may be inverted to define one or more changes in the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N) that need to be made to bring the one or more characteristic parameter values measured in the measuring step j 110 within a range of specification values”, wherein examiner interpreted the bringing one or more characteristic parameter values measured in the measuring step within a range of specification values as the future state, wherein examiner interpreted the adjustments made based on bringing specification values within range as the future state used to make adjustments, wherein examiner interpreted the desired range of specification values as future states of the object.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-9, and 14-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Toprac et al. USP 6470230 (hereinafter “Toprac”).
Regarding claim 1, Toprac teaches a semiconductor processing method ([Abstract] “A method is provided. for manufacturing, the method including processing a workpiece”), the method comprising:
determining, with one or more physical processors (Col. 2, Line 31-40), a sequence of states of an object subject to a semiconductor manufacturing process, the states determined based on processing information associated with the object, wherein the sequence of states includes one or more future states of the object (Col. 3, Line 41-49 “As shown in FIG. 1, a workpiece 100, such as a semiconducting substrate or wafer, having one or more process layers and/or semiconductor devices such as an MOS transistor disposed thereon, for example, is delivered to a processing step j 105, where j may have any value from j=1 to j=N. The total number N of processing steps, such as masking, etching, depositing material and the like, used to form the finished workpiece 100, may range from N=1 to about any finite value”, and Col. 3, Line 50-60 “As shown in FIG. 2, the workpiece 100 is sent from the processing step j 105 and delivered to a measuring step j 110. In the measuring step j 110, the workpiece 100 is measured by having a metrology or measuring tool (not shown) measure one or more parameters characteristic of the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The measurements in the measuring step j 110 produce scan data 115 indicative of the one or more characteristic parameters measured in the measuring step j 110”, wherein examiner interpreted measuring one or more parameters characteristic of the processing performed in any previous processing steps as determining a sequence of states of an object subject to manufacturing process, the states determined based on processing information associated with the object, wherein the sequence of states includes one or more future states of the object, wherein examiner interpreted characteristics of processing which includes processing steps used to form the finished workpiece as future a sequence of states of an object including the future states of the object);
determining, with the one or more processors (Col. 2, Line 31-40), based on at least one of the states within the sequence of states and the one or more future states, a process metric associated with the object, the process metric comprising an indication of whether processing requirements for the object are satisfied for individual states in the sequence of states (Col. 4, Line 26-34 “the output signal 125 is sent from the characteristic parameter modeling step 120 and delivered to a target value setting step 130. In the target value setting step 130, the characteristic parameter model may be inverted to define one or more changes in the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N) that need to be made to bring the one or more characteristic parameter values measured in the measuring step j 110 within a range of specification values”, wherein examiner interpreted characteristic parameters measured to be within range of specification values as determining process metric associated with the object, and wherein characteristic parameter being within range as the process metric indicating whether processing requirements for the object are satisfied for individual states in the sequence of states); and
initiating, with the one or more processors (Col. 2, Line 31-40), an adjustment to the semiconductor manufacturing process based on (1) at least one of the states and the one or more future states and (2) the process metric, the adjustment configured to enhance the process metric for the individual states in the sequence of states such that final processing requirements for the object are satisfied (Col. 4, Line 36-54 “The inversion of the characteristic parameter model (based on the output signal 125) in the target value setting step 130 may be used to alert an engineer of the need to adjust the processing performed any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The engineer may also alter, for example, the type of characteristic parameter modeled in the characteristic parameter modeling z step 120, affecting the output signal 125 produced. As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, wherein examiner interpreted engineer altering characteristic parameter modeled in the characteristic parameter modeling step and feedback control signal sent to adjust processing performed in the processing step as initiating adjustment to the semiconductor manufacturing process based on based on (1) at least one of the states and the one or more future states and (2) the process metric, the adjustment configured to enhance the process metric for the individual states in the sequence of states such that final processing requirements for the object are satisfied, and Col. 4, Line 55 – Col. 5, Line 10).
