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 .
Election/Restrictions
Examiner acknowledges Applicant’s election without traverse of species I having claims 1-10,17-18 and 20 in the reply filed on 07/17/2026.
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.
Claim(s) 1-8,10,17,18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Strang (US 20050071035 A1) in view of over Sawlani et al. (US 20230049157 A1).
Regarding claim 1, Strang teaches, a method for optimizing zones in a fluid flow system (a semiconductor processing tool used for etching by flow of fluid is simulated to optimize process non-uniformity, [0072] and [0074]), comprising:
running a baseline simulation for the fluid flow system (given the current condition such as current gas flow rate (fluid flow system) and etc. of the semiconductor processing tool, simulation is performed without changing any process parameters, [0044] and [0074]);
running a plurality of sensitivity simulations (by perturbing the input parameters to the current execution of simulations, a set of perturbing solutions (plurality of sensitivity simulation) can be obtained, [0074]), wherein each sensitivity simulation perturbs a flowrate through one of a plurality of pitch circles in the fluid flow system by an offset percentage (when perturbing, input such as gas flow rate through a gas inject plate1 having pitch circles can be varied2 for each simulation run and a sensitivity matrix can be formed based on the set of the perturbing solutions. For each simulation run, the amount varied is the offset percentage [0074] and [0096]);
generating a sensitivity matrix from the plurality of sensitivity simulations (sensitivity matrix formed based on simulation results, [0074], [0094] and [0096]).
Strang does not explicitly teach the details of optimizing an objective function to enable grouping of the plurality of pitch circles into a plurality of zones. However, Strang teaches in [0061] and [0096] that based on simulation results for a gas injection plate, the process condition for gas inject plate (showerhead) can be changed which can include distribution of pitch circles/holes in the gas inject plate or showerhead for correcting process non-uniformity.
Sawlani et al. teaches, optimizing an objective function3 to enable grouping of the plurality of pitch circles into a plurality of zones (to optimize the process condition, different distribution of the holes of the showerhead are simulated to determine process performance and based on satisfactory performance, a certain distribution of holes can be grouped into zones Z1-Z4, [0071]-[0073] and [0142]).
Strang and Sawlani et al. are analogous art because they are from the same field of endeavor that is simulating showerhead/gas inject plate.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the method simulating fluid flow system running baseline simulation and sensitivity simulations by perturbing gas flow rate of the gas inject plate or showerhead as taught by Strang by applying the known technique of determining the optimal distribution of holes/pitch circles in the showerhead through simulation runs as taught by Sawlani et al. to yield predictable results of determining which configuration of showerhead hole distribution would perform best for the given product or process as taught by Sawlani et al. in [0143] and [0145].
Strang teach:
[0044] Once the simulation is executed, the simulation result is used to facilitate a process performed by the semiconductor processing tool 102. As used herein, the term "facilitate a process performed by the processing tool" includes using the simulation result for example to detect a fault in the process, to control the process4, to characterize the process for manufacturing runs, to provide virtual sensor readings relating to the process, or any other use of the simulation result in conjunction with facilitating a process performed by the semiconductor processing tool 102.
