Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “substrate offering device” “fluid applying device” “laser applying device” in claim 17 and “optimum value predicting device” in claim 20.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The structure for performing the claimed function can be found in applicant’s specification:
“substrate offering device” - para. 0020, “workstage” and Fig. 12A work stage
“fluid applying device” – para. 0056, “nozzle” and Fig. 12 1201 also para. 0060
“laser applying device” – para. 0019 and 0059 and Fig. 1A Laser source
“optimum value predicting device” – para. 0089 and ANN 10 trained on a computer using matlab
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Allowable Subject Matter
Claims 10-12 and 14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record does not teach or suggest either alone or in combination the subject matter recited in claims 10-12 and 14. The prior art of record does not teach the limitations directed towards a processing map overlaying step and filtering step.
10. The method as claimed in claim 9, wherein the parameter optimum value predicting step comprises:a model establishing step: establishing a value design model containing a laser value comprising a pulse energy value of the laser and a pulse number value of the laser;a first simulating step: performing a first substrate processing simulation by using N of the laser values in the value design model, thereby obtaining a first simulating result, wherein the N is a positive integer;a first experimenting step: performing a first substrate processing experiment by using a part of the N of the laser value in the value design model,thereby obtaining a first experimenting result;a extracting step: extracting a first value of the parameter from the first simulating result and extracting a second value of the parameter from the first experimenting result;a simulating result validating step: comparing the first value of the parameter with the second value of the parameter so as to validate the simulating result;a model confirming step: confirming whether the value design model is reliable, if not, return to the simulating step for reassessing the value design model; an artificial neural network training step: following the model confirming step if the value design model is reliable, calculating the N of laser values in the value design model by using an artificial intelligence software, so as to train the artificial neural network (ANN) model; a processing map establishing step: establishing a processing map for the parameter by using the artificial neural network (ANN); a processing map overlaying step: overlaying the processing map so as to establish a final processing map; and a processing map filtering step: filtering the final processing map so as to recognize an ideal region on the final processing map, wherein the ideal region comprises the optimum laser value.
11. The method as claimed in claim 10, further comprising: a second simulating step: performing a second substrate processing simulation by using the optimum laser value, so as to generate a second simulating result; and a simulating result drawing step: analyzing and drawing the second simulating result.
12. The method as claimed in claim 10, further comprising: a second experimenting step: performing a second substrate processing experiment by using the optimum laser value, so as to generate a second experimenting result; and a SEM analyzing step: analyzing a substrate structure of the second experimenting result by using a scanning electron microscope (SEM).
14. The method as claimed in claim 13, wherein the parameter optimum value predicting step comprises:a model establishing step: establishing a value design model containing a laser value comprising a pulse energy value of the laser and a pulse number value of the laser;a first simulating step: performing a first substrate processing simulation by using N of the laser value in the value design model, and thereby obtaining a first simulating result, wherein the N is positive integer;a first experimenting step: performing a first substrate processing experiment by using a part of the N of the laser value in the value design model,thereby obtaining a first experimenting result;an extracting step: extracting a first value of the parameter from the first simulating result and extracting a second value of the parameter from the first experimenting result;a simulating result validating step: comparing the first value of the parameter with the second value of the parameter so as to validate the simulating result;a model confirming step: confirming whether the value design model is available, if not, get back to the simulating step for reassessing the value design model;an artificial neural network training step: following the model confirming step if the value design model is reliable, calculating the N of laser values in the value design model by using an artificial intelligence software, so as to train the artificial neural network (ANN) model; a processing map establishing step: establishing a processing map for the parameter by using the artificial neural network (ANN); a processing map overlaying step: overlaying the processing map so as to establish a final processing map; and a processing map filtering step: filtering the final processing map so as to recognize an ideal region on the final processing map, wherein the ideal region comprises the optimum laser value.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4, 6, 9, 13, 15, and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Goeckertiz US 2020/0300787 A1.
Goeckertiz teaches:
1. A method for processing substrate by using laser, comprising:
a substrate providing step: providing a substrate; [substrate 103]
a fluid applying step: applying a fluid on the substrate; [Fig. 3 302 – dispense etchant into vessel and onto substrate surface] and
a laser applying step: applying a laser through the fluid and performing a laser processing on the substrate so as to obtain a processed substrate, [Fig. 3 305 – generate radiation using etch-assist radiation emitter(s) and para. 0070-0072, emitters can be lasers]
wherein the laser processing comprises laser drilling, laser cutting, laser grooving, laser trimming, laser trenching, or any combination thereof. [para. 0039, “The absorbed radiation is converted to heat thereby raising the local temperature of the etchant 104 and increasing the etch rate of the substrate 103. In some embodiments, nanoparticles that are highly absorptive of the radiation may be dispersed in the etchant to increase the conversion of electromagnetic energy to thermal energy.”]