Regarding claim 2, Toprac teaches wherein the sequence of states corresponds to a sequence of processing operations performed for the object, and further comprising: determining a policy function that defines processing operation corrections for individual states, for equipment for performing the processing operations, and/or for one or more process parameters for the processing operations (Col. 4, Line 45-54 “As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, wherein examiner interpreted adjusting processing performed in processing steps as determining a policy function that defines processing operation corrections for individual states, for equipment for performing the processing operations, and/or for one or more process parameters for processing operations); and/or
determining a value function that defines the enhancement of the process metric assuming the policy function is followed until completion of the sequence of processing operations (Col. 4, Line 55 – Col. 5, Line 10 “As shown in FIG. 6, in addition to, and/or instead of, the feedback control signal 135, target values 145 may be sent from the target value setting step 130 to a process change and control step 150. In the process change and control step 150, the target values 145 may be used in a high-level supervisory control loop. Thereafter, as shown in FIG. 7, a feedback control signal 155 may be sent from the process change and control step 150 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 155 may be sent from the process change and control step 150 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps. In various illustrative embodiments, output signals from final WET measurements may be used, in conjunction with measurements made at each operation or processing step j 105, where j may have any value from j=1 to j=N, and an invertible transistor model, to change setpoints at one or more of the processing steps j 105 in a supervisory manner such that subsequent production is driven closer to the WET measurement target values”, Col. 4, Line 25-35 “As shown in FIG. 4, the output signal 125 is sent from the characteristic parameter modeling step 120 and delivered to a target value setting step 130. In the target value setting step 130, the characteristic parameter model may be inverted to define one or more changes in the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N) that need to be made to bring the one or more characteristic parameter values measured in the measuring step j 110 within a range of specification values”, wherein examiner interpreted changing setpoints at one or more processing steps to bring characteristic parameter values measured to be within a range of specification values as determining a value function that defines the enhancement of the process metric assuming the policy function is followed until completion of the sequence of processing operations).
Regarding claim 3, Toprac teaches comprising determining the value function and wherein the value function defines an expected process metric for a given state (s) (Col. 4, Line 55 – Col. 5, Line 10 “As shown in FIG. 6, in addition to, and/or instead of, the feedback control signal 135, target values 145 may be sent from the target value setting step 130 to a process change and control step 150. In the process change and control step 150, the target values 145 may be used in a high-level supervisory control loop. Thereafter, as shown in FIG. 7, a feedback control signal 155 may be sent from the process change and control step 150 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 155 may be sent from the process change and control step 150 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps. In various illustrative embodiments, output signals from final WET measurements may be used, in conjunction with measurements made at each operation or processing step j 105, where j may have any value from j=1 to j=N, and an invertible transistor model, to change setpoints at one or more of the processing steps j 105 in a supervisory manner such that subsequent production is driven closer to the WET measurement target values”, Col. 4, Line 25-35 “As shown in FIG. 4, the output signal 125 is sent from the characteristic parameter modeling step 120 and delivered to a target value setting step 130. In the target value setting step 130, the characteristic parameter model may be inverted to define one or more changes in the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N) that need to be made to bring the one or more characteristic parameter values measured in the measuring step j 110 within a range of specification values”, wherein examiner interpreted changing or adjusting process as defining the expected process metric for a given state).
Regarding claim 4, Toprac teaches the method of claim 1, performed for a semiconductor processing environment, and the processed object is a semiconductor wafer, or one or more portions of the semiconductor wafer (Col. 3, Line 41-44 “a workpiece 100, such as a semiconducting substrate or wafer, having one or more process layers and/or semiconductor devices such as an MOS transistor disposed thereon”).
Regarding claim 5, Toprac teaches wherein the process metric comprises a reward, and the one or more processors comprise an agent (Col. 4, Line 36-54 “The inversion of the characteristic parameter model (based on the output signal 125) in the target value setting step 130 may be used to alert an engineer of the need to adjust the processing performed any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The engineer may also alter, for example, the type of characteristic parameter modeled in the characteristic parameter modeling z step 120, affecting the output signal 125 produced. As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, Col. 2, Line 31-40, wherein examiner interpreted defining changes as the reward, and model being formed in a computer as processors comprising an agent, and Col. 4, Line 55 – Col. 5, Line 10).
Regarding claim 6, Toprac teaches wherein the process metric comprises yield, and enhancing the process metric for the individual states in the sequence of states such that final processing requirements for the object are satisfied comprises increasing the yield (Col. 21, Line 29-43 “Any of the above-disclosed embodiments of a method of manufacturing according to the present invention enables the use of central values and spreads of parametric measurements sent from measuring tools and/or a wafer electrical test (WET) to make supervisory processing adjustments, either manually and/or automatically, to improve and/or better control the yield. Additionally, any of the above-disclosed embodiments of a method of manufacturing according to the present invention enables semiconductor device fabrication with increased device accuracy and precision, increased efficiency and increased device yield, enabling a streamlined and simplified process flow, thereby decreasing the complexity and lowering the costs of the manufacturing process and increasing throughput”).
