Lithography method, article manufacturing method, information processing method, information processing device, and program

Abstract

To provide a technique advantageous for reducing overlay errors.SOLUTION: A lithography method for reducing overlay errors of a pattern formed by a lithography apparatus on a substrate comprises: an estimation step of estimating strain of the substrate generated when the substrate is held by a substrate holding section of the lithography apparatus by simulation at least based on information on the substrate holding section and information on the substrate; and a control step for controlling the lithography apparatus such that the overlay errors may be reduced based on the strain obtained in the estimation step.SELECTED DRAWING: Figure 5

Description

[Technical field]

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

[0002] Patent Document 1 describes a method for determining a plurality of types of distortion components related to a shot area of ​​a substrate held by a chuck, using information on the warped shape of the substrate before it is held by the chuck. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-49456 A Summary of the Invention [Problem to be solved by the invention]

[0004] The overlay error between a pattern on a substrate held by a chuck and a pattern to be transferred thereon may depend on the structure of the chuck. Patent Document 1 does not describe or suggest calculating distortion while considering the structure of the chuck or calculating an overlay error while considering the structure of the chuck.

[0005] The present invention aims to provide an advantageous technique for reducing overlay errors. [Means for solving the problem]

[0006] One aspect of the present invention relates to a lithography method for reducing an overlay error related to a pattern on a substrate, the lithography method comprising: To estimate, Based on information regarding the substrate holder and information regarding the substrate Generated by physical simulation A first estimation model is generated based on log data of the lithography apparatus and a measurement result of the overlay error. Based on statistical analysis and a control step of controlling the lithography apparatus based on the overlay error obtained in the estimation step. Effect of the Invention

[0007] In accordance with the present invention, advantageous techniques are provided for reducing overlay errors. [Brief description of the drawings]

[0008] [Figure 1] 1A and 1B are diagrams showing an example of the effect of distortion of a substrate caused by the substrate being held by suction on an alignment mark and an overlay mark; [Diagram 2] FIG. 1 is a diagram showing the configuration of a lithography system according to an embodiment. [Figure 3A] FIG. 1 is a diagram showing the configuration of an exposure apparatus as an example of a lithography apparatus. [Figure 3B] 1A and 1B are diagrams illustrating examples of shot layouts and sample shot areas. [Figure 4] 4 is a flowchart showing an exposure sequence according to an embodiment. [Diagram 5] FIG. 11 is a diagram showing a flow of estimating an overlay error using an estimation model based on statistical analysis and an estimation model based on physical simulation. [Figure 6A] A diagram showing an outline of the finite element method (FEM) in the plane of a substrate. [Figure 6B] Schematic diagram of the finite element method (FEM) outside the substrate plane. [Figure 7] FIG. 4 is a diagram for explaining parameters of a board node. [Figure 8] 4 is a diagram illustrating an example of the positional relationship between a substrate chuck, a substrate, pins, and a hand. [Figure 9] FIG. 1 is a diagram illustrating an example of an estimation model generated by modeling statistical data. [Figure 10] FIG. 11 is a diagram illustrating an example of a distortion of a substrate calculated by a physical simulation. [Figure 11] A comparison of the overlay error among the four methods. [Figure 12] 1A and 1B are diagrams illustrating examples of distortion patterns of a substrate caused by the substrate being held by suction. [Figure 13] 1 is a flowchart showing an example of spiral learning. [Figure 14] A diagram showing the flow of generating a model through statistical analysis. [Figure 15] FIG. 13 is a diagram showing a flow of estimating an overlay error by using a model based on statistical analysis in combination with a physical simulation. [Figure 16] FIG. 13 is a diagram illustrating input information for a physical simulation. [Figure 17] FIG. 11 is a diagram illustrating an example of output information by a physical simulation. [Figure 18] FIG. 13 is a diagram for explaining an estimation model based on a patterned physical simulation. [Figure 19] 1 is a diagram illustrating analysis target data of an exposure tool and an inspection tool used to estimate an overlay error; [Figure 20] 1 is a diagram illustrating analysis target data of a front-end processing apparatus, an exposure apparatus, and an inspection apparatus used to estimate an overlay error; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the embodiments will be described in detail with reference to the attached drawings. Note that the following embodiments do not limit the invention according to the claims. Although the embodiments describe a number of features, not all of these features are essential to the invention, and the features may be combined in any manner. Furthermore, in the attached drawings, the same reference numbers are used for the same or similar configurations, and duplicated descriptions are omitted.

