Information processing device, lithography device, information processing method, article manufacturing method, and program

Abstract

To provide an information processor capable of performing high-accuracy correction by a small amount of data.SOLUTION: An information processor (100) has: first acquisition means (101) for acquiring first data regarding a process of forming a pattern on a first layer on a substrate; second acquisition means (102) for acquiring second data regarding a process of forming a pattern on a second layer which is a layer under the first layer on the substrate; third acquisition means (103) for acquiring measurement data regarding the substrate in which the pattern was formed on the first layer; and calculation means (104) for calculating correction data by using the first data, the second data and the measurement data, where the calculation means decomposes the measurement data into a plurality of components, generates a plurality of estimation models for each component, and synthesizes the plurality of estimation models to calculate correction data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing apparatus, a lithography apparatus, an information processing method, an article manufacturing method, and a program. [Background technology]

[0002] Conventionally, a method for correcting exposure accuracy of a semiconductor exposure apparatus using a statistical method based on machine learning has been known. Patent Document 1 discloses a method for calculating a predicted overlay correction value using a correction model based on process processing information, a misalignment amount of a lower layer pattern, a correction amount of a photomask for the lower layer pattern, a misalignment amount of an upper layer pattern, and a correction amount of a photomask for the upper layer pattern. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-34682 Summary of the Invention [Problem to be solved by the invention]

[0004] The method disclosed in Patent Document 1 requires a large amount of exposure tool data and accurately extracted feature quantities to predict the complex measurement results of the exposure results and correct the exposure accuracy. However, it is difficult to accurately extract all feature quantities, and preparing a large amount of exposure tool data and performing learning is also difficult because it requires a long learning time.

[0005] SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide an information processing apparatus, a lithography apparatus, an information processing method, an article manufacturing method, and a program that are capable of performing high-precision correction with a small amount of data. [Means for solving the problem]

[0006] An information processing apparatus according to one aspect of the present invention includes a first acquisition means for acquiring first data relating to a process of forming a pattern on a first layer on a substrate, a second acquisition means for acquiring second data relating to a process of forming a pattern on a second layer below the first layer on the substrate, and a third acquisition means for acquiring measurement data relating to the substrate on which the pattern is formed on the first layer, and a method of processing a pattern using the first data, the second data, and the measurement data. Used to correct exposure accuracy correction Parameters The calculation means calculates the measurement data. Linear components, quadratic components, and higher-order components (third and higher) and generating a plurality of estimation models for each component, and synthesizing the plurality of estimation models to obtain the correction Parameters Calculate.

[0007] Other objects and features of the present invention will be described in the following embodiments. [Effects of the Invention]

[0008] According to the present invention, it is possible to provide an information processing apparatus, a lithography apparatus, an information processing method, an article manufacturing method, and a program that are capable of performing high-precision correction with a small amount of data. [Brief explanation of the drawings]

[0009] [Figure 1] 10 is a flowchart of a correction value determination process in each embodiment. [Figure 2] 4 is a flowchart of a correction value determination process according to the first embodiment. [Figure 3] 10 is a flowchart of a correction value determination process according to the second embodiment. [Figure 4] 10 is a flowchart of a correction value determination process according to the third embodiment. [Figure 5] FIG. 2 is a configuration diagram of an exposure apparatus in each embodiment. [Figure 6] FIG. 2 is a block diagram of an information processing device according to each embodiment. [Figure 7] 5A and 5B are explanatory diagrams of high-order components in each embodiment. [Figure 8]10 is a flowchart of a correction value determination process as a comparative example. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0011] First, the lithography apparatus in each embodiment will be described with reference to Fig. 5. In each embodiment, an exposure apparatus that exposes a substrate to light to form a pattern on the substrate will be described as an example of the lithography apparatus, but the present invention is not limited to this. This embodiment can also be applied to, for example, an imprint apparatus that forms a pattern of an imprint material on a substrate using a mold (original), or a drawing apparatus that irradiates a substrate with a charged particle beam to form a pattern on the substrate.

[0012] 5 is a configuration diagram of an exposure apparatus 10. The exposure apparatus 10 is an exposure apparatus that exposes a substrate W by projecting an image of a pattern of a mask (original, reticle) M onto the substrate W via a projection optical system 14. Here, the direction parallel to the optical axis of the projection optical system 14 is defined as the Z-axis direction, and two directions that are orthogonal to each other in a plane perpendicular to the Z-axis direction are defined as the X-axis direction and the Y-axis direction. Furthermore, rotation around the X-axis, rotation around the Y-axis, and rotation around the Z-axis are defined as θX, θY, and θZ, respectively.

