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 .
This action is in response to the amendments filed 1/26/2026.
Claims 1-5 and 7-13 remain rejected in the application.
Note that Aniss Chad is the new examiner of record on this application.
Response to Amendment
The amendment to the claims has overcome the previous 35 U.S.C. § 112(f) interpretation and the previous rejection under 35 U.S.C. § 112(b).
Response to Arguments
Applicant primarily argues that the cited references including NPL_ 2018-Cochrane does not teach or suggest the features of amended Claim 1, including wherein a state of the plurality of droplets is determined for each computational element based on a volume of a space between the first member and the second member and a volume of the plurality of droplets of the curable composition in the computational element.
Applicant’s argument has been fully considered but is not found persuasive. The Examiner respectfully disagrees for the reasons set forth below and in the attached interview summary.
The claimed feature requires determining a state of the droplets based on (i) a volume of the space between the two members and (ii) a volume of the droplets of curable composition in the computational element. Contrary to Applicant’s assertion, Cochrane expressly discloses precisely this determination. Cochrane defines the fundamental state variable of its model — the saturation, S — as the ratio of the volume of the drops to the volume of the gap. Specifically, Cochrane states at Section 3.1 (Diffuse drops): “A patch of ink-jetted drops is represented by a saturation field with a value of the total volume of drops divided by the total gap volume.”
The Cochrane disclosure directly maps to the disputed claim limitation:
Claim limitation “a volume of the plurality of droplets of the curable composition in the computational element” corresponds to “total volume of drops”
Claim limitation “a volume of a space between the first member and the second member” corresponds to “total gap volume” (i.e., the gap between substrate and template)
Claim limitation “a state of the plurality of droplets is determined … based on” the above two volumes corresponds to “saturation field with a value of [drops volume] / [gap volume]”
Accordingly, the saturation value in Cochrane is a quantity determined based on the volume of the droplets and the volume of the space (gap) between the substrate (first member) and template (second member). Applicant’s argument does not address, distinguish, or even acknowledge this express definition of saturation.
Applicant appears to argue that Cochrane does not use these volumes to determine a state of the droplets. This argument is not persuasive because Cochrane repeatedly and expressly uses the saturation value to determine the state (and correspondingly the governing model) of the curable composition throughout the computational domain.
First, Cochrane teaches that the saturation S and the gas volume fraction Sg together define the phase composition of the imprint volume. See Section 2.2 (Mass balance). Second, Cochrane teaches that the saturation value determines transitions between distinct flow states — specifically, transitions between “free flow” and “no flow” — through the relative permeability model. See Section 2.3 (Relative permeability). Third, Cochrane teaches a computational switch that substitutes the governing equation depending upon the saturation-defined state of the material in a given region — e.g., whether the region is gas-containing or gas-devoid, or whether saturation has reached/exceeded unity. See Section 2.5 (Numerical implementation details): “In regions devoid of gas, Eq. (26) as written serves no purpose, and so is replaced with one that promotes monotonicity… When S ≥ 1, the advection and dissolution terms go to zero…”
The saturation field is defined and evaluated across the finite-element discretization of the domain. See Section 2.5 (“discretized in space with the finite element method”). Because saturation is defined per region of the discretized domain and represents an aggregation of a plurality of droplets, Cochrane teaches or at least suggests determining the state (via saturation) for each computational element where a plurality of droplets fall within the represented region/element.
For the foregoing reasons:
Cochrane discloses determining a state variable (saturation) based on the volume of the droplets of curable composition and the volume of the gap (space) between the substrate and template, i.e., “total volume of drops divided by the total gap volume” (Section 3.1); Cochrane uses that saturation-defined value to determine the state of the curable composition and to select the governing model/equation (Sections 2.2, 2.3, 2.5); and the combination of Jain, Taylor, and Cochrane teaches the argued feature.