[0010] FIG. 1 shows a schematic diagram of the shape of the lattice on the substrate W1 when the substrate chuck holds the substrate W1 by suction. The substrate W1 may be distorted by suction according to the shape of the holding surface of the substrate chuck. The suction may be vacuum suction or electrostatic suction. In FIG. 1, the upper right peripheral part of the substrate W1 is exaggerated in order to make the distortion of the substrate due to suction easier to understand. When distortion due to suction occurs in the substrate W1, even if the position of the alignment mark provided on the substrate W1 can be measured, the position of the overlay mark and the area to be transferred of the pattern cannot be accurately determined. For this reason, an overlay error may occur. The overlay error means a deviation between a pattern formed in advance on the substrate and a pattern of a layer formed thereon.

[0011] 2 shows the configuration of a lithography system according to an embodiment. The lithography system may include an exposure apparatus 101 as an example of a lithography apparatus, and an inspection apparatus 102 that inspects a substrate on which a pattern is formed by the exposure apparatus 101. The inspection apparatus 102 may be used to estimate or evaluate the quality of the performance of the exposure apparatus 101. The performance to be inspected by the inspection apparatus 102 may be, for example, the overlay accuracy and / or a CD (Critical Dimension) value of the exposure apparatus 101. The performance to be inspected by the inspection apparatus 102 may include productivity related to the manufacture of devices by the exposure apparatus 101.

[0012] The lithography system may include a front-end process processing apparatus 100. The front-end process processing apparatus 100 may include at least one of a thermal processing apparatus, a resist coating apparatus, a film forming apparatus, and an etching apparatus, for example, but may also include other apparatuses.

[0013] In an example sequence for manufacturing an article or device, a processing unit including a layer formation process by a front-end processing apparatus 100, a pattern formation process by an exposure apparatus 101, and an inspection process by an inspection apparatus 102 may be repeated.

[0014] The lithography system may include an analysis server (information processing device) 103. The analysis server 103 may be connected to, for example, the front-end processing device 100, the exposure device 101, and the inspection device 102 via a communication path. The communication path may be wired or wireless. The analysis server 103 may include an analysis unit that performs statistical analysis using data acquired from the front-end processing device 100, the exposure device 101, and the inspection device 102, and a physical simulator that performs physical simulation to output distortion information of the substrate. The analysis server 103 may be configured by a general-purpose or dedicated computer with a built-in program, or a PLD (abbreviation for Programmable Logic Device) such as an FPGA (abbreviation for Field Programmable Gate Array), or an ASIC (abbreviation for Application Specific Integrated Circuit), or a combination of all or part of these. The program may cause the computer to operate as an information processing device including the above-mentioned analysis unit and the physical simulator. The analysis server 103 may be connected to a plurality of front-end process processing devices 100 , a plurality of exposure tools 101 , and a plurality of inspection tools 102 .

[0015] The lithography system may include an APC (Advanced Process Control) 105. The APC 105 may calculate a correction value to be fed back to the exposure apparatus 101 based on the analysis result obtained by the analysis server 103. The lithography system may include an MES (Manufacturing Execution System) 104, and the correction value calculated by the APC 105 may be fed back (APC feedback) to the exposure apparatus 101 via the MES 104. The MES 104 may be connected to a plurality of exposure apparatuses 101.

[0016] 3A shows an example of the configuration of an exposure apparatus 101 as an example of a lithography apparatus. In the following, an example in which the exposure apparatus 101 exposes a substrate by a step-and-repeat method will be described, but the exposure apparatus 101 may also expose a substrate by a step-and-scan method. The exposure apparatus 101 may be an electron beam lithography apparatus. The exposure apparatus 101 may also be an imprint apparatus that hardens an imprint material by exposing it to light. Alternatively, the exposure apparatus 101 may be replaced by an imprint apparatus of another type.

[0017] In the example shown in FIG. 3A, an exposure light beam from an exposure illumination system (not shown) may be irradiated onto a pattern (e.g., an electronic circuit pattern) formed on a reticle (original) R. The pattern irradiated with the exposure light beam may be projected onto a substrate W1 via a projection optical system 1, thereby exposing the substrate W1. For example, g-line or i-line from a mercury lamp, or ultraviolet pulse light from an excimer laser light source may be used as the exposure light beam. The substrate W1 may be placed on a substrate chuck (substrate holder) CK mounted on a substrate stage 11 that is two-dimensionally movable along the XY plane. The exposure apparatus 101 may include an alignment optical system SHO. Although FIG. 3A shows an alignment optical system SHO that performs detection processing for alignment in the X direction, the exposure apparatus 101 may also include an alignment optical system SHO that performs detection processing for alignment in the Y direction. The exposure apparatus 101 may also include a CPU (controller) 9 that controls the overall operation of the exposure apparatus 101, a memory device 12, and a stage driving device 10 that drives the substrate stage 11. The exposure apparatus 101 may further include an optical autofocus device AFD. The optical autofocus device AFD may include, for example, a light emitting unit PD and a light receiving unit LD.