[0074] The APC controller 608 is coupled to the simulation module 606 in order to receive a simulation result from the simulation module 606 and to utilize the simulation result to implement a control methodology for process adjustment/correction of a process performed on the tool 602. For example, an adjustment can be made to correct process non-uniformities. In one embodiment of the present invention, one or more perturbation solutions are executed on the simulation module 606, centered on a process solution for a process currently run on the process tool 602. The perturbation solutions can then be utilized with, for instance, a nonlinear optimization scheme such as the method of steepest descent (Numerical Methods, Dahlquist & Bjorck, Prentice-Hall, Inc., Englewood Cliffs, N.J., 1974, p.441; Numerical Recipes, Press et al., Cambridge University Press, Cambridge, 1989, pp. 289-306) to determine a direction within an n-dimensional space for applying the correction. The correction can
then be implemented on the process tool 602 by the APC controller 608. For example, at least one of tool data (i.e. physical sensor data), or results from a current execution of the simulation can indicate that the processing system exhibits a non-uniform static pressure field overlying the given the current initial/boundary conditions5. The non-uniformity can, in turn, contribute to an observed non-uniformity of a metric used to quantify the performance of the process, measured by the metrology tool, on the , i.e. a critical dimension, feature depth, film thickness, etc. By perturbing the input parameters to the current execution of the simulation, a set of perturbation solutions can be obtained in order to determine the best "route" to take in order to remove, or reduce, the static pressure non-uniformity. For example, the input parameters for the process can include a pressure, a power (delivered to an electrode for generating plasma), a gas flow rate, etc. While perturbing one input parameter at a time and holding all other input parameters constant, a sensitivity can be formed6 that may be employed with the above identified optimization scheme to derive a correction suitable for correcting the process non-uniformity.
Sawlani et al. teach:
[0071] During chamber design, different options can be explored (e.g., changing showerhead hole distribution, the gap between the and the showerhead), and the goal is to achieve certain metrics on the wafer, such as having uniform profiles7. The designer develops with a configuration of the chamber (e.g., chamber knobs, geometries) and a recipe, and the system model 402 makes a prediction of the system behavior 408 that includes metrics on the performance of the wafer. The system model 402 can identify not only the performance on the wafer, but also information on what is happening in the chamber (e.g., plasma, temperature being too high or too low).
[0072] If the performance is not satisfactory, the designer can change certain parameters and repeat the process to see the effect, such as by changing one or more variables at a time8 (e.g., pressure, chemistry, timing). But using the ML models, this process is quick (e.g., minutes or hours) instead of having to wait weeks or months for the results. The system model 402 is able to evaluate performance impact when making changes to the hardware or to the process recipe.
[0144] The showerhead model is then used to estimate respective outputs9 on the performance of the showerhead based on a variety of inputs. The outputs may include uniformity measured along the, as well as metrology data obtained from the sensors in the chamber.
Regarding claim 2, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Sawlani et al. teaches, wherein the fluid flow system is a showerhead (the chamber includes a gas showerhead inputting gas into the chamber, [0047]).
Regarding claim 3, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Sawlani et al. teaches, wherein the plurality of pitch circles comprises five or more pitch circles (the showerhead has multiple holes (pitch circles) and can have any number of holes and any distribution of holes that would perform best for the given product, [0047], [0071] and [0145]).
Regarding claim 4, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Sawlani et al. teaches, wherein the plurality of zones comprises three zones (“Further, zones are defined within the showerhead, such as illustrated zones Z1-Z4…”,that is the showerhead has four zones, [0142]).
Regarding claim 5, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Sawlani et al. teaches, wherein the plurality of zones comprises three zones (“Further, zones are defined within the showerhead, such as illustrated zones Z1-Z4…”,[0142], that is the showerhead has four zones and more zones possible depending on design needs as taught in [0145]).
Regarding claim 6, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Strang teaches, wherein the offset percentage is 5% or more (the input such as gas flow rate is varied at each simulation run. Any percentage of gas flow rate can be varied as desired or required by the system to correct process non-uniformity, [0074]).
Regarding claim 7, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Strang teaches, wherein the offset percentage is a positive offset percentage or a negative offset percentage (the gas flow rate is either increased- positive or decreased- negative during simulation runs, [0074]).
Regarding claim 8, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Strang teaches, wherein a simulated flowrate through each hole in the fluid flow system is the same for all holes in the fluid flow system during the baseline simulation (during the first simulation of the current process, no inputs are varied that is gas flow rate is same for all holes. Then after first time/baseline simulation, the input such as gas flow rate for the holes in gas inject plate is varied by a certain amount for each simulation run, [0074] and [0096]).
