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 .
Information Disclosure Statement
Acknowledgment is made of the information disclosure statements filed on November 10 2022, August 31 2023, February 23 2024, November 06 2024, February 12 2025, and June 10 2025, U.S. patents and Foreign Patents have been considered.
Claim Rejections - 35 USC § 112
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.
Claims 3 and 4 are rejected under 35 U.S.C. 112(d) as being of improper dependent form for failing to contain a reference to a claim previously set forth.
Claim 3 is rejected because it recites "The computer-readable medium of claim 3, wherein determining the cost function includes..." creating an improper self-reference. A dependent claim cannot reference itself as this violates the "previously set forth" requirement of 35 U.S.C. 112(d). The claim attempts to incorporate by reference all limitations of itself, creating a circular dependency that renders the scope indefinite.
Claim 4 is rejected because it recites "The computer-readable medium of claim 8, wherein determining the cost function includes..." creating an improper forward reference. Claim 4 cannot reference claim 8 because claim 8 has not been "previously set forth" when claim 4 appears in the claim sequence. The statutory requirement mandates that dependent claims may only reference claims that appear earlier in numerical order.
Applicant may cancel the claims, amend the claims to place them in proper dependent form by referencing appropriate earlier-numbered claims, rewrite the claims in independent form, or present a sufficient showing that the dependent claims comply with the statutory requirements.
Claims 3 and 4 are treated under the assumption that they are depending from claim 1.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 6, and 14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Apostol et al. (United States Patent Application Publication US20150149971A1), hereinafter referenced as Apostol.
In regards to claim 1 (Apostol) shows:
A non-transitory computer-readable medium having instructions that, when executed by a computer, cause the computer to execute a method for improving a design of a patterning device, the method comprising; Apostol [0022-0030] teaches a computer system with processor, memory, and storage for executing optimization instructions to improve mask design for lithographic processing.
obtaining mask points of a design of a mask feature, wherein the mask feature is associated with a target feature in a target pattern to be printed on a substrate; Apostol [0039] teaches obtaining vertices of rectilinear polygons making up the initial mask, where the mask represents the desired pattern to be printed on the substrate.
adjusting locations of the mask points to generate a modified design of the mask feature based on the adjusted mask points; Apostol [0046] teaches iteratively making changes to the derivative of the mask by finding update directions that move the current mask to the desired mask, thereby adjusting vertex locations to generate an improved mask design.
In regards to claim 6 (Apostol) shows the computer-readable medium of claim 1, wherein obtaining the mask points includes:
deriving the mask points from the target feature, wherein the deriving comprises associating the mask points with control points on the target feature to generate a first association between a first set of mask points and a first control point and a second association between a second set of mask points and a second control point; Apostol [0039] teaches computing the derivative by starting from the vertex that is closest to one of the axes, setting the derivative at the respective coordinates to 1, and then alternately assigning values to each vertex position, thereby associating mask vertices with specific control coordinates on the target pattern.
In regards to claim 14 (Apostol) shows the computer-readable medium of claim 1, wherein obtaining the design includes:
obtaining the design from a process that generates the design from the target feature; Apostol [0021] teaches using a novel inverse lithographic technique, embodiments of an optimization system generate optimized mask designs from target patterns.
wherein the process includes one or more of machine learning (ML)-based optimal proximity correction (OPC), continuous transmission mask (CTM) Freeform OPC, CTM+ Freeform OPC, segment-based OPC, or Inverse lithography technology; Apostol [0021] teaches using a novel inverse lithographic technique for optimizing lithography masks, thereby teaching inverse lithography technology as a process that generates design from target features.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 2 – 5, and 11 – 13 are rejected under 35 U.S.C. 103 as being unpatentable over US20150149971A1 (Apostol) in view of US20170082927A1 (Hsu).