[0018] The alignment optical system SHO may include an illumination device 2 that irradiates the substrate W1 with non-exposure light that does not expose a resist (photosensitive agent) (not shown) applied on the substrate W1, a beam splitter 3, an imaging optical system 4, and an imaging device 5 that photoelectrically converts an image formed by the imaging optical system 4. The exposure apparatus 101 may include an A / D conversion device 6 that converts the output of the imaging device 5 into a two-dimensional image signal (two-dimensional digital signal sequence). The exposure apparatus 101 may also include an integrating device 7. The integrating device 7 may set a window for the two-dimensional image signal output from the A / D conversion device 6, and convert the two-dimensional image signal into a one-dimensional digital signal sequence S(x) by performing moving average processing in the Y direction within the window. The exposure apparatus 101 may also include a position detection device 8. The position detection device 8 may be configured to perform pattern matching for the one-dimensional digital signal sequence S(x) using a template pattern stored in advance, and output the address position of S(x) that has the highest degree of match with the template pattern to the CPU 9. The A / D conversion device 6, the integration device 7, and the position detection device 8 may also be provided for the alignment optical system SHO that performs detection processing for alignment in the Y direction.

[0019] FIG. 4 exemplarily shows an exposure sequence executed in exposure apparatus 101. The exposure sequence is controlled by CPU 9 based on a control program (not shown). First, prior to exposure, in steps 81 to 87, the relative alignment between reticle R and substrate W1 and the measurement for focus leveling can be performed in the following procedure. In step 81, substrate W1 is placed on substrate chuck CK on substrate stage 11 by a substrate transport device (not shown). In steps 82 and 83, the global leveling component of substrate W1 is measured. In step 82, a command is sent to stage driving device 10, and substrate stage 11 is driven so that sample shot area S1 for focus leveling is positioned in the detection area of ​​optical autofocus device AFD. In step 83, data for performing focus leveling is acquired by optical autofocus device AFD.

[0020] 3B illustrates sample shot areas S1 to S8. In step 84, it is determined whether steps 82 and 83 have been executed for all of sample shot areas S1 to S8. If steps 82 and 83 have been executed for all of sample shot areas S1 to S8, processing proceeds to step 85; if not, steps 82 and 83 are executed for the remaining sample shot areas.

[0021] In step 85, a command is sent to the stage driving device 10, and the substrate stage 11 is driven so that the alignment mark of the first sample shot area S1 among the multiple sample shot areas is positioned within the field of the alignment optical system SHO. In step 86, alignment measurement is performed for the first sample shot area S1. That is, the illumination device 2 of the alignment optical system SHO illuminates the alignment mark of the sample shot area S1 via the beam splitter 3, the reticle R, and the projection optical system 1. The alignment mark can be, for example, a plurality of rectangular patterns of the same shape arranged at a constant pitch. The light beam reflected by the alignment mark reaches the beam splitter 3 via the projection optical system 1 and the reticle R, where it is reflected and forms an image of the alignment mark on the imaging surface of the imaging device 5 via the imaging optical system 4. This image is photoelectrically converted by the imaging device 5, converted into a two-dimensional digital signal sequence by the A / D conversion device 6, and further converted into a one-dimensional digital signal sequence S(x) by the integrator 7. The position detector 8 performs pattern matching on this digital signal sequence S(x), and the address position of S(x) that has the highest degree of match with the template pattern can be determined by the CPU 9. Since this address position indicates the position of the alignment mark based on the imaging surface of the imaging device 5, the CPU 9 calculates the position of the alignment mark relative to the reticle R based on the relative positions of the imaging device 5 and the reticle R that have been determined in advance. This measures the amount of positional deviation in the X direction of the first sample shot area S1. Next, the CPU 9 measures the amount of positional deviation in the Y direction in the same procedure as the X direction measurement. The above process ends the alignment measurement for the sample shot area S1. Furthermore, alignment measurements are performed for the other sample shot areas S2 to S8 in the same procedure.

[0022] In step 87, it is determined whether steps 85 and 86 have been executed for all of the alignment sample shot areas S1 to S8. If steps 85 and 86 have been executed for all of the sample shot areas S1 to S8, the process proceeds to step 88; if not, steps 85 and 86 are executed for the remaining sample shot areas.

[0023] Next, in step 88, focus leveling correction can be performed based on the result of the focus leveling measurement. Next, in step 89, alignment correction can be performed based on the result of the alignment measurement. Then, in step 90, the substrate is exposed by the step-and-repeat method. Thereafter, when it is determined in step 91 that exposure of all shot areas has been completed, in step 92, the substrate W1 can be carried out by a substrate transport device (not shown).