Regarding claim 10, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Strang teaches, wherein the fluid flow system is part of a plasma chamber (the semiconductor processing tool has a process chamber processing plasma, [0034] and [0074]10).
Regarding claim 17, Strang teaches, a method for optimizing zone grouping in a system with a plurality of inputs (a semiconductor processing tool used for etching by flow of fluid is simulated to optimize process non-uniformity, [0072] and [0074]), comprising:
running a baseline simulation for the fluid flow system (given the current condition such as current gas flow rate (fluid flow system) and etc. of the semiconductor processing tool, simulation is performed without changing any process parameters, [0044] and [0074]), wherein the baseline simulation comprises applying a uniform stimulus to each of the inputs (during baseline simulation, the inputs are not varied, every input is held constant, [0074]);
running a plurality of sensitivity simulations (by perturbing the input parameters to the current execution of simulations, a set of perturbing solutions (plurality of sensitivity simulation) can be obtained, [0074]), wherein each sensitivity simulation perturbs a stimulus to one of the plurality of inputs in the system by an off set percentage (when perturbing, input such as gas flow rate through a gas inject plate11 having pitch circles can be varied12 for each simulation run and a sensitivity matrix can be formed based on the set of the perturbing solutions. For each simulation run, the amount varied is the offset percentage [0074] and [0096]);
generating a sensitivity matrix from the plurality of sensitivity simulations (sensitivity matrix formed based on simulation results, [0074], [0094] and [0096]).
Strang does not explicitly teach the details of optimizing an objective function to enable grouping of the plurality of pitch circles into a plurality of zones. However Strang teaches in [0061] and [0096] that based on simulation results for a gas injection plate, the process condition for gas inject plate (showerhead) can be changed which can include distribution of pitch circles/holes in the gas inject plate or showerhead for correcting process non-uniformity.
Sawlani et al. teaches, optimizing an objective function13 to enable grouping of the plurality of pitch circles into a plurality of zones (to optimize the process condition, different distribution of the holes of the showerhead are simulated to determine process performance and based on satisfactory performance, a certain distribution of holes can be grouped into zones Z1-Z4, [0071]-[0073] and [0144]).
Strang and Sawlani et al. are analogous art because they are from the same field of endeavor that is simulating showerhead/gas inject plate.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the method simulating fluid flow system running baseline simulation and sensitivity simulations by perturbing gas flow rate of the gas inject plate or showerhead as taught by Strang by applying the known technique of determining the optimal distribution of holes/pitch circles in the showerhead through simulation runs as taught by Sawlani et al. to yield predictable results of determining which configuration of showerhead hole distribution would perform best for the given product or process as taught by Sawlani et al. in [0143] and [0145].
Regarding claim 18, combination of Strang and Sawlani et al. teach the method of claim 17. In addition, Strang teaches, wherein the system is a showerhead (the semiconductor processing tool has a gas inject plate with holes, [0074] and [0096]), and wherein the inputs are flowrates through holes in the showerhead (when perturbing, input such as gas flow rate through a gas inject plate14 having pitch circles can be varied15 for each simulation run, [0074] and [0096]).
Regarding claim 20, combination of Strang and Sawlani et al. teach the method of claim 17. In addition, Strang teaches, wherein the system is part of a semiconductor processing tool (process tool implemented as semiconductor processing tool, [0072]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Strang (US 20050071035 A1) in view of over Sawlani et al. (US 20230049157 A1) and Gao (US 20210222552 A1).
Regarding claim 9, combination of Strang and Sawlani et al. teach the method of claim 1. In addition, Sawlani et al. teaches, group the plurality of pitch circles into the plurality of zones (to optimize the process condition, different distribution of the holes of the showerhead are simulated to determine process performance and based on satisfactory performance, a certain distribution of holes can be grouped into zones Z1-Z4, [0071]-[0073] and [0144]).