Applicant’s argument is therefore not persuasive, and the rejection of Claim 1 under 35 U.S.C. § 103 is maintained.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-5 and 7-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Defectivity prediction for droplet-dispensed UV nanoimprint lithography, enabled by fast simulation of resin flow at feature, droplet, and template scales” (2016-Taylor) in view of “Simulation of UV Nanoimprint Lithography on Rigid and Flexible Substrates” (2016-Jain) in further view of "Multiphase model for nanoimprint lithography" (2018-Cochrane)
With respect to claim 1, 2016-Taylor teaches A method of imprinting (modeling and simulation to select process parameters, [page 2 paragraph 1 line 1], process parameters referring to the physical process of FIG. 1. Droplet-dispensed nanoimprint lithography process, [page 2]) using a film forming apparatus (the entire apparatus shown in FIG. 1, [page 2]) including a mold, (patterned quartz template [page 2 paragraph 4 line 1]) a dispenser, (inkjet, [page 2, see caption in FIG. 1]) a substrate (wafer substrate, [page 2, see caption in FIG. 1]) and a control unit(system that senses threshold value, and switches to constant-load phase, [page 2 paragraph 4 line 4]), and a computing apparatus including a processor connected to a memory, the processor being configured for predicting a behavior of a curable composition (standard personal computer, [page 10 paragraph 1 lines 1-2], which inherently includes a processor; running a simulation with memory requirements, [page 10 paragraph 1 line 6]; to enable detailed studies of droplet-feature interactions within full-field imprinting process, [page 10 paragraph 1 lines 8-9]; where the droplets are referring to a UV-curing resin, [page 1 paragraph 1 line 4]), defining, by the computing apparatus, a computational grid formed by a plurality of computational elements, the defining being based on a plurality of droplets of the curable composition falling within one computational element (Previous work on simulating droplet spreading has been droplets constrained by computational requirements to only a few features or droplets; in contrast, our abstracted approach does not need to solve for the behavior of each individual droplet and so can readily simulate the spreading of >10^4 droplets, [page 2 paragraph 4 lines 7-9]; a spatial discretization is used that is larger than both the droplets and the nanoscale features. In this highest-level simulation, each individual spatial element, or “pixel”, also has associated with it a characteristic volume and spacing of droplets, [page 3 paragraph 2 line 6-8]; example of Full-field simulations of the imprinting of 30 mm × 40 mm pattern on a 1 mm grid take ~5 s to run on a standard personal computer, [page 10 paragraph 1 lines 1-2]); obtaining, but the computing apparatus, the behavior of the curable composition in each computational element in accordance with a model corresponding to a state of the curable composition in each computational element(In this highest-level simulation, each individual spatial element, or “pixel”, also has associated with it a characteristic volume and spacing of droplets, as well as a characteristic imprinted feature size, shape and density. The results of the characterization simulations done at the smaller two length scales are encapsulated in parametric models that feed into the top-level, full-field simulation, so that as the template sinks into the resin droplet array, both the residual layer thickness and the degree of template cavity filling can be tracked as functions of position and time, [page 3 paragraph 2 lines 7-11], where the state is the "position and time", and the behavior is " residual layer thickness and the degree of template cavity filling"; example of Full-field simulations of the imprinting of 30 mm × 40 mm pattern on a 1 mm grid take ~5 s to run on a standard personal computer, [page 10 paragraph 1 lines 1-2]); dispensing, with the dispenser, a plurality of droplets of the curable composition ... and the substrate, based on the obtained behavior of the curable composition (Droplets with volume ~ 1 pL are initially inkjet-dispensed on to the wafer substrate in a pre-determined pattern tailored to the areal density of cavities on the template, [page 2 FIG. 1 caption]; where Modeling and simulation can help in several ways to select process parameters for imprint lithography processes. Primarily, the objective of simulation is to select a combination of applied template loads and displacements and loading durations that enable complete cavity filling without unnecessarily prolonging processing time, [page 2 paragraph 2 lines 1-3]; example given in 9b and 9c, [page 9] where In Figure 9b/c we show a comparison of imprinting simulation results for two different approaches to droplet placement: in Figure 9b, droplets are dispensed on to the substrate in a uniform square array; in Figure 9c, they are matched in density to the distribution of cavities on the template, [page 8 paragraph 3 lines 1-3]); and causing, by the control unit, the plurality of droplets on the model and the substrate to come into contact with each other to form a film of the curable composition (When the contact load is sensed to have reached a threshold value (a few Newtons to tens of Newtons), the system switches to a constant-load phase during which the curvature of the template is relaxed to zero at a constant rate over a period of approximately one second. There is then a holding phase, which may occur at lower load to enable RLT to homogenize by minimizing elastic deflections of the template and wafer, [page 2 paragraph 4 lines 3-6]).