[0024] The above steps 81 to 92 are performed for all substrates to be processed. In step 93, it is determined whether or not all substrates to be processed have been processed, and if an unprocessed substrate remains, steps 81 to 92 are repeated for that substrate. On the other hand, if all substrates to be processed have been processed, the exposure sequence shown in FIG. 4 ends.

[0025] 4, the CPU 9 may store in the storage device 12 correction values ​​for alignment, alignment mark position measurement results, matching degree between the captured alignment mark and the template, alignment mark measurement conditions, and the like. The CPU 9 may also store in the storage device 12 feature quantities such as evaluation values ​​of the waveform during alignment mark measurement, focus measurement values, calibration measurement values, and deviation information of the XY stage. Values ​​acquired by the CPU 9 and the position detection device 8 may be stored in the storage device 12 as log data. The CPU 9 may perform processing (preprocessing) so that the relationship between the above feature quantities and overlay measurement data can be machine-learned by the inspection device 102, and generate an estimation model by statistical analysis through machine learning.

[0026] The analysis server 103 may also generate an estimation model for estimating a distortion of the substrate caused by the substrate chuck holding the substrate by suction through a physical simulation. An example of a flow for estimating an overlay error is shown in Fig. 5. The analysis server 103 may estimate an overlay error using an estimation model based on the statistical analysis as described above and an estimation model based on the physical simulation as described above.

[0027] The analysis server 103 may generate an estimation model 23 based on statistical analysis by performing modeling based on the statistical data 20. Details of the statistical data will be described in the description of the first and second embodiments. The estimation model 23 based on statistical analysis may be generated, for example, using a random forest method implemented in a machine learning module. The random forest method is an example of a supervised machine learning method that can evaluate or determine which feature quantity an overlay error factor is based on based on statistics and hyperparameters such as log data and overlay measurement data. The estimation model 23 based on statistical analysis may be generated by other machine learning methods. Examples of other machine learning methods include, but are not limited to, a Bayesian classifier, an artificial neural network, a decision tree, and a support vector machine.

[0028] The analysis server 103 may generate an estimated model 24 by physical simulation by performing modeling based on the physical data 21 acquired from the exposure apparatus 101. FIG. 16 illustrates input information related to the physical data. As illustrated in FIG. 16, the information related to the substrate may include, for example, information related to at least one of the shape, size, amount of warping, friction coefficient, and bending rigidity of the substrate. Furthermore, the information related to the substrate chuck (substrate holding portion) may include information related to the structure of the substrate chuck (substrate holding portion). The information related to the structure of the substrate chuck (substrate holding portion) may include, for example, information related to at least one of the positions of pins supporting the substrate, the shape of the pins, the height of the pins, the structure of the bank, the piping system, and the suction port. The information related to the substrate chuck (substrate holding portion) may include information related to the drive sequence of a substrate stage on which the substrate chuck (substrate holding portion) is mounted. An example of output information as an estimation result based on physical data is shown in Fig. 17. In this example, modeling is performed based on the information shown in Fig. 16 and Fig. 17, but is not limited to these.

[0029] In step 83, the analysis server 103 may acquire physical values ​​related to the warpage shape of the substrate W1 held by suction by the substrate chuck CK mounted on the substrate stage 11, and store the physical values ​​in the storage device 12. The analysis server 103 may acquire information on the warpage shape of at least one substrate when it is not held by the substrate chuck CK by suction from a measuring device outside or inside the exposure apparatus 101. Here, the information on the warpage shape may be, for example, the amount of warpage (the distance from the flat surface) at at least one point on the substrate with respect to a flat surface that passes through the center of the substrate surface and is parallel to the substrate surface. Alternatively, the analysis server 103 may acquire information on the warpage shape of substrates with various warpage shapes by computer simulation using the finite element method. The analysis server 103 may also acquire information on the warpage shape from outside the exposure apparatus 101, or, for example, the information on the warpage shape may be input by an operator from a console of the exposure apparatus 101. Alternatively, if the exposure apparatus 101 is connected to a network such as a LAN, the analysis server 103 may obtain information on the warpage shape and overlay error from external measuring instruments, servers, and other devices connected to the network via the network.

[0030] For the physical simulation, the analysis server 103 may use data on physical values ​​stored in the storage device 12. Here, in order to evaluate the amount of misalignment when the substrate is held by the substrate chuck CK through suction based on the data on physical values, the finite element method (FEM) as shown in FIG. 6A can be adopted. In this example, the deformation outside the substrate plane, which employs an element with 4 nodes and 12 degrees of freedom, the simplest plate bending element, is defined as shown in formula (1). Meanwhile, the deformation within the substrate plane as shown in FIG. 6B is defined as shown in formula (2). In this example, a determinant representing coordinates on the substrate plane is used as an equation representing the amount of misalignment.