[0013] The exposure apparatus 10 also has a light source 11, an illumination optical system 12, a mask stage (original stage) 13, a projection optical system 14, a substrate stage 15, and a main controller 16. The exposure apparatus 10 also has a first driver 21 that drives the mask stage 13, a second driver 22 that drives the optical element 14a of the projection optical system 14, and a third driver 51 that drives the substrate stage 15. The first driver 21, the second driver 22, and the third driver 51 are mechanisms that perform at least part of the process of forming a pattern on the substrate W, and are controlled by a mask stage controller 31, a projection controller 32, and a substrate stage controller 41, respectively. The main controller 16 also has, for example, a processor and a storage device, and controls the entire exposure apparatus 10 (each part of the exposure apparatus 10) by controlling the mask stage controller 31, the projection controller 32, and the substrate stage controller 41.

[0014] The light source 11 emits exposure light. The illumination optical system 12 illuminates the mask M using the light emitted from the light source 11. The mask stage 13 holds the mask M and can be configured to be movable, for example, within a plane perpendicular to the optical axis of the projection optical system 14, i.e., within the XY plane, by a first drive unit 21. The projection optical system 14 projects an image of the pattern of the mask M illuminated by the illumination optical system 12 onto the substrate. The projection optical system 14 includes an optical element 14a that can be moved, for example, in the X-axis direction by a second drive unit 22. The substrate stage 15 holds the substrate W and can be configured to be movable, for example, within the XY plane and rotatable in the θZ direction by a third drive unit 51.

[0015] The exposure tool 10 has an information processing device 100 for improving the overlay accuracy, focus accuracy, or pattern line width accuracy of the exposure tool 10 (correcting the exposure accuracy of the exposure tool 10). The information processing device 100 calculates correction parameters using a statistical method based on machine learning, based on exposure data from the exposure tool 10, etching data from the etching tool, film thickness data from the resist coating tool, or pattern overlay results. FIG. 6 is a block diagram of the information processing device 100. The information processing device 100 has a first acquisition unit 101, a second acquisition unit 102, a third acquisition unit 103, and a calculation unit 104.

[0016] A method of calculating correction parameters (correction value determination process) as a comparative example will now be described with reference to Fig. 8. Fig. 8 is a flowchart of the correction value determination process as a comparative example.

[0017] First, in steps S501 and S502, the information processing device acquires, as explanatory variables, data related to the process of forming a pattern on the lower layer (second layer) on the substrate. In step S502, the information processing device acquires, as explanatory variables, data related to the process of forming a pattern on the upper layer (first layer) on the substrate. The data acquired in steps S501 and S502 correspond to explanatory variables. In step S503, the information processing device acquires exposure data (lithography data) of the exposure device as a response variable. Subsequently, in step S504, the information processing device generates an estimation model using the explanatory variables acquired in steps S501 and S502 and the response variable acquired in step S503. Then, in step S505, the information processing device calculates correction parameters.

[0018] By using a statistical method based on machine learning, exposure processing can be performed using predicted overlay correction values ​​calculated based on process processing information, the amount of misalignment of the lower layer pattern, the amount of photomask correction, and the amount of misalignment of the upper layer pattern, and the amount of photomask correction. However, to predict the complex measurement results of the exposure results and correct the exposure accuracy using the method of the comparative example, a large amount of exposure data and accurately extracted feature quantities (all explanatory variables) are required. However, it is difficult to accurately extract all feature quantities, and preparing a large amount of exposure data and performing learning is also difficult because it requires a long learning time.

[0019] Next, a method for calculating correction parameters (correction value determination processing) in each embodiment will be described with reference to Fig. 1 and Fig. 6. Fig. 1 is a flowchart of the correction value determination processing in each embodiment.

[0020] First, in step S101, the second acquisition means 102 of the information processing device 100 acquires, as an explanatory variable, data (second data) relating to a process for forming a pattern on a lower layer (second layer) on a substrate. That is, the second acquisition means 102 acquires second data relating to a process for forming a pattern on the second layer, which is a layer below the first layer on a substrate.