2016-Taylor does not teach a substrate holder for supporting or dispensing ... on the mold; or wherein a state of the plurality of droplets is determined for each computational element based on a volume of a space between the first member and the second member and a volume of the curable composition in the computational element.
However, Jain teaches a substrate holder for supporting (rollers shown as circles in FIG. 1.7 of the R2RNIL [page 8]; same diagram shown in FIG. 5.1, [page 137], with a picture of the physical machine focused on the rollers shown in FIG. 5.2(a), [page 138]) and dispensing ... on the mold (FIG. 1.11(a) shows how drops may be delivered on both the mold and substrate, [page 18]).
Jain does not teach wherein a state of the plurality of droplets is determined for each computational element based on a volume of a space between the first member and the second member and a volume of the plurality of droplets of the curable composition in the computational element.
However, Cochrane teaches wherein a state of the plurality of droplets is determined for each computational element based on a volume of a space between the first member and the second member and volume of the plurality of droplets of the curable composition in the computational element (see eq. (18), [page 13]; a state of the plurality of droplets is determined for each computational element = S, volume of a space between the first member and the second member = V_t, a volume of the curable composition in the computational element = V_d; FIG. 3 shows that h, which can be used to derive V_t is referring to the gap between the two members, [page 11]).
It would have been obvious to one skilled in the art before the effective filing date to combine Taylor with Jain because this is combining prior art elements according to known methods to yield predictable results. Taylor discloses a system and method that teaches all of the claimed features except for the holder and dispensing droplets on the mold as claimed. Jain discloses various fully functioning UV NIL devices, which include a holder and a mold (see 2016-Jain FIGS 1.1-1.16, [pages 1-24]). Because Taylor focuses more on the simulation aspects, a person having skill in the art could apply the simulation of Taylor to anyone of the devices physical devices disclosed in Jain. The elements such as the inkjet, the mold, and the rollers function the same way when they are combined. A person having skill in the art would have recognized the results as predictable if the simulation of 2016-Taylor was combined with one or more of the physical devices in 2016-Jain. Therefore, it would have been obvious to combine 2016-Taylor with 2016-Jain to a person having ordinary skill in the art.
It would have been obvious to one skilled in the art before the effective filing date to combine Taylor in view of Jain with Cochrane because a teaching, suggestion, or motivation in the prior art would have led one skilled in the art to combine prior art teaching to arrive at the claimed invention. Taylor in view of Jain discloses a system and method that teaches all of the claimed features except for gas entrapment at the scale of multiple droplets per cell, “One phenomenon that we do not model in this work is any form of gas entrapment”, (2016-Taylor [page 9 paragraph 1]). Cochrane teaches how to overcome this issue by assuming including a uniformity in the geometry of the model:
While useful process understanding has been gained from these works, no approach accounts for the effects of gas trapping and pressure-driven gas dissolution with a continuum model over practical processing areas of O(10cm^2).
(Cochrane [page 10 col 2 paragraph 3 lines 1-4]).
The formulation is developed in Section 2 utilizing multiphase concepts of saturation and relative permeability augmented by a reduced-order gas dissolution model.
(Cochrane [page 10 col 2 paragraph 3 lines 12-14]). Where section 2 shows the model geometrically in FIG. 6:
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(Cochrane [page 14]).
A person having skill in the art would have a reasonable expectation of modeling gas entrapment in the system and method of Taylor in view of Jain by modifying Taylor in view of Jain with the geometric model and saturation equation of Cochrane. Therefore, it would have been obvious to combine Taylor in view of Jain with Cochrane to a person having ordinary skill in the art, and this claim is rejected under 35 U.S.C. 103.
With respect to claim 2, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 1, as noted above. 2016-Taylor further teaches wherein the state includes a non-connected state in which the plurality of droplets of the curable composition in the computational element are not connected to each other, and a connected state in which the plurality of droplets of the curable composition in the computational element are connected to each other, and the model includes a non-connected state model corresponding to the non-connected state and a connected state model corresponding to the connected state (“To simulate droplet-dispensed imprinting of real product designs it is essential to account for the merging of multiple droplets before template filling occurs. We have combined an analytical solution for the squeezing of disks of viscous resist with a numerical model that captures the flow of resist once droplets have touched each other but before the whole volume beneath the template is filled. The mechanical work needed to deform the resist comes from the externally applied template load and from the surface tension that acts between the spreading edges of resist droplets” [page 6 paragraph 5 lines 1-5]; Examiner’s note - the analytical model represents the non-connected state; and the numerical model that captures the flow of resist “once the droplets have touched each other” represents the connected state).