[0031]

number

[0032]

number

[0033] For equation (1), a linear plate bending element with 4 nodes and 12 degrees of freedom is adopted. Each node has three degrees of freedom: z-direction displacement uz, and rotation angles θx, θy around the x-axis and y-axis. For the element, a normalized coordinate system (ξ, η) is defined as shown in Figure 7. At this time, the z-displacement field uz(ξ, η) is interpolated by equation (3) using the nodal displacement values.

[0034]

number

[0035] Here, fk, gk, and hk are shape functions and are expressed by equations (4.1) to (4.3).

[0036]

number

[0037]

number

[0038]

number

[0039] Here, ξk, ηk are the normalized coordinate values ​​of each node.

[0040] For equation (2), a plane stress element with 4 nodes and 8 degrees of freedom can be used to calculate the in-plane deformation. Each node has 2 degrees of freedom: x-displacement ux and y-displacement uy. The displacement fields ux and uy are expressed by equations (5.1) and (5.2), respectively.

[0041]

number

[0042]

number

[0043] Here, Nk(ξ, η) is a shape function and is expressed by equation (6).

[0044]

number

[0045] (ξk, ηk) are the coordinate values ​​of each node in the normalized element coordinate system, and the values ​​are shown in FIG. 7. In the substrate chuck, the in-plane deformation of the substrate is very small compared to the out-of-plane deformation (z-axis direction) of the substrate. Therefore, the minute displacement in the in-plane direction caused by the out-of-plane deformation cannot be ignored. In a general simulator, such an effect is calculated by applying a nonlinear finite element method, but this has the disadvantages of making the implementation complicated and increasing the calculation cost. Therefore, in this embodiment, it is preferable to adopt a method of approximately expressing the in-plane deformation caused by the out-of-plane deformation in the physical simulation, but the nonlinear finite element method may also be adopted.

[0046] As described above, in this embodiment, an overlay error is estimated from the estimation model 23 based on statistical analysis, the estimation model 24 based on physical simulation, and the overlay error estimation target data acquired from the exposure tool and the front-end processing device. Then, an overlay error estimation result 25 is calculated, and a correction value (offset value) for correcting the position and orientation of the substrate during exposure is calculated based on the estimation result 25, which is fed back to perform the subsequent exposure sequence.

[0047] First Embodiment As described above, in the flow shown in FIG. 5, an estimation result of an overlay error is derived using an estimation model by statistical analysis and an estimation model by physical simulation. In the first embodiment, an estimation result of an overlay error is derived based on information obtained from the exposure tool 101 and the inspection tool 102. FIG. 19 illustrates items of statistical data 20 for generating an estimation model by statistical analysis corresponding to the flow of FIG. 5, physical data 21 for generating an estimation model by physical simulation, and data 22 for estimating an overlay error. The statistical data 20 may include log data acquired from the exposure tool 101 and overlay measurement data acquired from the inspection tool 102. The physical data 21 may include data related to physical values ​​acquired from the exposure tool 101. The statistical data 20 may include log data acquired from a plurality of exposure tools 101 and overlay measurement data acquired from a plurality of inspection tools 102.

[0048] Data 22 for estimating the overlay error may include log data and data related to physical values ​​acquired from the exposure apparatus 101. Here, the acquired log data and data related to physical values ​​may be the same as the log data acquired as statistical data 20 and the data related to physical values ​​acquired as physical data 21, respectively.

[0049] FIG. 14 illustrates an example of a statistical analysis flow performed by the analysis server 103. In step 300, the analysis server 103 may acquire log data of the exposure tool 101. In step 301, the analysis server 103 may acquire overlay measurement data by the inspection tool 102 corresponding to the log data. In step 302, the analysis server 103 may perform preprocessing on the log data and the measurement data. In this preprocessing, an overlay value may be extracted as a response variable from the overlay measurement data by the inspection tool 102, and an explanatory variable for estimating the response variable may be extracted from the log data of the exposure tool 101. The amount of data required for machine learning may be determined so as to reduce the influence of data variability. In one example, the number of records is preferably 4000 or more, but 100 or more may be sufficient. In step 303, the analysis server 103 may generate an estimation model by statistical analysis from the preprocessed file.