[0021] Here, if multiple layers are formed as lower layers on the substrate, the second data may include at least one of etching data, film thickness data, or exposure data related to the process of forming patterns in the multiple layers.

[0022] The etching data may also include data relating to etching accuracy, such as information about distortion of a pattern or information about an error due to a step in a pattern.

[0023] The film thickness data also includes data indicating film thickness accuracy, for example, information about unevenness in the film thickness of the resist.

[0024] The exposure data also includes data indicating exposure accuracy (lithography accuracy). For example, the exposure data may include at least one of information about vibrations of the exposure apparatus due to reaction forces during operation, information about the synchronization accuracy between the mask stage and the substrate stage, or information about deviations of the mask stage or the substrate stage. The exposure data may also include at least one of error information from an interferometer that measures the position of the mask stage or the substrate stage, error information from a scope that measures alignment marks on the substrate, or error information from measurement of the flatness of the substrate. The exposure data may also include at least one of information about the exposure dose when exposing the substrate, focus information about focus accuracy, information about misalignment of lenses included in the optical system, or information about the shape of the effective light source of the illumination optical system.

[0025] In step S102, the first acquiring means 101 of the information processing device 100 acquires, as an explanatory variable, data (first data) relating to the process of forming a pattern on the upper layer (first layer) on the substrate. That is, the first acquiring means 101 acquires the first data relating to the process of forming a pattern on the first layer on the substrate.

[0026] In step S103, third acquisition means 103 of information processing apparatus 100 acquires measurement data relating to the substrate after exposure by exposure apparatus 10 (measurement data relating to the substrate on which a pattern is formed on the first layer).

[0027] Here, the measurement data includes measurement data relating to the overlay residual or the pattern shape, for example, the measurement data includes at least one of data relating to the overlay shape of the pattern or data relating to the arrangement of the pattern formed on the substrate.

[0028] Subsequently, in step S104, the calculation means 104 of the information processing device 100 decomposes the data acquired in step S103 into components. Specifically, in steps S104, S105, and S106, the calculation means 104 acquires the linear components, second-order components, and third-order or higher-order components of the pattern overlay residual or the pattern shape, respectively, and sets these as objective variables.

[0029] Subsequently, in step S107, the calculation means 104 generates a linear component estimation model using the explanatory variables acquired in steps S101 and S102 and the linear components of the dependent variable acquired in step S104. Similarly, in step S108, the calculation means 104 generates a second-order component estimation model using the explanatory variables acquired in steps S101 and S102 and the second-order components of the dependent variable acquired in step S105. Furthermore, in step S109, the calculation means 104 generates a third-order or higher-order component estimation model using the explanatory variables acquired in steps S101 and S102 and the third-order or higher-order components (for example, up to about fifth-order components) of the dependent variable acquired in step S106. Subsequently, in step S110, the calculation means 104 combines the estimation models for each component generated in steps S107 to S109 into one. Then, in step S111, the calculation means 104 calculates correction data based on the estimation model synthesized in step S110.

[0030] Here, the correction data includes correction parameters related to the pattern formed on the substrate.

[0031] (First embodiment) Next, a first embodiment will be described with reference to Fig. 2. Fig. 2 is a flowchart of a correction value determination process in this embodiment. In this embodiment, a parameter for improving overlay accuracy is calculated using a statistical method based on machine learning from the overlay residual between the upper and lower layers (first and second layers) when exposed by the exposure apparatus 10.

[0032] First, in step S201, the second acquisition means 102 acquires, as explanatory variables, data (second data) relating to the process of forming a pattern on a lower layer (second layer) on the substrate. In step S202, the first acquisition means 101 acquires, as explanatory variables, data (first data) relating to the process of forming a pattern on an upper layer (first layer) on the substrate. In step S203, the third acquisition means 103 acquires the overlay residual between the upper and lower layers (first and second layers) of the substrate after exposure by the exposure tool 10. Subsequently, in step S204, the calculation means 104 decomposes the data acquired in step S203 into components. Specifically, in steps S204, S205, and S206, the calculation means 104 acquires the linear component, the quadratic component, and the higher-order components (third or higher) of the pattern overlay residual, respectively, and sets these as objective variables. Then, the calculation means 104 goes through steps S207 to S210, which are similar to steps S107 to S110 in FIG. 1, and calculates correction parameters relating to focus in step S211.