With respect to claim 3, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 2, as noted above. 2016-Taylor further teaches wherein the non-connected state model is a model having, as variables, a characteristic of a droplet representing the plurality of droplets (the analytical model that includes surface tension of the droplet calculated as virtual pressure, [page 6 paragraph 5 line 5]-[page 7 paragraph 1 line 6]; see Specification [0089] for interpretation of “characteristic … of a droplet”) of the curable composition in the computational element and the number of the plurality of droplets (“The results of the characterization simulations done at the smaller two length scales are encapsulated in parametric models that feed into the top-level, full-field simulation”, [page 3 paragraph 2 lines 9-10]; meaning once the pressure and RLT is calculated for the single droplet scale, it is applied to each droplet in the larger template scale that contains the number of droplets), and the connected state model is a model having, as a variable, a characteristic of a connected body formed when the plurality of droplets of the curable composition in the computational element are connected to each other (numerical model that captures flow of resist, [page 6 paragraph 5 line 3]).
With respect to claim 4, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 3, as noted above. 2016-Taylor further teaches wherein the non-connected state model and the connected state model are models that decide a force to be applied to the second member by the curable composition in the computational element (These load response functions capture plate-like bending of the template as well as local elastic deflections of the surfaces of the template and substrate. The response function used is detailed in our earlier work 7, 8. At each time-step, the convolution of the instantaneous pressure solution with these load response functions is used to estimate the deformed shape of the solid surfaces bounding the residual layer of resin. Simulation of the resin’s viscous flow and the template’s and substrate’s elastic deformations is coupled within each solution step, as described in our previous work, [page 4 paragraph 1 lines 2-7]; Note the pressure is calculated from the virtual pressure as being superimposed on the actual pressures arising from the externally applied template loads, [page 7 paragraph 1 lines 3-4]).
With respect to claim 5, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 3, as noted above. 2016-Taylor does not teach wherein the connected state model includes a plurality of models corresponding to stages in which a film is formed by the plurality of droplets of the curable composition in the computational element.
However, Jain teaches wherein the connected state model includes a plurality of models corresponding to stages in which a film is formed by the plurality of droplets of the curable composition in the computational element (FIG. 4.16(a) is a model that relates dimensionless gap height (i.e. the state) to fraction of total substrate area for each of the labeled phases, [page 125]; defects are model by eq. (3.25), [page 95], where the gaps are modeled as cylinders; when connected, the model is in the second state; when the gaps model as cylinders connect in the x-direction, they form a channel defect as labeled in FIG. 4.16(a); and when the gaps don't connect, they are called pocket defects as labeled in FIG. 4.16(a), [page 125]).
It would have been obvious to one skilled in the art before the effective filing date to combine Taylor with Jain because a teaching, suggestion, or motivation in the prior art would have led one skilled in the art to combine prior art teaching to arrive at the claimed invention. Taylor discloses a system and method that teaches all of the claimed features except for the connected state model includes a plurality of models corresponding to stages. Taylor does not discuss the separate states that lead to gas entrapment because of an assumption, but recognizes the need in the absence of that assumption: “We do however realize that achieving a short enough spreading time may necessitate accepting some gas entrapment and dissolution, and that there may be many pattern–droplet interactions that will lead to gas entrapment at whatever rate template curvature is relaxed”, (Taylor [page 9 paragraph 1 lines 4-6]). 2016-Jain goes into great detail to study NIL, and in particular the defects (2016-Jain [Abstract] paragraph 2). Jain classifies two types of connected defects, pocket and channel, and then provides a model to predict the size of these defects to determine the time required for the encapsulated gas to diffuse in the resist, [Abstract paragraph 3]. A person having skill in the art would have a reasonable expectation of successfully simulating the time required to remove defects in the system and method of 2016-Taylor by modifying 2016-Taylor with the modeling of the specific defect states of 2016-Jain. Therefore, it would have been obvious to combine 2016-Taylor with 2016-Jain to a person having ordinary skill in the art, and this claim is rejected under 35 U.S.C. 103.