[0050] Fig. 8 exemplarily shows factors that may affect the overlay error in the exposure apparatus 101 for which the overlay error is to be estimated. Fig. 8 exemplifies the positional relationship between the substrate chuck CK, the substrate W1, three pins P that suck the substrate W1, and the hand H that transports the substrate W1 to the three pins P. The analysis server 103 can perform a physical simulation using the suction ports of the three pins P, the gravitational acceleration when the substrate W1 is transported by the hand H, and the contact area between the substrate chuck CK and the substrate W1 as input values. In the example of Fig. 8, the substrate chuck CK moves in the positive Z direction and can hold the substrate W1 that is on the three pins P by suction.

[0051] FIG. 15 illustrates an example of the flow of a method for estimating an overlay error. In this example, the analysis server 103 uses log data from the exposure tool 101, measurement data from the inspection tool 102, and physical data from the exposure tool 101 as input data. First, in step 304, the analysis server 103 acquires log data from the exposure tool 101. In step 305, the analysis server 103 acquires overlay measurement data from the inspection tool 102 that corresponds to the log data acquired in step 304. In step 306, the analysis server 103 performs preprocessing on the log data and measurement data. vinegar In step 307, the analysis server 103 inputs the above input data into the model based on the statistical analysis generated in the flow of FIG. 14. In step 308, the analysis server 103 calculates or acquires the overlay error (overlay prediction result) according to the model based on the statistical analysis. In step 309, the analysis server 103 acquires physical data from the exposure tool 101, and in step 310, the analysis server 103 performs a physical simulation based on the physical data to estimate the distortion of the substrate. In step 311, the analysis server 103 estimates the overlay error based on the overlay error calculated in step 308 and the distortion of the substrate estimated in step 311.

[0052] Fig. 9 illustrates an estimation model F(x) based on statistical analysis, which is generated by the analysis server 103 through learning using the log data of the exposure tool 101 and the overlay measurement data from the inspection tool 102. The arrows in Fig. 9 indicate overlay error vectors. Fig. 10 illustrates an estimation model S(x) modeled by the analysis server 103 through physical simulation. The arrows in Fig. 10 indicate vectors of distortion of the substrate.

[0053] The analysis server 103 may estimate the overlay error using an estimation model F(x) based on statistical analysis and an estimation model S(x) modeled by physical simulation. The analysis server 103 may provide the estimated overlay error to the APC 105, which may calculate a correction value based on the overlay error. The MES 104 may feed back the correction value calculated by the APC 105 to the exposure tool 101 to correct subsequent exposure processes. The estimated value y of the overlay error may be expressed by, for example, Equation (7). Note that the correction value may be the overlay error itself. The correction value for correcting the overlay error may include a value for correcting or reducing an alignment error of the substrate.

[0054] y = αF(x) + βS(x) (7) α and β are constants

[0055] The analysis server 103 may be provided separately from the exposure apparatus 101, but may also be incorporated into the exposure apparatus 101. APC feedback may be provided from the APC 105 to the exposure apparatus 101 without going through the MES 104.

[0056] Fig. 11 shows the result of not estimating the overlay error, and the result of estimating the overlay error from an estimation model based on statistical analysis and feeding back the estimation result using APC (pattern 1). Fig. 11 also shows the result of estimating the overlay error using an estimation model based on physical simulation and feeding back the estimation result using APC (pattern 2). Fig. 11 also shows the result of estimating the overlay error using an estimation model based on statistical analysis and an estimation model based on physical simulation and feeding back the estimation result using APC (pattern (1+2)). The vertical axis shows 3σ of the overlay error measured by inspection device 102.

[0057] The results of Pattern 2 show better results than the case without estimating the overlay error. In the example of Fig. 11, the results of Pattern 2 are worse than those of Pattern 1, but by improving the accuracy of the physical simulation, the results of Pattern 2 can be made better than those of Pattern 1.

[0058] Pattern (1+2) gives the best results, and the reasons for this are discussed below. Statistical analysis using exposure tool data shows that A In the physical simulation, the positioning error when the substrate is placed on the check and the measurement data of the unevenness of the substrate are used, but if the pressure distribution and the flow velocity distribution when the pins suck the substrate are not measured, they cannot be used for analysis. On the other hand, in the physical simulation, it is possible to simulate the pressure distribution and the flow velocity distribution when the pins suck the substrate as illustrated in FIG. 17 from the input values ​​related to the pin shape and the piping system illustrated in FIG. 16. On the other hand, in the physical simulation, it is not possible to simulate the positioning error when the substrate is placed on the check and the unevenness of the substrate. Therefore, by using an estimation model based on statistical analysis and an estimation model based on physical simulation in combination, it is possible to consider not only the positioning error when the substrate is placed on the check and the unevenness of the substrate, but also the pressure distribution and the flow velocity distribution when the pins suck the substrate. This can reduce the 3σ of the overlay error.