Goeckertiz teaches:
2. The method as claimed in claim 1, wherein the substrate comprises silicon, silicon carbide, gallium nitride, gallium arsenide, aluminum nitride, or any combination thereof. [para. 0028, “The substrate 103 may be any number of solid materials such as glass, ceramic, semiconductor, crystal or rigid polymer.”]
Goeckertiz teaches:
3. The method as claimed in claim 1, wherein the fluid comprises fluid membrane. [para. 0029, “The depicted etchant 104 is a fluid such as a liquid, gas or plasma.” And Figs 1A-1C show fluid 104 with membrane at surface]
Goeckertiz teaches:
4. The method as claimed in claim 3, wherein the fluid comprises gas, liquid, or a combination thereof. [para. 0029, “The depicted etchant 104 is a fluid such as a liquid, gas or plasma.”]
Goeckertiz teaches:
6. The method as claimed in claim 4, wherein the liquid contains nanomaterial, comprising: nanotube, nanoplatelet, nanoribbon, nanowire, nanofiber, or any combination thereof. [para. 0039, “In some embodiments, nanoparticles that are highly absorptive of the radiation may be dispersed in the etchant to increase the conversion of electromagnetic energy to thermal energy.”]
Goeckertiz teaches:
9. The method as claimed in claim 1, further comprising: a parameter optimum value predicting step: before the fluid applying step, predicting an optimum value of a parameter by using an artificial neural network (ANN) model, wherein the optimum value of the parameter corresponds to an optimum laser value of the laser, and the parameter relates to the processed substrate. [para. 0056, “In some embodiments, machine learning may be used to improve the etching process. For example, an artificial neural network using reinforcement learning may be used to dynamically adjust the inputs to the etch process (e.g. etch-assist radiation power and emitter location) in response to positive or negative reinforcement (i.e. a reward). The strength of the reinforcement is based on the difference between the calculated optical properties of the optical element under fabrication and user-specified optical properties of the optical element where actions that minimize the difference result in a positive reward.”]
Goeckertiz teaches:
13. A method for processing substrate by using laser, comprising:
a substrate providing step: providing a substrate; [para. 0057, “The method of FIG. 3 commences with the first step 301 of placing a substrate in contact with a vessel wall and sealing the substrate surface against the vessel wall thereby forming a vessel for containing an etchant.”]
a parameter optimum value predicting step: predicting an optimum value of a parameter of the substrate by using an artificial neural network (ANN) model, thereby obtaining an optimum laser value; [para. 0056, “In some embodiments, machine learning may be used to improve the etching process. For example, an artificial neural network using reinforcement learning may be used to dynamically adjust the inputs to the etch process (e.g. etch-assist radiation power and emitter location) in response to positive or negative reinforcement (i.e. a reward). The strength of the reinforcement is based on the difference between the calculated optical properties of the optical element under fabrication and user-specified optical properties of the optical element where actions that minimize the difference result in a positive reward.”] and
a laser applying step: applying a laser and performing a laser processing on the substrate according to the optimum laser value; [para. 0058, “In step 306, the radiation power of select emitters is adjusted to selectively heat the etchant and form a target etch shape in the substrate.”]
wherein the laser processing comprises laser drilling, laser cutting, laser grooving, laser trimming or laser trenching. [para. 0077, “Focused radiation incident on the etchant 104 has a higher power density and increases the etch rate of the substrate 103 more than defocused radiation.”]
Goeckertiz teaches:
15. The method as claimed in claim 13, further comprising:
a fluid applying step: applying a fluid on the substrate before the laser applying step, [Fig. 3 302]
wherein at the laser applying step, the laser is applied for performing the laser processing on the substrate through the fluid. [Fig. 3 305]
Goeckertiz teaches:
17. A system for processing substrate by using laser, comprising:
a substrate offering device for offering a substrate; [Fig. 1A 101]
a fluid applying device, which is connected to the substrate offering device and is used for applying a fluid on the substrate; [etchant 104 injected into vessel through inlet port 105 and outlet port 106] and
a laser applying device, which is connected to the fluid applying device and is used for applying a laser through the fluid and performing a laser processing comprising laser drilling, laser cutting, laser grooving, laser trimming, laser trenching, or any combination thereof. [para. 0036, “The etch-assist emitters 109 may comprise any number of radiation sources such as lasers, LEDs, masers, gyrotrons, backward wave oscillators, or radio elements.”]