With respect to claim 7, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 1, as noted above. Taylor does not teach wherein a criterion to determine the state of the curable composition is changed based on an arrangement of the plurality of droplets of the curable composition in the computational element.
However, 2016-Jain teaches wherein a criterion to determine the state of the curable composition is changed based on an arrangement of the plurality of droplets of the curable composition in the computational element (see FIG. 3.5, showing different characteristic lengths between droplets, as well as the ratio between characteristic lengths changes, [page 76]; which results in different percentages of droplet free areas, [page 75 paragraph 2]; the term criterion is interpreted in light of the specification, (see Specification [0073]-[0074]).
It would have been obvious to one skilled in the art before the effective filing date to combine Taylor with Jain because a teaching, suggestion, or motivation in the prior art would have led one skilled in the art to combine prior art teaching to arrive at the claimed invention. Taylor discloses a system and method that teaches all of the claimed features except for the connected state model includes a plurality of models corresponding to stages. Taylor does not discuss the separate states that lead to gas entrapment because of an assumption, but recognizes the need in the absence of that assumption: “We do however realize that achieving a short enough spreading time may necessitate accepting some gas entrapment and dissolution, and that there may be many pattern–droplet interactions that will lead to gas entrapment at whatever rate template curvature is relaxed”, (Taylor [page 9 paragraph 1 lines 4-6]). 2016-Jain goes into great detail to study NIL, and in particular the defects (2016-Jain [Abstract] paragraph 2). Jain seeks to find the minimum throughput from different arrangements of droplets [Abstract paragraph 2], and finds that rest flows more slowly after droplets merge causing a slower time for hexagonal arrangements, (see [page 83 paragraph 1], and the corresponding feature at the bottom right of the graph in FIG. 3.9, [page 83]). A person having skill in the art would have a reasonable expectation of successfully simulating the throughput time in the system and method of 2016-Taylor by modifying 2016-Taylor with the modeling of the specific arrangements of 2016-Jain. Therefore, it would have been obvious to combine 2016-Taylor with 2016-Jain to a person having ordinary skill in the art, and this claim is rejected under 35 U.S.C. 103.
With respect to claim 8, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 1, as noted above. 2016-Taylor does not teach wherein the state includes a first state in which the plurality of droplets of the curable composition in the computational element and the second member are not in contact with each other, a second state in which the plurality of droplets of the curable composition in the computational element and the second member are in contact with each other and the plurality of droplets are not connected to each other, a third state in which the plurality of droplets of the curable composition in the computational element and the second member are in contact with each other, droplets arranged in the first direction among the plurality of droplets are connected to each other, and droplets arranged in the second direction among the plurality of droplets are not connected to each other, a fourth state in which the plurality of droplets of the curable composition in the computational element and the second member are in contact with each other, all of the plurality of droplets are connected to each other to form a connected body, and there is a bubble in the connected body, and a fifth state in which the plurality of droplets of the curable composition in the computational element and the second member are in contact with each other, all of the plurality of droplets are connected to each other to form a connected body, and there is no bubble in the connected body, and the model includes a first model, a second model, a third model, a fourth model, and a fifth model respectively corresponding to the first state, the second state, the third state, the fourth state, and the fifth state.