[0059] <Second embodiment> In the second embodiment, the overlay error is estimated based on information or data obtained from the front-end processing device 100, the exposure tool 101, and the inspection tool 102. FIG. 5 shows a derivation flow of the second embodiment. FIG. 20 shows examples of items of statistical data 20 for generating an estimation model by statistical analysis corresponding to FIG. 5, physical data 21 for generating an estimation model by physical simulation, and data 22 for estimating the overlay error. The statistical data 20 includes measurement data acquired from the front-end processing device 100, log data acquired from the exposure tool 101, and overlay measurement data acquired from the inspection tool 102. The physical data 21 includes data related to physical values ​​acquired from the exposure tool 101. The statistical data 20 may include measurement data acquired from a plurality of front-end processing devices 100, log data acquired from a plurality of exposure tools 101, and overlay measurement data acquired from a plurality of inspection tools 102.

[0060] Data 22 for estimating the overlay error may include log data acquired from the exposure tool 101, data related to physical values, and measurement data acquired from the front-end processing device 100. Here, the acquired log data and data related to physical values ​​may be the same as the log data and measurement data acquired as the statistical data 20, and the data related to physical values ​​acquired as the physical data 21, respectively.

[0061] The analysis server 103 can perform pre-processing on the measurement data previously acquired from the front-end process processing device 100, multiple log data at the time of exposure, and overlay measurement data of the inspection tool 102 corresponding to the log data. 102 From the overlay measurement data by the exposure apparatus 101, an overlay value is extracted as a response variable, and explanatory variables for estimating the response variable can be extracted from the log data of the exposure apparatus 101. The amount of data required for machine learning can be determined so as to reduce the influence of data variability. In one example, the number of records is preferably 4000 or more, but 100 or more is also acceptable. aThe analysis server 103 may generate an estimation model from the preprocessed files through statistical analysis.

[0062] Fig. 8 exemplarily shows factors that may affect the overlay error in the exposure apparatus 101 for which the overlay error is to be estimated. Fig. 8 exemplifies the positional relationship between the substrate chuck CK, the substrate W1, three pins P that suck the substrate W1, and the hand H that transports the substrate W1 to the three pins P. The analysis server 103 can perform a physical simulation using the suction ports of the three pins P, the gravitational acceleration when the substrate W1 is transported by the hand H, and the contact area between the substrate chuck CK and the substrate W1 as input values. In the example of Fig. 8, the substrate chuck CK moves in the positive Z direction and can hold the substrate W1 that is on the three pins P by suction.

[0063] The estimation model F(x) based on statistical analysis learned by the analysis server 103 using the measurement data of the front-end processing device 100, the log data of the exposure tool 101, and the overlay measurement data of the inspection tool 102 can be roughly visualized in a format similar to that of FIG. 9. However, the second embodiment differs from FIG. 9 in that the measurement data of the front-end processing device 100 is taken into consideration. The arrows in FIG. 9 indicate overlay error vectors. FIG. 10 illustrates an estimation model S(x) modeled by the analysis server 103 using physical simulation. The arrows in FIG. 10 indicate vectors of distortion of the substrate.

[0064] The analysis server 103 may estimate an overlay error using an estimation model F(x) based on statistical analysis and an estimation model S(x) modeled by physical simulation. The analysis server 103 may provide the estimated overlay error to the APC 105, which may store it. The MES 104 feeds back the overlay error stored in the APC 105 to the exposure tool 101 to correct subsequent exposure processes. The estimated value y of the overlay error may be expressed, for example, by Equation (8).

[0065] y = αF(x) + βS(x) (8) α and β are constants

[0066] The analysis server 103 may be provided separately from the exposure apparatus 101, but may also be incorporated into the exposure apparatus 101. APC feedback may be provided from the APC 105 to the exposure apparatus 101 without going through the MES 104.

[0067] <Third embodiment> In the third embodiment, a method for calculating a correction value when a pilot wafer is run as a condition setting for the exposure tool 101 is provided for the purpose of further reducing the overlay error. FIG. 13 shows the process in the third embodiment. In the third embodiment, spiral learning can be adopted. First, in step 200, the analysis server 103 acquires physical data as exemplified in FIG. 19. In step 201, the analysis server 103 performs physical data modeling based on the physical data to calculate an estimation model S(x) by physical simulation. In step 202, the analysis server 103 acquires statistical data as exemplified in FIG. 19. In step 203, the analysis server 103 performs statistical data modeling based on the statistical data to calculate an estimation model F(x) by statistical analysis.