[0033] The objective variable in this embodiment is a value obtained by decomposing the overlay residual between the upper and lower layers of a substrate after exposure with an exposure apparatus, where the upper layer is an upper layer of a substrate on which a pattern has been formed by exposure with an exposure apparatus, into linear components, quadratic components, cubic components or higher order components using principal component analysis.

[0034] In this embodiment, the linear components of the objective variables are mainly Shift, Wafer Mag, Wafer Rot, Chip Mag, and Chip Rot. Shift is the overall orientation error of the substrate and is calculated from the average value of the overlay residual for the entire substrate. Wafer Mag is the overall magnification error of the substrate and is calculated from the slope of a linear function of the overlay residual for each substrate coordinate. Wafer Rot is the overall rotation error of the substrate and is calculated from the slope of a linear function of the overlay residual for each substrate coordinate on a different axis (in the case of Wafer Rot X, the relationship between the Y coordinate of the substrate and the X coordinate of the overlay residual). Chip Mag is the magnification error within the substrate chip and is calculated from the slope of a linear function of the overlay residual for each coordinate within the chip. Chip Rot is the rotation error within the substrate chip and is calculated from the slope of a linear function of the overlay residual for each chip coordinate on a different axis (in the case of Chip Rot X, the relationship between the Y coordinate within the chip and the X coordinate of the overlay residual).

[0035] The second-order and third-order or higher-order components can be calculated from the coordinates within the substrate and chip, and the coefficients of the second-order and third-order or higher-order functions of the overlay residual. The main second-order or higher-order components of the overlay residual X and Y are expressed by the following equations (see Figure 7).

[0036]

number

[0037] The explanatory variables in this embodiment are etching data, film thickness data, and exposure data from the process in which the lower layer was formed and from the process prior to that, as well as film thickness data and exposure data from the exposure of the upper layer. The data content is selected to have a high correlation with the overlay residual. The etching data includes pattern distortion information, and the film thickness data includes resist film thickness unevenness information. The exposure data includes at least one of vibration information, synchronization error information, deviation information, interferometer error information, and scope error information.

[0038] Using a statistical method based on machine learning, an estimated model is generated for each objective variable that has been decomposed into linear, quadratic, cubic or higher order components using principal component analysis, and for each explanatory variable.The generated estimated models are then combined into a single prediction result, and the parameters for correcting the overlap residual are calculated from the prediction result, and correction is then performed.

[0039] (Second embodiment) Next, a second embodiment will be described with reference to Fig. 3. Fig. 3 is a flowchart of a correction value determination process in this embodiment. In this embodiment, a parameter for improving focus accuracy during exposure is calculated using a statistical method based on machine learning from the pattern shape between upper and lower layers formed on a substrate when exposed by exposure apparatus 10.

[0040] First, in step S301, the second acquisition means 102 acquires data (second data) relating to the process of forming a pattern on the lower layer (second layer) on the substrate as an explanatory variable. In step S302, the first acquisition means 101 acquires data (first data) relating to the process of forming a pattern on the upper layer (first layer) on the substrate as an explanatory variable. In step S303, the third acquisition means 103 acquires the pattern shape between the upper and lower layers (first and second layers) on the substrate after exposure by the exposure tool 10. Subsequently, in step S304, the calculation means 104 decomposes the data acquired in step S303 into components. Specifically, in steps S304, S305, and S306, the calculation means 104 acquires the linear component, second-order component, and third-order or higher-order component of the pattern shape, respectively, and sets these as the objective variables. Then, the calculation means 104 goes through steps S307 to S310, which are similar to steps S107 to S110 in FIG. 1, and calculates correction parameters relating to focus in step S311.

[0041] The objective variable in this embodiment is a value obtained by decomposing the pattern shape between the upper and lower layers of a substrate, where the lower layer is a substrate on which a pattern has been formed by exposure using an exposure apparatus, and the upper layer is a substrate after exposure using an exposure apparatus, into linear components, quadratic components, cubic components or higher order components using principal component analysis.

[0042] The explanatory variables in this embodiment are etching data, film thickness data, and exposure data related to the process of forming a pattern in the lower layer, and etching data, film thickness data, and exposure data related to the process of forming a pattern in the upper layer. The data content is selected to be highly related to focus accuracy. The etching data includes error information due to step differences in the pattern. The film thickness data includes information on uneven film thickness of the resist. The exposure data includes at least one of vibration information, deviation information, and flatness measurement error information.

