DETAILED ACTION
This Office action is in response to the amendment filed on June 2nd, 2026. Claims 1-8 and 17-20 are pending.
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 Rejections - 35 USC § 112(d)
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 2-3 are rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The claims are directed to a method of predicting a set of control parameters with a model, but the limitations relate to the datasets used to train the model. The claimed method proceeds identically whether the datasets used to train the model are the ones recited in claims 2 & 3 or some different training data. Hence, the claimed method is not further limited. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
Claim 18 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. The claims are directed to an ion implanter including circuitry with instructions to predict a set of control parameters using a model, but the limitation relates to the datasets used to train the model. The ion implanter, including the processing circuitry and instructions, are identical regardless of what data was used to train the control model. Hence the claimed ion implanter is not further limited. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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.
Claim(s) 17-18 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2022/0301817 (Takemura).
Regarding claim 17, Takemura et al. discloses an ion implanter, comprising:
an ion source to generate an ion beam (fig. 1, element 2);
at least one beamline component to direct the ion beam towards a substrate (fig. 1, elements 3-7);
a processing circuitry (fig. 1, elements 8-9); and
a memory communicatively coupled to the processing circuitry (“The control device 8 is a computer comprising at least one CPU, a memory, a display, and input means,” P 48), the memory storing instructions that, when executed by the processing circuitry, causes the processing circuitry to:
receive a set of process parameters and associated values for the ion implanter by an inverted control model (“when the CPU and its peripherals are cooperatively operated according to control program code stored in the memory, execute the control program code to function as a recipe acceptance part 81,” P 48), the inverted control model comprising an artificial neural network (ANN) (“The machine learning part 92 is a function brought out by the aforementioned artificial intelligence feature, and is configured to update the above learning algorithm, based on supervised learning, unsupervised learning, reinforcement learning or deep learning, etc.” P 77, where it is understood that deep learning requires a neural network); and
predict, by the inverted control model, a set of control parameters and associated values for the ion implanter based on the set of process parameters and associated values (“The program code when executed by a computer, such as a central processing unit (CPU) or a microprocessor, may perform the functions of a control device to input at least a processing condition during new processing and a monitored value indicative of a state of at least one of the modules during processing just before the new processing to a trained machine learning algorithm and receive as an output from the trained machine learning algorithm an initial value of each basic operation parameter for controlling an operation of a respective one of the modules,” P 40).
Regarding claim 18, the claim does not further limit the ion implanter, and is therefore rejected on the same grounds as claim 17.
Regarding claim 20, Takemura discloses the ion implanter of claim 17, wherein the processing circuitry is further caused to configure the at least one beamline component of the ion implanter based on the set of control parameters (“input the selected initial value to the respective one of the modules,” P 37).
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) 1-7 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0301817 (Takemura).
Regarding claim 1, Takemura et al. discloses a method, comprising:
receiving a set of process parameters and associated values for an ion implanter by an inverted control model (“The recipe includes a variety of information indicative of the quality of the ion beam IB generated by the ion beam irradiation apparatus 100 such as an ion species of dopant ions included in the ion beam IB, a beam energy of the ion beam IB, and/or a beam current of the ion beam IB.” P 50), the inverted control model comprising an artificial neural network (ANN) (“The machine learning part 92 is a function brought out by the aforementioned artificial intelligence feature, and is configured to update the above learning algorithm, based on supervised learning, unsupervised learning, reinforcement learning or deep learning, etc.” P 77, where it is understood that deep learning requires a neural network);
predicting, by the inverted control model, a set of control parameters and associated values for the ion implanter based on the set of process parameters and associated values (“The program code when executed by a computer, such as a central processing unit (CPU) or a microprocessor, may perform the functions of a control device to input at least a processing condition during new processing and a monitored value indicative of a state of at least one of the modules during processing just before the new processing to a trained machine learning algorithm and receive as an output from the trained machine learning algorithm an initial value of each basic operation parameter for controlling an operation of a respective one of the modules,” P 40);
configuring a component of the ion implanter based on the set of control parameters (“input the selected initial value to the respective one of the modules,” P 37).
Takemura does not disclose presenting the set of control parameters and associated values on a graphical user interface (GUI) of an electronic display. Takemura does disclose an electronic display (“The machine learning device 9 is a computer comprising at least one CPU, a memory, a display, input means, such as a keyboard, mouse, trackpad, touch screen display, etc., and an artificial intelligence feature.” P 68) and presenting values on such a display screen in a GUI is well known in the art. It would have been obvious to a person having ordinary skill in the art to display the values in such a GUI so that the user could view them.
Regarding claims 2-3, these claims do not further limit the method, and are therefore rejected on the same grounds as claim 1.
Regarding claim 4, Takemura et al. discloses the method of claim 1, wherein each control parameter corresponds to a hardware or software setting that controls a configuration or operation of a component of the ion implanter (“Here, the basic parameter refers to a setting item used to control an operation of the respective one of the modules M, and is preliminarily set with respect to the respective one of the modules M.” P 51).