In regards to claim 2 (Apostol) does not show the computer-readable medium of claim 1: wherein adjusting locations of the mask points is an iterative process, wherein each iteration includes: determining a cost function associated with an optical proximity correction process or a source mask optimization process, determining, for each control point on the target feature, location data of the mask points based on the cost function, and adjusting a location of one or more of the mask points based on the location data to optimize the cost function, wherein the adjusting includes updating the modified design.
Hsu teaches wherein adjusting locations of the mask points is an iterative process, wherein each iteration includes: Hsu [0081-0082] teaches an iterative method using the Gauss-Newton algorithm where design variables take values in successive iterations until convergence is reached.
Hsu teaches determining a cost function associated with an optical proximity correction process or a source mask optimization process; Hsu [0070] teaches computing a multi-variable cost function CF(z1, z2, ..., zN) = Σp wp fp²(z1, z2, ..., zN) of design variables for optimizing the lithographic process.
Hsu teaches determining, for each control point on the target feature, location data of the mask points based on the cost function; Hsu [0070-0071] teaches evaluation points as physical points on the wafer or design layout where fp(z1, z2, ..., zN) represents characteristics at each evaluation point used to determine optimization parameters.
Hsu teaches adjusting a location of one or more of the mask points based on the location data to optimize the cost function, wherein the adjusting includes updating the modified design; Hsu [0076] teaches reconfiguring the characteristics of the lithographic process by adjusting the design variables until a predefined termination condition is satisfied to minimize the cost function.
The motivation to combine Apostol and Hsu at the effective filing date of the invention is to provide improved lithographic mask optimization by incorporating advanced cost function methodologies. Apostol provides vertex-based mask optimization framework, while Hsu teaches sophisticated cost function formulations including edge placement error metrics, process window optimization, and iterative algorithms like Gauss-Newton. A person of ordinary skill would be motivated to combine these references to achieve more robust optimization results with better convergence properties and comprehensive performance metrics for lithographic patterning.
In regards to claim 3 (Apostol) does not show the computer-readable medium of claim 1: wherein determining the cost function includes: performing a simulation with the modified design to obtain a resist image signal or an etch image signal as the simulated signal, and determining the simulated signal for each control point, and wherein the adjusting is based on the simulated signal for the control point.
Hsu teaches wherein determining the cost function includes: performing a simulation with the modified design to obtain a resist image signal or an etch image signal as the simulated signal; Hsu [0064] teaches an aerial image is the radiation intensity distribution on the substrate, and a resist layer is exposed where the aerial image is transferred to the resist layer as a latent resist image. Hsu [0065] teaches resist image can be simulated from the aerial image using a resist model.
Hsu teaches determining the simulated signal for each control point, and wherein the adjusting is based on the simulated signal for the control point; Hsu [0070-0071] teaches evaluation points as physical points on the wafer or design layout where fp(z1, z2, ..., zN) represents characteristics at each evaluation point, and adjusting design variables based on these evaluation point characteristics.
The motivation to combine Apostol and Hsu at the effective filing date of the invention is to provide improved lithographic mask optimization by incorporating advanced cost function methodologies. Apostol provides vertex-based mask optimization framework, while Hsu teaches sophisticated cost function formulations including edge placement error metrics, process window optimization, and iterative algorithms like Gauss-Newton. A person of ordinary skill would be motivated to combine these references to achieve more robust optimization results with better convergence properties and comprehensive performance metrics for lithographic patterning.
In regards to claim 4 (Apostol) does not show the computer-readable medium of claim 1: wherein determining the cost function includes: performing a simulation with the modified design to obtain a simulated image; obtaining the process window using the simulated image, wherein the process window includes a range of focus and dose values for which the target pattern printed on a substrate using the modified design satisfies a predetermined specification.
Hsu teaches wherein determining the cost function includes: performing a simulation with the modified design to obtain a simulated image; obtaining the process window using the simulated image; Hsu [0064-0065] teaches simulating aerial image and resist image from design layout models. Hsu [0073] teaches process window maximization by minimizing EPE under various PW conditions.