Regarding claim 7, Toprac teaches wherein initiating the adjustment comprises (1) optimizing the process metric based on the sequence of states, and determining the adjustment based on the optimized process metric; and/or (2) prompting a user to make the adjustment (Col. 4, Line 36-54 “The inversion of the characteristic parameter model (based on the output signal 125) in the target value setting step 130 may be used to alert an engineer of the need to adjust the processing performed any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The engineer may also alter, for example, the type of characteristic parameter modeled in the characteristic parameter modeling z step 120, affecting the output signal 125 produced. As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, and Col. 4, Line 55 – Col. 5, Line 10, wherein examiner interpreted feedback signal sent to adjust processing steps, and an engineer altering characteristic parameter modeled in characteristic as optimizing the process metric based on the sequence of states, and determining the adjustment based on the optimized process metric; and/or (2) prompting a user to make the adjustment).
Regarding claim 8, Toprac teaches wherein the sequence of states corresponds to a sequence of processing operations performed for the object, and the adjustment comprises one or more selected from: a change in which processing operations are performed, a change in an order in which the processing operations are performed, or a change in one or more pieces of equipment used to perform one or more of the processing operations (Col. 4, Line 36-54 “The inversion of the characteristic parameter model (based on the output signal 125) in the target value setting step 130 may be used to alert an engineer of the need to adjust the processing performed any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The engineer may also alter, for example, the type of characteristic parameter modeled in the characteristic parameter modeling z step 120, affecting the output signal 125 produced. As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, and Col. 4, Line 55 – Col. 5, Line 10).
Regarding claim 9, Toprac teaches wherein the sequence of states corresponds to a sequence of processing operations performed for the object, and wherein the processing information comprises one or more selected from: values of measurements of the object performed as part of the processing operations, an indication of which processing operations were performed, an indication of an order of the sequence of processing operations, an indication of which equipment was used in the processing operations and/or associated machine constants, or processing parameters of the processing operations (Col. 3, Line 41-49 “As shown in FIG. 1, a workpiece 100, such as a semiconducting substrate or wafer, having one or more process layers and/or semiconductor devices such as an MOS transistor disposed thereon, for example, is delivered to a processing step j 105, where j may have any value from j=1 to j=N. The total number N of processing steps, such as masking, etching, depositing material and the like, used to form the finished workpiece 100, may range from N=1 to about any finite value”, and Col. 3, Line 50-60 “As shown in FIG. 2, the workpiece 100 is sent from the processing step j 105 and delivered to a measuring step j 110. In the measuring step j 110, the workpiece 100 is measured by having a metrology or measuring tool (not shown) measure one or more parameters characteristic of the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The measurements in the measuring step j 110 produce scan data 115 indicative of the one or more characteristic parameters measured in the measuring step j 110”).
Regarding claim 14, Toprac teaches a computer program product comprising a non-transitory computer-readable medium comprising instructions therein, the instructions configured to, when executed on a computer system, cause the computer system to at least (Col. 2, Line 31-40):
determine a sequence of states of an object subject to a semiconductor manufacturing process, the states determined based on processing information associated with the object, wherein the sequence of states includes one or more future states of the object (Col. 3, Line 41-49 “As shown in FIG. 1, a workpiece 100, such as a semiconducting substrate or wafer, having one or more process layers and/or semiconductor devices such as an MOS transistor disposed thereon, for example, is delivered to a processing step j 105, where j may have any value from j=1 to j=N. The total number N of processing steps, such as masking, etching, depositing material and the like, used to form the finished workpiece 100, may range from N=1 to about any finite value”, and Col. 3, Line 50-60 “As shown in FIG. 2, the workpiece 100 is sent from the processing step j 105 and delivered to a measuring step j 110. In the measuring step j 110, the workpiece 100 is measured by having a metrology or measuring tool (not shown) measure one or more parameters characteristic of the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The measurements in the measuring step j 110 produce scan data 115 indicative of the one or more characteristic parameters measured in the measuring step j 110”, wherein examiner interpreted measuring one or more parameters characteristic of the processing performed in any previous processing steps as determining a sequence of states of an object subject to manufacturing process, the states determined based on processing information associated with the object, wherein the sequence of states includes one or more future states of the object, wherein examiner interpreted characteristics of processing which includes processing steps used to form the finished workpiece as future a sequence of states of an object including the future states of the object);