Goeckertiz teaches:
18. The system as claimed in claim 16, wherein the substrate offering device comprises a laser work stage used for placing the substrate. [Fig. 1A vessel 101 holds substrate 103]
Goeckertiz teaches:
19. The system as claimed in claim 17, wherein the fluid applying device comprises a nozzle, which is arranged on the laser work stage and is used for spraying water mist, compressed air, or carbon material containing fluid on the substrate. [para. 0029, inlet port]
Goeckertiz teaches:
20. The system as claimed in claim 16, further comprising: an optimum value predicting device, which is connected to the laser applying device, and is used for predicting an optimum value of a parameter by using an artificial neural network (ANN) model. [para. 0056, “In some embodiments, machine learning may be used to improve the etching process. For example, an artificial neural network using reinforcement learning may be used to dynamically adjust the inputs to the etch process (e.g. etch-assist radiation power and emitter location) in response to positive or negative reinforcement (i.e. a reward). The strength of the reinforcement is based on the difference between the calculated optical properties of the optical element under fabrication and user-specified optical properties of the optical element where actions that minimize the difference result in a positive reward.”]
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) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Goeckertiz US 2020/0300787 A1 in view of Scaggs US 2003/0062126 A1.
Goeckertiz does not teach the following limitation, however, Scaggs teaches:
5. The method as claimed in claim 4, wherein the gas comprises air or compressed air. [para. 0012, “Similar to the optimum absorptive properties of the fluid, it is preferred that the propellant is substantially transmissive relative to light emitted from the laser beam. The propellant is preferably an inert gas including, but not limited to, air, nitrogen, helium, argon, carbon dioxide or the like.”]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of Scaggs with those of Goeckertiz. A person having ordinary skill in the art would have been motivated to combine the teachings because Scaggs teaches that fluids that are “transmissive relative to light” are preferred. (See para. 0012).
Claim(s) 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Goeckertiz US 2020/0300787 A1 in view of Tan et al. US 2007/0268491 A1.
Goeckertiz does not teach the following limitation, however, Tan teaches:
7. The method as claimed in claim 6, wherein the nanomaterial comprises carbon- based nanomaterial, comprising: carbon nanomaterial, graphite nanomaterial, graphene nanomaterial, fullerene nanomaterial, or any combination thereof. [para. 0023, “The fluid 154 includes multiple layers or channels of different fluids. It may be contained in a fluidic channel or container made by glass or a polymeric material. The fluid 154 includes a number of CNTs 155.sub.1 to 155.sub.N. The CNTs may be single-walled CNTs (SWNTs) or multi-walled CNTs (MWNTs). The CNTs may be functionalized. The fluid 154 is placed between the polarizer 152 and the objective 160 to allow observation and monitoring the location or movement of the CNTs 155.sub.1 to 155.sub.N.”]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of Tan with those of Goeckertiz. A person having ordinary skill in the art would have been motivated to combine the teachings because Tan teaches that carbon nanotubes (CNTs) can be used with lasers to make feedback observations in real time, increasing accuracy. (See para. 0015).
Tan teaches:
16. The method as claimed in claim 15, wherein the fluid comprises a carbon material containing nanofluid comprising: carbon nanotube (CNT) nanofluid, graphite nanoplatelet nanofluid, graphene nanoplatelet nanofluid, fullerene nanofluid, carbon nanoribbon nanofluid, carbon nanowire nanofluid, carbon nano fiber nanofluid, or any combination thereof. [CNTs 155]
Claim(s) 8 is rejected under 35 U.S.C. 103 as being unpatentable over Goeckertiz US 2020/0300787 A1 in view of Jones US 2012/0280430 A1.
Goeckertiz does not teach the following limitation, however, Jones teaches:
8. The method as claimed in claim 6, wherein the volume fraction of the nanomaterial in the liquid ranges from 0.5-3.0 vol%. [para. 0089, teaches CNT can be dissolved in water solvent from 50% to .0001%]
It would have been obvious to a person having ordinary skill in the art before the time of filing to combine the teachings of Jones with those of Goeckertiz. A person having ordinary skill in the art would have been motivated to combine the teachings because Jones teaches the concentration range can be used in laser cutting using tooling techniques. (See para. 0050).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GARY COLLINS whose telephone number is (571)270-0473. The examiner can normally be reached Monday - Friday 1-930PM EST.
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/GARY COLLINS/Primary Examiner, Art Unit 2115
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