[0043] Using machine learning statistical techniques on each objective variable decomposed into linear, quadratic, cubic or higher order components through principal component analysis and explanatory variables, an estimation model is generated for each objective variable decomposed into linear, quadratic, cubic or higher order components through principal component analysis.The generated estimation models are then combined into a single prediction result, and parameters related to focus accuracy are calculated and corrected.

[0044] (Third embodiment) Next, a third embodiment will be described with reference to Fig. 4. Fig. 4 is a flowchart of a correction value determination process in this embodiment. In this embodiment, a parameter that improves the line width accuracy of the pattern during exposure is calculated using a statistical method based on machine learning from the pattern shape between upper and lower layers formed on a substrate when exposed by exposure apparatus 10.

[0045] First, in step S401, the second acquisition means 102 acquires data (second data) relating to the process of forming a pattern on the lower layer (second layer) on the substrate as an explanatory variable. In step S402, the first acquisition means 101 acquires data (first data) relating to the process of forming a pattern on the upper layer (first layer) on the substrate as an explanatory variable. In step S403, the third acquisition means 103 acquires the pattern shape between the upper and lower layers (first and second layers) on the substrate after exposure by the exposure tool 10. Subsequently, in step S404, the calculation means 104 decomposes the data acquired in step S403 into components. Specifically, in steps S404, S405, and S406, the calculation means 104 acquires the linear component, the quadratic component, and the third or higher order component of the pattern shape, respectively, and sets these as the objective variables. Then, the calculation means 104 goes through steps S407 to S410 similar to steps S107 to S110 in FIG. 1, and calculates correction parameters relating to focus in step S411.

[0046] The objective variable in this embodiment is a value obtained by decomposing the pattern shape between the upper and lower layers of a substrate, where the lower layer is a substrate on which a pattern has been formed by exposure using an exposure apparatus, and the upper layer is a substrate after exposure using an exposure apparatus, into linear components, quadratic components, cubic components or higher order components using principal component analysis.

[0047] The explanatory variables in this embodiment are etching data, film thickness data, and exposure data related to the process of forming a pattern in the lower layer, and etching data, film thickness data, and exposure data related to the process of forming a pattern in the upper layer. The data content is selected to be highly related to the line width accuracy of the pattern. The etching data includes error information due to step differences in the pattern. The film thickness data includes information on uneven film thickness of the resist. The exposure data includes at least one of information on the exposure dose, focus information, information on lens misalignment, information on the shape of the effective light source, and deviation information.

[0048] Using a statistical method based on machine learning, an estimation model is generated for each objective variable decomposed into linear, quadratic, cubic or higher order components using principal component analysis, and explanatory variables. The generated estimation models are then combined into a single prediction result, after which parameters related to the line width and shape of the pattern are calculated and corrected.

[0049] As described above, in each embodiment, the information processing apparatus 100 includes a first acquisition unit 101, a second acquisition unit 102, a third acquisition unit 103, and a calculation unit 104. The first acquisition unit acquires first data (explanatory variables) related to a process of forming a pattern on an upper layer on a substrate (S102, S202, S302, S402). The second acquisition unit acquires second data (explanatory variables) related to a process of forming a pattern on a lower layer on a substrate (S101, S201, S301, S401). The third acquisition unit acquires measurement data (objective variables) related to a substrate on which a pattern has been formed by a lithography apparatus (S103, S203, S303, S403). The calculation unit calculates correction data (correction parameters) using the first data, the second data, and the measurement data. The calculation means also decomposes the measurement data into a plurality of components (S104 to S106), generates a plurality of estimation models for each component (S107 to S109), and combines the plurality of estimation models (S110) to calculate correction data (S111).

[0050] (Embodiment of manufacturing method of article) The method for manufacturing an article according to each embodiment is suitable for manufacturing articles such as, for example, microdevices such as semiconductor devices, elements having microstructures, and flat panel displays. The method for manufacturing an article according to this embodiment includes a step of processing a substrate using the substrate processing apparatus described above, and a step of manufacturing an article from the substrate processed in this step. Furthermore, this manufacturing method may include well-known steps (exposure, oxidation, film formation, vapor deposition, doping, planarization, etching, resist stripping, dicing, bonding, packaging, etc.). The method for manufacturing an article according to this embodiment is advantageous over conventional methods in at least one of the performance, quality, productivity, and production cost of the article.