determine, based on at least one of the states within the sequence of states and the one or more future states, a process metric associated with the object, the process metric comprising an indication of whether processing requirements for the object are satisfied for individual states in the sequence of states (Col. 4, Line 26-34 “the output signal 125 is sent from the characteristic parameter modeling step 120 and delivered to a target value setting step 130. In the target value setting step 130, the characteristic parameter model may be inverted to define one or more changes in the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N) that need to be made to bring the one or more characteristic parameter values measured in the measuring step j 110 within a range of specification values”, wherein examiner interpreted characteristic parameters measured to be within range of specification values as determining process metric associated with the object, and wherein characteristic parameter being within range as the process metric indicating whether processing requirements for the object are satisfied for individual states in the sequence of states); and
initiate an adjustment to a processing process based on (1) at least one of the states and the one or more future states and (2) the process metric, the adjustment configured to enhance the process metric for the individual states in the sequence of states such that final processing requirements for the object are satisfied (Col. 4, Line 36-54 “The inversion of the characteristic parameter model (based on the output signal 125) in the target value setting step 130 may be used to alert an engineer of the need to adjust the processing performed any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The engineer may also alter, for example, the type of characteristic parameter modeled in the characteristic parameter modeling z step 120, affecting the output signal 125 produced. As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, wherein examiner interpreted engineer altering characteristic parameter modeled in the characteristic parameter modeling step and feedback control signal sent to adjust processing performed in the processing step as initiating adjustment to the semiconductor manufacturing process based on based on (1) at least one of the states and the one or more future states and (2) the process metric, the adjustment configured to enhance the process metric for the individual states in the sequence of states such that final processing requirements for the object are satisfied, and Col. 4, Line 55 – Col. 5, Line 10).
Regarding claim 15, Toprac teaches wherein the sequence of states corresponds to a sequence of processing operations performed for the object, and wherein the instructions are further configured to cause the computer system to:
determine a policy function that defines processing operation corrections for individual states, equipment for performing the processing operations, and/or one or more process parameters for the processing operations (Col. 4, Line 45-54 “As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, wherein examiner interpreted adjusting processing performed in processing steps as determining a policy function that defines processing operation corrections for individual states, for equipment for performing the processing operations, and/or for one or more process parameters for processing operations); and/or
determine a value function that that defines the enhancement of the process metric assuming the policy function is followed until completion of the sequence of processing operations (Col. 4, Line 55 – Col. 5, Line 10 “As shown in FIG. 6, in addition to, and/or instead of, the feedback control signal 135, target values 145 may be sent from the target value setting step 130 to a process change and control step 150. In the process change and control step 150, the target values 145 may be used in a high-level supervisory control loop. Thereafter, as shown in FIG. 7, a feedback control signal 155 may be sent from the process change and control step 150 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 155 may be sent from the process change and control step 150 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps. In various illustrative embodiments, output signals from final WET measurements may be used, in conjunction with measurements made at each operation or processing step j 105, where j may have any value from j=1 to j=N, and an invertible transistor model, to change setpoints at one or more of the processing steps j 105 in a supervisory manner such that subsequent production is driven closer to the WET measurement target values”, Col. 4, Line 25-35 “As shown in FIG. 4, the output signal 125 is sent from the characteristic parameter modeling step 120 and delivered to a target value setting step 130. In the target value setting step 130, the characteristic parameter model may be inverted to define one or more changes in the processing performed in any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N) that need to be made to bring the one or more characteristic parameter values measured in the measuring step j 110 within a range of specification values”, wherein examiner interpreted changing setpoints at one or more processing steps to bring characteristic parameter values measured to be within a range of specification values as determining a value function that defines the enhancement of the process metric assuming the policy function is followed until completion of the sequence of processing operations).
Regarding claim 16, Toprac teaches wherein the process metric comprises a reward, and the one or more processors comprise an agent (Col. 4, Line 36-54 “The inversion of the characteristic parameter model (based on the output signal 125) in the target value setting step 130 may be used to alert an engineer of the need to adjust the processing performed any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The engineer may also alter, for example, the type of characteristic parameter modeled in the characteristic parameter modeling z step 120, affecting the output signal 125 produced. As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, Col. 2, Line 31-40, wherein examiner interpreted defining changes as the reward, and model being formed in a computer as processors comprising an agent, and Col. 4, Line 55 – Col. 5, Line 10).