Hsu teaches wherein the process window includes a range of focus and dose values for which the target pattern printed on a substrate using the modified design satisfies a predetermined specification; Hsu [0093] teaches the process window can be defined as a set of focus and dose values for which the resist image are within a certain limit of the design target of the resist image.
The motivation to combine Apostol and Hsu at the effective filing date of the invention is to provide improved lithographic mask optimization by incorporating advanced cost function methodologies. Apostol provides vertex-based mask optimization framework, while Hsu teaches sophisticated cost function formulations including edge placement error metrics, process window optimization, and iterative algorithms like Gauss-Newton. A person of ordinary skill would be motivated to combine these references to achieve more robust optimization results with better convergence properties and comprehensive performance metrics for lithographic patterning.
In regards to claim 5 (Apostol) does not show the computer-readable medium of claim 2: wherein the cost function includes at least one of an edge placement error, a simulated signal, a process window, or a mask rule check violation penalty.
Hsu teaches wherein the cost function includes at least one of an edge placement error, a simulated signal, a process window, or a mask rule check violation penalty; Hsu [0071] teaches fp(z1, z2, ..., zN) can be a distance between a point in the resist image to an intended position, which is edge placement error EPEp. Hsu [0073] teaches minimizing EPE under various PW conditions leads to maximizing the process window. Hsu [0073] teaches minimization of MEEF (Mask Error Enhancement Factor) and Hsu [0074] teaches constraints including rules governing patterning device manufacturability.
The motivation to combine Apostol and Hsu at the effective filing date of the invention is to provide improved lithographic mask optimization by incorporating advanced cost function methodologies. Apostol provides vertex-based mask optimization framework, while Hsu teaches sophisticated cost function formulations including edge placement error metrics, process window optimization, and iterative algorithms like Gauss-Newton. A person of ordinary skill would be motivated to combine these references to achieve more robust optimization results with better convergence properties and comprehensive performance metrics for lithographic patterning.
In regards to claim 11 (Apostol) does not show the computer-readable medium of claim 2: wherein adjusting the location of one or more of the mask points includes adjusting a set of the mask points collectively or individually.
Hsu teaches wherein adjusting the location of one or more of the mask points includes adjusting a set of the mask points collectively or individually; Hsu [0076] teaches optimization can be executed simultaneously where design variables are allowed to change at the same time, or alternately where different sets of variables are fixed while others are optimized in different steps.
The motivation to combine Apostol and Hsu at the effective filing date of the invention is to provide improved lithographic mask optimization by incorporating advanced cost function methodologies. Apostol provides vertex-based mask optimization framework, while Hsu teaches sophisticated cost function formulations including edge placement error metrics, process window optimization, and iterative algorithms like Gauss-Newton. A person of ordinary skill would be motivated to combine these references to achieve more robust optimization results with better convergence properties and comprehensive performance metrics for lithographic patterning.
In regards to claim 12 (Apostol) does not show the computer-readable medium of claim 2: wherein the location data of each mask point includes a slope value and a distance value by which a location adjustment of the corresponding mask point is to be performed in relation to a control point with which the corresponding mask point is associated.
Hsu teaches wherein the location data of each mask point includes a slope value and a distance value by which a location adjustment of the corresponding mask point is to be performed in relation to a control point with which the corresponding mask point is associated; Hsu [0081] teaches the gradient descent algorithm. Hsu [0083] teaches linearization using partial derivatives ∂fp(z1, z2, ..., zN)/∂zn which provide slope information, and the algorithm calculates distance values (zn - zni) for adjusting design variables in relation to evaluation points.
The motivation to combine Apostol and Hsu at the effective filing date of the invention is to provide improved lithographic mask optimization by incorporating advanced cost function methodologies. Apostol provides vertex-based mask optimization framework, while Hsu teaches sophisticated cost function formulations including edge placement error metrics, process window optimization, and iterative algorithms like Gauss-Newton. A person of ordinary skill would be motivated to combine these references to achieve more robust optimization results with better convergence properties and comprehensive performance metrics for lithographic patterning.