Neither in combination nor individually Strang and Sawlani et al. teach the details of a clustering algorithm used for grouping. However, Sawlani et al. teaches to determine different distribution of holes of showerhead during each simulation run as taught in [0071] and [0072]. An algorithm must be used by the system to determine the distribution of the holes/pitch circles.
Gao teaches, wherein a clustering algorithm is used to group (a clustering algorithm used to group together wells, [0080]).
Gao is a pertinent art to application, Strang and Sawlani et al. because Gao is trying to solve the same problem as the application that is determining how to group objects such as holes/pitch circles/ wells based on certain criteria.
Therefore it would have been obvious before the effective filing date of the claimed invention to a person of ordinary skill in the art to modify the method for optimizing zones in a fluid flow system where pitch circles are grouped into zones as taught by combination of Strang and Sawlani et al. by applying the known technique of using clustering algorithm for grouping as taught by Gao et al. to yield predictable results of grouping the pitch circles/holes in a showerhead16.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Meidhof (US 20150149125 A1) teaches a perforated plate having five or more pitch circles of nested nozzle bores as taught in Fig.1, [0080] and [0081].
CN51 (CN 103471851 A) teaches a movable pressure distortion simulation plate having holes and the flow through the holes are simulated.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANZUMAN SHARMIN whose telephone number is (571)272-7365. The examiner can normally be reached M and Th 7:00am - 3:00pm and Tue 8:00am-12:00pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, KAMINI SHAH can be reached at (571)272-2279. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANZUMAN SHARMIN/Examiner, Art Unit 2115
/KAMINI S SHAH/Supervisory Patent Examiner, Art Unit 2115
1 It is showerhead with pitch circles/holes.
2 The gas flow rate could for the entire process chamber or for each individual hole flow rate of the gas inject plate/showerhead. Someone one of ordinary skilled in the art can choose from the above identified, predictable solutions of gas flow rate with a reasonable expectation of success to optimize process condition. MPEP.2143.I.(E).
3 The claim does not recite how optimization is performed or under what conditions the groupings of the pitch circles are performed and optimized.
4 Control such as gas flow through gas inject plate as taught in [0096].
5 Pressure is directly related to gas flow. To correct uniformity, the gas flow rate through the gas inject plate is varied to identify the proper gas flow creating uniform pressure across the gas inject plate and the process chamber.
6 Baseline simulation followed by sensitivity simulations to generate a sensitivity matrix.
7 Someone of ordinary skill in the art can modify the concept of correcting non-uniformity of the gas inject plate gas flow rate by applying the known technique of determining the correct distribution of holes of the gas inject plate/shower head to yield predictable results of uniform gas profile.
8 The hole distribution for each zone is varied via simulation until a satisfactory performance is achieved.
9 Uniform gas flow for a certain distribution of holes of the showerhead as taught in [0047].
10 See also [0055] of Sawlani et al. which teaches about plasma simulations for chamber flow simulation.
11 It is showerhead with pitch circles/holes.
12 The gas flow rate could for the entire process chamber or for each individual hole flow rate of the gas inject plate/showerhead. Someone one of ordinary skilled in the art can choose from the above identified, predictable solutions of gas flow rate with a reasonable expectation of success to optimize process condition. MPEP.2143.I.(E).
13 The claim does not recite how optimization is performed or under what conditions the groupings of the pitch circles are performed and optimized.
14 It is showerhead with pitch circles/holes.
15 The gas flow rate could for the entire process chamber or for each individual hole flow rate of the gas inject plate/showerhead. Someone one of ordinary skilled in the art can choose from the above identified, predictable solutions of gas flow rate with a reasonable expectation of success to optimize process condition. MPEP.2143.I.(E).
16 Known work in one field of endeavor may prompt variations of it for use in either the same field or a different one based on design incentives or other market forces if the variations are predictable to one of ordinary skill in the art, MPEP.2143.I.(F).
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