Regarding claim 17, Toprac teaches wherein the process metric comprises yield, and enhancement of the process metric for the individual states in the sequence of states such that final processing requirements for the object are satisfied comprises increasing of the yield (Col. 21, Line 29-43 “Any of the above-disclosed embodiments of a method of manufacturing according to the present invention enables the use of central values and spreads of parametric measurements sent from measuring tools and/or a wafer electrical test (WET) to make supervisory processing adjustments, either manually and/or automatically, to improve and/or better control the yield. Additionally, any of the above-disclosed embodiments of a method of manufacturing according to the present invention enables semiconductor device fabrication with increased device accuracy and precision, increased efficiency and increased device yield, enabling a streamlined and simplified process flow, thereby decreasing the complexity and lowering the costs of the manufacturing process and increasing throughput”).
Regarding claim 18, Toprac teaches wherein the instruction configured to cause the computer system to initiate the adjustment are further configured to cause the computer system to (1) optimize the process metric based on the sequence of states, and determine the adjustment based on the optimized process metric; and/or (2) prompt a user to make the adjustment (Col. 4, Line 36-54 “The inversion of the characteristic parameter model (based on the output signal 125) in the target value setting step 130 may be used to alert an engineer of the need to adjust the processing performed any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The engineer may also alter, for example, the type of characteristic parameter modeled in the characteristic parameter modeling z step 120, affecting the output signal 125 produced. As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, and Col. 4, Line 55 – Col. 5, Line 10, wherein examiner interpreted feedback signal sent to adjust processing steps, and an engineer altering characteristic parameter modeled in characteristic as optimizing the process metric based on the sequence of states, and determining the adjustment based on the optimized process metric; and/or (2) prompting a user to make the adjustment).
Regarding claim 19, Toprac teaches wherein the sequence of states corresponds to a sequence of processing operations performed for the object, and wherein the adjustment comprises one or more selected from: a change in which processing operations are performed, a change in an order in which the processing operations are performed, or a change in one or more pieces of equipment used to perform one or more of the processing operations, and/or wherein the processing information comprises one or more selected from: values of measurements of the object performed as part of the processing operations, an indication of which processing operations were performed, an indication of an order of the sequence of processing operations, an indication of which equipment was used in the processing operations and/or associated machine constants, or processing parameters of the processing operations (Col. 4, Line 36-54 “The inversion of the characteristic parameter model (based on the output signal 125) in the target value setting step 130 may be used to alert an engineer of the need to adjust the processing performed any of the previous processing steps (such as processing step j 105, where j may have any value from j=1 to j=N). The engineer may also alter, for example, the type of characteristic parameter modeled in the characteristic parameter modeling z step 120, affecting the output signal 125 produced. As shown in FIG. 5, a feedback control signal 135 may be sent from the target value setting step 130 to the processing step j 105 to adjust the processing performed in the processing step j 105. In various alternative illustrative embodiments (not shown), the feedback control signal 135 may be sent from the target value setting step 130 to any of the previous processing steps (similar to processing step j 105, where j may have any value from j=1 to j=N) to adjust the processing performed in any of the previous processing steps”, and Col. 4, Line 55 – Col. 5, Line 10).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim 10-11, and 20 is rejected under 35 U.S.C. 103 as being unpatentable over Toprac et al. USP 6470230 (hereinafter “Toprac”) as applied to claims 1-9, and 14-19 above, in view of NAKADA et al. USPGPUB 2019/0369605 (hereinafter “NAKADA”).
Regarding claim 10, Toprac teaches all of the features with respect to claim 1 as outlined above.
Toprac does not explicitly teach wherein the determining the sequence of states, determining the process metric, and initiating the adjustment are performed as at least part of a model free reinforcement learning (MFRL) framework.
However, NAKADA teaches wherein the determining the sequence of states, determining the process metric, and initiating the adjustment are performed as at least part of a model free reinforcement learning (MFRL) framework (Paragraph [0037] “The analysis system 20 determines an appropriate processing condition for each of a plurality of processes included in a processing procedure by performing reinforcement learning. Examples of a reinforcement learning algorithm include temporal difference (TD) learning such as Q-learning and SARSA, a policy gradient method such as an actor-critic method, and Monte Carlo method. It should be noted that the present invention is not limited to the reinforcement learning algorithm”, and Paragraph [0038] “When the reinforcement learning is applied to a semiconductor manufacturing process, the semiconductor processing apparatus 10 is treated as an environment, a shape of a sample is treated as a state, and the processing condition is treated as an action”, wherein examiner interpreted reinforcement learning applied to semiconductor manufacturing process that is treated as an environment as using a model free reinforcement learning (MFRL) framework).