However, 2016-Jain teaches wherein the state (see steps 1 and 2 of FIG. 3.1, for understanding the overall process, [page 67] and see FIG. 4.16(a) for the models defined by the states, [page 125]) includes a first state in which the plurality of droplets of the curable composition in the computational element and the second member are not in contact with each other (the state is defined by the height of the template above the droplets in step 1 of FIG. 3.1, [page 67]; this is the state just before h=1, so h > 1, defined by the first line in FIG. 4.16, [page 125]), a second state in which the plurality of droplets of the curable composition in the computational element and the second member are in contact with each other and the plurality of droplets are not connected to each other (the height from 1 > h >0.1 in FIG. 4.16(a), [page 124]; at the beginning of the imprint process, there are unfilled edges and droplets, [page 119 paragraph 1 bullet (i)]; see also reference character pointing to "unfilled edges" for illustrative description in FIG. 4.12, [page 120]), a third state in which the plurality of droplets of the curable composition in the computational element and the second member are in contact with each other, droplets arranged in the first direction among the plurality of droplets are connected to each other, and droplets arranged in the second direction among the plurality of droplets are not connected to each other (the intermediate states somewhere between 0.1>>h>>.01, shown as four horizontal lines with gray boxes that label unfilled channels in FIG. 4.16(a), [page 125]; definition of unfilled channels, [page 119 paragraph 1 bullet (iii)]; see also reference character "unfilled channels" in FIG. 4.12 for pictorial description, [page 120]), a fourth state in which the plurality of droplets of the curable composition in the computational element and the second member are in contact with each other, all of the plurality of droplets are connected to each other to form a connected body, and there is a bubble in the connected body (the second to last height where h<0.01, shown as a horizontal line with "unfilled pockets", which are bubbles in FIG. 4.16(a), [page 125]; defined as unfilled pockets at the end of the process, [page 119 paragraph 1 bullet (ii)]; see also reference character "unfilled pockets" for pictorial description in FIG. 4.12, [page 120]), and a fifth state in which the plurality of droplets of the curable composition in the computational element and the second member are in contact with each other, all of the plurality of droplets are connected to each other to form a connected body, and there is no bubble in the connected body (the last height of 0.01 = h in FIG. 4.16(a), where the "fluid filled" label is entirely filling the total substrate area meaning no bubbles/unfilled pockets, [page 125]), and the model includes a first model, a second model, a third model, a fourth model, and a fifth model respectively corresponding to the first state, the second state, the third state, the fourth state, and the fifth state (FIG. 4.16(a) is a model with sub-models defined by each horizontal line, where each sub-model has a height that defines the particular state as noted above, and labels that define the phases/defects, [page 125]).
It would have been obvious to one skilled in the art before the effective filing date to combine Taylor with Jain because a teaching, suggestion, or motivation in the prior art would have led one skilled in the art to combine prior art teaching to arrive at the claimed invention. Taylor discloses a system and method that teaches all of the claimed features except for the connected state model includes a plurality of models corresponding to stages. Taylor does not discuss the separate states that lead to gas entrapment because of an assumption, but recognizes the need in the absence of that assumption: “We do however realize that achieving a short enough spreading time may necessitate accepting some gas entrapment and dissolution, and that there may be many pattern–droplet interactions that will lead to gas entrapment at whatever rate template curvature is relaxed”, (Taylor [page 9 paragraph 1 lines 4-6]). 2016-Jain goes into great detail to study NIL, and in particular the defects (2016-Jain [Abstract] paragraph 2). Jain classifies two types of connected defects, pocket and channel, and then provides a model to predict the size of these defects to determine the time required for the encapsulated gas to diffuse in the resist, [Abstract paragraph 3]. A person having skill in the art would have a reasonable expectation of successfully simulating the time required to remove defects in the system and method of 2016-Taylor by modifying 2016-Taylor with the modeling of the specific defect states of 2016-Jain. Therefore, it would have been obvious to combine 2016-Taylor with 2016-Jain to a person having ordinary skill in the art, and this claim is rejected under 35 U.S.C. 103.
With respect to claim 9, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 1, as noted above. 2016-Taylor further teaches wherein the second member includes a pattern region having a pattern to be transferred to the curable composition (We have used the framework to explore directionality of droplet spreading beneath patterned templates, [Abstract] lines 5-6).
With respect to claim 10, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 9, as noted above. 2016-Taylor further teaches wherein the state of the curable composition in each computational element is determined in consideration of unevenness of the pattern (see section 2.5 “anisotropic resin spreading across patterned templates” and FIG. 4 showing how the shape of the pattern causes directional spreading, [page 5]; the term “unevenness” is interpreted in light of the specification, (Specification [0053])).
With respect to claim 11, 2016-Taylor in view of Jain and Cochrane teaches all of the limitations of claim 1, as noted above. 2016-Taylor further teaches wherein the state of the curable composition in each computational element is determined in consideration of unevenness of a surface of the first member (see section 2.5 “anisotropic resin spreading across patterned templates” and FIG. 4 showing how the shape of the pattern causes directional spreading, [page 5]; the term “unevenness” is interpreted in light of the specification, (Specification [0053])).