[0051] (Other embodiments) The present invention can also be realized by supplying a program that realizes one or more functions of the above-described embodiments to a system or device via a network or a storage medium, and having one or more processors in the computer of the system or device read and execute the program. It can also be realized by a circuit (e.g., ASIC) that realizes one or more functions.

[0052] According to this embodiment, it is possible to preserve the features of the principal components by performing analysis of variance on the measurement data of the overlay shape and pattern shape after exposure, and therefore it is possible to extract feature quantities with a correct relationship. Therefore, according to this embodiment, it is possible to provide an information processing device, a lithography device, an information processing method, an article manufacturing method, and a program that enable high-precision correction with a small amount of data.

[0053] Although the preferred embodiments of the present invention have been described above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. [Explanation of symbols]

[0054] 100 Information processing device 101 First Acquisition Method 102 Second acquisition method 103 Third Acquisition Method 104 Calculation Method

Claims

1. a first acquisition means for acquiring first data relating to a process in which a pattern was formed in a first layer on a substrate; a second acquiring means for acquiring second data relating to a process of forming a pattern on a second layer that is a layer below the first layer on the substrate; a third acquisition means for acquiring measurement data relating to the substrate on which the pattern is formed on the first layer; a calculation unit that calculates a correction parameter for correcting a parameter related to exposure accuracy using the first data, the second data, and the measurement data, The calculation means calculates the measurement data by dividing it into a linear component, a second-order component, and a third-order or higher-order component.

1. An information processing apparatus comprising: decomposing an image into higher-order components, generating a plurality of estimation models for each component, and combining the plurality of estimation models to calculate the correction parameters.

2. 2. The information processing apparatus according to claim 1, wherein the first data includes at least one of etching data including data on etching accuracy, film thickness data including data indicating film thickness accuracy, and lithography data including data indicating lithography accuracy, relating to a process of forming a pattern on the first layer.

3. 3. The information processing device according to claim 1, wherein the second data includes at least one of etching data including data regarding etching accuracy, film thickness data including data indicating film thickness accuracy, or lithography data including data indicating lithography accuracy, relating to a process of forming a pattern on the second layer.

4. 4. The information processing apparatus according to claim 2, wherein the etching data includes at least one of information on distortion of a pattern and information on an error due to a step in a pattern.

5. 4. The information processing apparatus according to claim 2, wherein the film thickness data includes information about unevenness in the film thickness of the resist.

6. 6. The information processing device according to claim 2, wherein the lithography data includes at least one of information on vibration of the apparatus body due to reaction forces during operation of the lithography apparatus, information on synchronization accuracy between the original stage and the substrate stage, deviation information on the original stage or the substrate stage, error information on an interferometer that measures the position of the substrate stage or the original stage, error information on a scope that measures alignment marks on the substrate, error information on measurement of flatness of the substrate, information on the exposure amount when exposing the substrate, focus information on focus accuracy, information on positional deviation of lenses included in the optical system, or information on the shape of the effective light source of the illumination optical system.

7. 7. The information processing apparatus according to claim 1, wherein the measurement data includes data relating to the arrangement of the patterns formed on the first layer on the substrate, or data relating to the overlapping shape of the patterns.

8. 8. The information processing apparatus according to claim 1, wherein the correction parameters are correction parameters relating to the pattern formed on the first layer on the substrate.

9. the first data and the second data are explanatory variables; 9. The information processing apparatus according to claim 1, wherein the measurement data is a response variable.

10. 1. A lithographic apparatus for forming a pattern on a substrate, comprising: A lithography apparatus comprising: an information processing apparatus according to claim 1 .

11. a first acquisition step of acquiring first data relating to a process in which a pattern was formed in a first layer on a substrate; a second acquisition step of acquiring second data relating to a process of forming a pattern on a second layer that is a layer below the first layer on the substrate; a third acquisition step of acquiring measurement data relating to the substrate on which the pattern is formed in the first layer; a calculation step of calculating a correction parameter used to correct exposure accuracy using the first data, the second data, and the measurement data, The calculation step Decomposing the measurement data into linear components, quadratic components, and third-order or higher-order components to generate a plurality of estimation models for each component; and calculating the correction parameters by combining the plurality of estimation models.

12. forming the pattern on the substrate using a lithographic apparatus according to claim 10; and manufacturing an article from the substrate on which the pattern has been formed.

13. A program causing a computer to execute the information processing method according to claim 11.

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