Toprac, and NAKADA are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to manufacturing optimization.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above semiconductor processing method, as taught by Toprac, and incorporating model free reinforcement learning (MFRL) framework, as taught by NAKADA.
One of ordinary skill in the art would have been motivated to improve optimizing semiconductor manufacturing process, as suggested by NAKADA (see Paragraphs [0002-0008]).
Regarding claim 11, Toprac, and NAKADA teaches all of the features with respect to claim 10 as outlined above.
NAKADA further teaches wherein the MFRL framework comprises one or more selected from: an asynchronous advantage actor-critic algorithm, a Q-learning with normalized advantage function, a trust region policy optimization algorithm, a proximal policy optimization algorithm, a twin delayed deep deterministic policy gradient, or a soft actor-critic algorithm (Paragraph [0037] “The analysis system 20 determines an appropriate processing condition for each of a plurality of processes included in a processing procedure by performing reinforcement learning. Examples of a reinforcement learning algorithm include temporal difference (TD) learning such as Q-learning and SARSA, a policy gradient method such as an actor-critic method, and Monte Carlo method. It should be noted that the present invention is not limited to the reinforcement learning algorithm”, and Paragraph [0038] “When the reinforcement learning is applied to a semiconductor manufacturing process, the semiconductor processing apparatus 10 is treated as an environment, a shape of a sample is treated as a state, and the processing condition is treated as an action”, wherein examiner interpreted reinforcement learning applied to semiconductor manufacturing process that is treated as an environment as using a model free reinforcement learning (MFRL) framework, and wherein the reinforcement learning comprise Q-learning, and actor-critic algorithm).
Regarding 20, Toprac teaches all of the features with respect to claim 14 as outlined above.
Toprac does not explicitly teach wherein the determination of the sequence of states, determination of the process metric, and initiation of the adjustment are performed as at least part of a model free reinforcement learning (MFRL) framework.
However, NAKADA further teaches wherein the determination of the sequence of states, determination of the process metric, and initiation of the adjustment are performed as at least part of a model free reinforcement learning (MFRL) framework (Paragraph [0037] “The analysis system 20 determines an appropriate processing condition for each of a plurality of processes included in a processing procedure by performing reinforcement learning. Examples of a reinforcement learning algorithm include temporal difference (TD) learning such as Q-learning and SARSA, a policy gradient method such as an actor-critic method, and Monte Carlo method. It should be noted that the present invention is not limited to the reinforcement learning algorithm”, and Paragraph [0038] “When the reinforcement learning is applied to a semiconductor manufacturing process, the semiconductor processing apparatus 10 is treated as an environment, a shape of a sample is treated as a state, and the processing condition is treated as an action”, wherein examiner interpreted reinforcement learning applied to semiconductor manufacturing process that is treated as an environment as using a model free reinforcement learning (MFRL) framework).
Toprac, and NAKADA are analogous art because they are from the same field of endeavor and contain overlapping structural and functional similarities. They relate to manufacturing optimization.
Therefore, before the time of effective filing date, it would have been obvious to a person of ordinary skill in the art to modify the above semiconductor processing method, as taught by Toprac, and incorporating model free reinforcement learning (MFRL) framework, as taught by NAKADA.
One of ordinary skill in the art would have been motivated to improve optimizing semiconductor manufacturing process, as suggested by NAKADA (see Paragraphs [0002-0008]).
Allowable Subject Matter
Claims 12-13 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.
Citation of Pertinent Prior Art
The prior art made of record and on the attached PTO Form 892 but not relied upon is considered pertinent to applicant's disclosure.
Fahrenkopf et al. (USPGPUB 2021/0247744) teaches manufacturing process control, closed-loop control is provided (18) based on a constrained reinforcement learned network (32).
UEDA et al. (USPGPUB 2018/0284739) teaches a quality control apparatus.
Nazari et al. (USPGPUB 2019/0102676) teaches long-term value in a machine learning system.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/D.P./Examiner, Art Unit 2119
/MOHAMMAD ALI/Supervisory Patent Examiner, Art Unit 2119