With respect to claim 12, 2016-Taylor teaches A non-transitory computer readable medium (memory that is part of a computer, [page 10 paragraph 1 line 6]) storing a program for causing a computer to execute a simulation method (full-field simulations run on a standard personal computer, [page 10 paragraph 1 lines 1-2]) of predicting a behavior of a curable composition in a process of bringing a plurality of droplets of the curable composition arranged on a first member and a second member into contact with each other and forming a film of the curable composition on the first member, the method comprising (Full-field, physically-based simulation of nanoimprint lithography (NIL), [Abstract] line 1; where the first member is the template, the second member is the wafer, and the dispensed droplets are the droplets as shown in FIG. 1, [page 2]; where the droplets are of a UV-curing resin, [page 1 paragraph 1 line 4]; the method is three steps, but only requires the last step to teach limitations [page 3 paragraph 2 lines 5-10]).
Regarding the rest of claim 12, incorporating the rejection of claim 1, claim 12 is rejected for substantially similar rational.
With respect to claim 13, 2016-Taylor teaches A system (full-field simulations run on a standard personal computer, [page 10 paragraph 1 lines 1-2]; used to select process parameters, [page 2 paragraph 2 line 1]; for the apparatus shown in FIG. 1, [page 2]) for bringing a plurality of droplets of the curable composition arranged on a first member and a second member into contact with each other and forming a film of the curable composition on the first member, the system comprising: (Full-field, physically-based simulation of nanoimprint lithography (NIL), [Abstract] line 1; where the first member is the template, the second member is the wafer, and the dispensed droplets are the droplets as shown in FIG. 1, [page 2]; where the droplets are of a UV-curing resin, [page 1 paragraph 1 line 4]; the method is three steps, but only requires the last step to teach limitations [page 3 paragraph 2 lines 5-10]) a computing apparatus including a processor connected to a memory, the processor being configured for predicting a behavior of a curable composition, the processor being configured to: (standard personal computer, [page 10 paragraph 1 lines 1-2], which inherently includes a processor; running a simulation with memory requirements, [page 10 paragraph 1 line 6]; to enable detailed studies of droplet-feature interactions within full-field imprinting process, [page 10 paragraph 1 lines 8-9]; where the droplets are referring to a UV-curing resin, [page 1 paragraph 1 line 4]) and a film forming apparatus (the entire apparatus shown in FIG. 1, [page 2]) including a mold (patterned quartz template [page 2 paragraph 4 line 1]), a dispenser (inkjet, [page 2, see caption in FIG. 1]), ... a substrate (wafer substrate, [page 2, see caption in FIG. 1]).
Taylor does not teach a substrate holder for supporting a substrate, a pressure control unit; the pressure control unit is configured to control deformation of the mold by controlling a pressure in a space defined in a rear side of the mold to act on the plurality of droplets on the mold and the substrate.
However, Jain teaches a substrate holder for supporting a substrate (rollers shown as circles in FIG. 1.7 of the R2RNIL [page 8]; same diagram shown in FIG. 5.1, [page 137], with a picture of the physical machine focused on the rollers shown in FIG. 5.2(a), [page 138], where “substrate is held under tension”, [page 136 paragraph 1 line 9]); a pressure control unit (rollers performing the substrate roll, [page 136 paragraph 1 line 13]); the pressure control unit is configured to control deformation of the mold by controlling a pressure in a space defined in a rear side of the mold to act on the plurality of droplets on the mold and the substrate (The inkjet dispenser dispenses photo-curable resist droplets on the substrate which is held under tension between two rollers. The patterned template is lowered on the droplets until they merge together to form a uniform resist film. Once the fluid spreading is complete and the entire template is filled, UV light is irradiated on the resist to photo-polymerize the resist monomer into a solid film. The template is then peeled off from the resist by a precise and synchronized movement of the substrate roll and the template, starting on one side and ending on the other side of the template, [page 136 paragraph 1 lines 8-14]).
Regarding the rest of claim 13, incorporating the rejection of claim 1, claim 13 is rejected for substantially similar rational.
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 ANISS CHAD whose telephone number is (571)270-3832 or aniss.chad@uspto.gov. The examiner can normally be reached M-F 8:00-4:00pm EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, James Trammell can be reached at 571-272-6712. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANISS CHAD/
Supervisory Patent Examiner
Art Unit 3662
Prosecution Insights