In regards to claim 13 (Apostol) does not show the computer-readable medium of claim 12 further comprising: applying a mask rule check process on the modified design to satisfy mask rule check constraints.
Hsu teaches applying a mask rule check process on the modified design to satisfy mask rule check constraints; Hsu [0074] teaches constraints that may include rules governing patterning device manufacturability, and the optimization process finds design variables under constraints to ensure the lithographic process meets manufacturing requirements.
The motivation to combine Apostol and Hsu at the effective filing date of the invention is to provide improved lithographic mask optimization by incorporating advanced cost function methodologies. Apostol provides vertex-based mask optimization framework, while Hsu teaches sophisticated cost function formulations including edge placement error metrics, process window optimization, and iterative algorithms like Gauss-Newton. A person of ordinary skill would be motivated to combine these references to achieve more robust optimization results with better convergence properties and comprehensive performance metrics for lithographic patterning.
Claims 7 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US20150149971A1 (Apostol) as applied to claims 1 and 6 above, respectively, and further in view of US20050097501A1 (Cobb).
In regards to claim 7 (Apostol) does not show the computer-readable medium of claim 6: wherein adjusting the locations of the mask points includes: modifying an association between the mask points and the control points based on a comparison between the modified design and the target feature.
Cobb teaches wherein adjusting the locations of the mask points includes: modifying an association between the mask points and the control points based on a comparison between the modified design and the target feature; Cobb [0023] teaches control sites where the smoothed fragmented polygons differ from pre-smoothed fragmented polygon can be moved or eliminated. Cobb [0036] teaches if the distance d or the angle θ is greater than a threshold, the control site can be eliminated from the polygon, and if the control site is within a predefined maximum distance threshold to the contour curve, then the position of the control site can be adjusted to be on the contour curve.
The motivation to combine Apostol and Cobb at the effective filing date of the invention is to enhance mask optimization with advanced control point management capabilities. Apostol provides basic vertex optimization, while Cobb teaches dynamic control site modification, smoothing filters, and sub-resolution assist feature handling. A person of ordinary skill would be motivated to combine these teachings to achieve more sophisticated mask designs with improved manufacturability and better pattern fidelity through enhanced control point associations and feature management.
In regards to claim 15 (Apostol) does not show the computer-readable medium of claim 1: wherein the mask feature is a sub-resolution assist feature or a main feature.
Cobb teaches wherein the mask feature is a sub-resolution assist feature or a main feature; Cobb [0031] teaches adjacent to the polygon are subresolution assist features that are placed in the layout to aid the formation of objects corresponding to portions of the polygon. Cobb [0032] teaches the subresolution assist features are too small to be resolved on a wafer, thereby distinguishing subresolution assist features from main polygon features that define objects to be created on the wafer.
The motivation to combine Apostol and Cobb at the effective filing date of the invention is to enhance mask optimization with advanced control point management capabilities. Apostol provides basic vertex optimization, while Cobb teaches dynamic control site modification, smoothing filters, and sub-resolution assist feature handling. A person of ordinary skill would be motivated to combine these teachings to achieve more sophisticated mask designs with improved manufacturability and better pattern fidelity through enhanced control point associations and feature management.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over US20150149971A1 (Apostol) as applied to claim 6 above, and further in view of US20050055658A1 (Mukherjee).
In regards to claim 8 (Apostol) does not show the computer-readable medium of claim 6: wherein one or more control points on the target feature is associated with different set of mask points in at least two iterations.
Mukherjee teaches wherein one or more control points on the target feature is associated with different set of mask points in at least two iterations; Mukherjee [0066] teaches the point of maximum deviation Pmax becomes a new evaluation point of a new segment A2, and the existing adjacent segments are divided so that new evaluation points are created. Mukherjee [0070] teaches the image intensity is computed at each of the new evaluation points of the newly created segments, thereby associating control points with different mask segments across iterations.