Regarding claim 5, Takemura et al. discloses the method of claim 1, wherein each control parameter comprises a first parameter affecting an energy of an ion beam generated by the ion implanter, an acceleration or deceleration voltage parameter, a dopant gas flow rate, a diluent gas flow rate, an ion source parameter, an analyzer parameter, a focus parameter, a scan parameter, a quadrupole lens current, or a post-acceleration voltage (“Further, examples of the basic parameter set for the ion source system-module may include a flow rate of gas to be supplied to the plasma chamber; a supply current to be supplied to the source magnet; and/or an arc current.” P 54 and “Further, examples of the basic parameter set for the beam line electromagnetic field system-module may include a magnetic flux density of the mass separation magnet 3, a voltage to be applied to the acceleration tube 4, a magnetic flux density of the energy separation magnet 5, and/or a magnetic flux density of the beam parallelizing magnet 7.” P 58).
Regarding claim 6, Takemura et al. discloses the method of claim 1, wherein each process parameter corresponds to a metric associated with a beam property for an ion beam generated by the ion implanter (“The recipe includes a variety of information indicative of the quality of the ion beam TB generated by the ion beam irradiation apparatus 100 such as an ion species of dopant ions included in the ion beam TB, the beam energy of the ion beam TB, and/or the beam current of the ion beam IB.” P 50).
Regarding claim 7, Takemura et al. discloses the method of claim 1, wherein each process parameter comprises a beam height, a beam width, an energy parameter, a region of interest (ROI) current parameter, or a uniformity parameter (“The recipe includes a variety of information indicative of the quality of the ion beam TB generated by the ion beam irradiation apparatus 100 such as an ion species of dopant ions included in the ion beam TB, the beam energy of the ion beam TB, and/or the beam current of the ion beam IB.” P 50).
Claim(s) 8 & 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takemura et al. as applied to claims 1 & 17 above, and further in view of https://www.v7labs.com/blog/neural-networks-activation-functions (Baheti).
Regarding claims 8 & 19, Takemura et al. discloses the claimed invention, wherein the inverted control model comprises a regression neural network comprising one input layer, one or more hidden layers, and an output layer, where each neuron in the one or more hidden layers performs computations on input data using an activation function to generate an output value (inherent in a deep learning neural network).
Takemura et al. does not disclose whether the activation function generates a continuous output value or whether the activation function comprising a rectified linear unit (ReLU) function, a leaky ReLU function, or a parametric ReLU function. Baheti discloses that each of these activation functions is known and generates a continuous output value (see sections titled ReLU function, Leaky ReLU, and Parametric ReLU function). It would have been obvious to a person having ordinary skill in the art to use a rectified linear unit (ReLU) function for the unspecified activation function of Takemura because it is computationally efficient, as disclosed by Baheti (“Since only a certain number of neurons are activated, the ReLU function is far more computationally efficient when compared to the sigmoid and tanh functions.”). It would have been obvious to use a leaky ReLU function instead because this solves the dying ReLU problem, as disclosed by Baheti (“Leaky ReLU is an improved version of ReLU function to solve the Dying ReLU problem as it has a small positive slope in the negative area.”). Finally, it would have been obvious to use a parametric ReLU function to solve the problem of dead neurons, as disclosed by Baheti (“The parameterized ReLU function is used when the leaky ReLU function still fails at solving the problem of dead neurons, and the relevant information is not successfully passed to the next layer.”).
Response to Arguments
Applicant's arguments filed June 2nd, 2026 have been fully considered but they are not persuasive.
Applicant argues that Takemura fails to disclose an inverted control model that predicts a set of control parameters from a set of process parameters. In particular, they argue that Takemura teaches a fundamentally different, conventional forward model. They note that that Takemura’s system is designed to optimize a process by predicting better initial control values based on the machine’s current state. As evidence for this they point to figure 6, which shows that the input data includes a recipe and the previous machine operation state.
The model of Takemura does take the previous machine operation state as one of the inputs, but the other input – the recipe – includes a set of process parameters (see “The recipe includes a variety of information indicative of the quality of the ion beam IB generated by the ion beam irradiation apparatus 100 such as an ion species of dopant ions included in the ion beam IB, a beam energy of the ion beam IB, and/or a beam current of the ion beam IB.” P 50, see also “Examples of the explanatory variable may include … a beam current amplitude of the ion beam; a beam angle of the ion beam; and/or a beam current density of the ion beam.” P 34). The model then predicts an initial value of a control parameter from the process parameter using an inverted control model. Takemura does include an optional forward prediction process to optimize the initial value, but this is after the initial parameter is determined (see “For example, in some embodiments, the control device 8 of the ion beam irradiation apparatus 100 may simply access the trained machine learning algorithm that is stored in the machine learning part 9 and use the trained machine learning algorithm to predict the initial value.” P 117).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ELIZA W OSENBAUGH-STEWART whose telephone number is (571)270-5782. The examiner can normally be reached 10am - 6pm Pacific Time M-F.
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/ELIZA W OSENBAUGH-STEWART/Primary Examiner, Art Unit 2881
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