The motivation to combine Apostol and Mukherjee at the effective filing date of the invention is to provide adaptive refinement capabilities to mask optimization. Apostol teaches vertex-based optimization, while Mukherjee provides dynamic evaluation point creation and adaptive segment refinement across iterations. A person of ordinary skill would be motivated to combine these references to achieve more flexible optimization that can adaptively adjust control point associations based on convergence requirements and local geometry constraints.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over US20150149971A1 (Apostol) as applied to claim 1 above, in view of US20050097501A1 (Cobb) and further in view of US20050055658A1 (Mukherjee).
In regards to claim 9 (Apostol) does not show the computer-readable medium of claim 1: wherein obtaining the mask points includes: applying a smoothing process to the mask points, wherein the smoothing process performs curve fitting to connect the mask points with curves to generate the design of the patterning device as a first curvilinear pattern.
Cobb teaches wherein obtaining the mask points includes: applying a smoothing process to the mask points; Cobb [0025] teaches the smoothing filter produces a shape that is close to the shape of the polygon but does not include the sharp edges or high frequency components. Cobb [0027] teaches the smoothing filter comprises a convolution of the fragmented polygon and a two-dimensional Gaussian function.
Cobb differs from the claimed invention in that it does not explicitly disclose wherein the smoothing process performs curve fitting to connect the mask points with curves to generate the design of the patterning device as a first curvilinear pattern;
Mukherjee teaches wherein the smoothing process performs curve fitting to connect the mask points with curves to generate the design of the patterning device as a first curvilinear pattern; Mukherjee [0054] teaches curve fitting may be any method that fits a curve between two or more points, including B-spline, polynomial approximation, circular arc, cubic spline and Bezier curve. Mukherjee [0062] teaches a Binomial spline curve fit is performed using the information contained in the image intensities, image gradient and the image curvature to generate fitted curves connecting evaluation points.
The motivation to combine Apostol and Cobb at the effective filing date of the invention is to provide smoothing capabilities to vertex-based optimization for improved manufacturability.
The motivation to combine Apostol, Cobb, and Mukherjee at the effective filing date of the invention is to achieve comprehensive curvilinear pattern optimization. Apostol provides vertex optimization framework, Cobb teaches smoothing and filtering for manufacturability, and Mukherjee provides curve fitting techniques including B-spline and Bezier curves. A person of ordinary skill would be motivated to combine these teachings to create smooth, manufacturable curvilinear patterns with optimized optical performance.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over US20150149971A1 (Apostol) as applied to claims 1 and 6 above, in view of US20050055658A1 (Mukherjee) as applied to claim 8 above, and further in view of US20100162199A1 (Liu).
In regards to claim 10 (Apostol) does not show the computer-readable medium of claim 8 further comprising: performing image perturbation on the design of the patterning device to generate an enlarged version of the design.
Liu teaches performing image perturbation on the design of the patterning device to generate an enlarged version of the design; Liu [0064] teaches if tH, tV and tC are viewed as perturbations to the thin mask, then for first order approximation their cross terms are kept with the thin mask, and keeping the second order terms allows for capture of feature-to-feature interactions due to 3D scattering, thereby teaching performing image perturbation on the patterning device design to generate modified versions with enhanced feature interactions.
The motivation to combine Apostol and Mukherjee at the effective filing date of the invention is to provide adaptive refinement to vertex-based optimization.
The motivation to combine Apostol, Mukherjee, and Liu at the effective filing date of the invention is to incorporate three-dimensional mask effects into adaptive optimization. Apostol provides vertex optimization, Mukherjee teaches adaptive refinement, and Liu provides 3D mask perturbation modeling for enhanced feature interactions. A person of ordinary skill would be motivated to combine these references to achieve more accurate optimization that accounts for 3D scattering effects and feature-to-feature interactions in advanced lithographic processes.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANWER AHMED ALAWDI whose telephone number is (703)756-1018. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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/ANWER AHMED ALAWDI/Examiner, Art Unit 2851
/JACK CHIANG/Supervisory Patent Examiner, Art Unit 2851