[0068] Next, in step 204, the physics server 103 estimates an overlay error using an estimated model S(x) based on a physical simulation and an estimated model F(x) based on a statistical analysis. In step 205, the analysis server 103 provides the estimated overlay error to the APC 105, which may calculate a correction value based on the overlay error. The MES 104 may feed back (APC feedback) the correction value calculated by the APC 105 to the exposure tool 101 to correct the subsequent exposure process. The estimated value y of the overlay error may be expressed as, for example, 9(7). Note that the correction value may be the overlay error itself.

[0069] y = αF(x) + βS(x) (9) α and β are constants

[0070] The analysis server 103 repeats the operations of steps 202 to 206 until the overlay error falls within the target range, thereby updating the estimation model F(x) based on the statistical analysis.

[0071] The analysis server 103 may be provided separately from the exposure apparatus 101, but may also be incorporated into the exposure apparatus 101. APC feedback may be provided from the APC 105 to the exposure apparatus 101 without going through the MES 104.

[0072] <Fourth embodiment> In the fourth embodiment, the estimation model F(x) based on statistical analysis is not used, and the overlay error can be estimated from the result of a physical simulation based on the input information exemplified in Fig. 16. The subsequent exposure process can be corrected by APC feedback based on the obtained estimated value of the overlay error. The overlay estimated value y can be expressed as Equation (10).

[0073] y = β'S(x) (10) β' is a constant

[0074] The analysis server 103 may be provided separately from the exposure apparatus 101, but may also be incorporated into the exposure apparatus 101. APC feedback may be provided from the APC 105 to the exposure apparatus 101 without going through the MES 104.

[0075] The input information for generating the estimation model S(x) by the physical simulation is not limited to the information exemplified in FIG. 16, and other information may be used.

[0076] <Fifth embodiment> No. 5In the embodiment, the estimation model F(x) based on statistical analysis is not used, and the overlay error can be estimated from the result of a physical simulation based on the input information exemplified in FIG.

[0077] No. 5 In the embodiment, in order to reduce the time required for simulation in the analysis server 103, a plurality of models with reduced degrees of freedom may be generated from the input information, for example, models for each of the conditions of patterns 1 to 5 illustrated in FIG. 18. FIG. 12 illustrates examples of estimation models based on physical simulation for patterns 1 to 5 corresponding to FIG. 18. During exposure processing, a pattern that is closest to the conditions of the exposure processing may be selected from among the estimation models based on physical simulation. Then, the selected estimation model based on physical simulation may be used for APC feedback.

[0078] The analysis server 103 may be provided separately from the exposure apparatus 101, but may also be incorporated into the exposure apparatus 101. APC feedback may be provided from the APC 105 to the exposure apparatus 101 without going through the MES 104.

[0079] The disclosure of this specification includes the following lithography method, article manufacturing method, information processing method, and program. (Item 1) 1. A lithography method for reducing an overlay error of a pattern formed by a lithography apparatus on a substrate, comprising: an estimation step of estimating a distortion of the substrate that occurs when the substrate is held by a substrate holding part of the lithography apparatus by a simulation based on at least information related to the substrate holding part and information related to the substrate; a control step of controlling the lithography apparatus so as to reduce the overlay error based on the distortion obtained in the estimation step; 13. A lithography method comprising: (Item 2) The simulation includes a physical simulation. 2. The lithography method according to item 1, characterized in that (Item 3) The information about the substrate includes information about at least one of a shape, a size, an amount of warping, a coefficient of friction, and a bending rigidity of the substrate. 3. A lithography method according to item 1 or 2, characterized in that (Item 4) The information about the substrate holder includes information about a structure of the substrate holder. 4. The lithography method according to any one of items 1 to 3, (Item 5) The information regarding the substrate holding part includes information regarding a drive sequence of a substrate stage on which the substrate holding part is mounted. 5. A lithography method according to any one of items 1 to 4, characterized in that (Item 6) In the control step, the lithography apparatus is controlled so as to reduce an alignment error of the substrate based on the distortion obtained in the estimation step. 6. A lithography method according to any one of items 1 to 5, characterized in that (Item 7) the estimation step includes a generation step of generating an estimation model of an overlay error based on log data of the lithography apparatus and a measurement result of the overlay error, and estimating the overlay error by the estimation model and the simulation; 7. A lithography method according to any one of items 1 to 6, characterized in that (Item 8) a feedback step of feeding back a correction value based on the estimation model and the overlay error estimated by the simulation to the lithography apparatus; repeating the estimation step and the feedback step; 8. The lithography method according to item 7, characterized in that (Item 9) The generating step further includes generating the estimation model based on measurement data obtained from a pre-processing device that processes the substrate prior to the lithography device. 8. The lithography method according to item 7, characterized in that (Item 10) a feedback step of feeding back a correction value based on the estimation model and the overlay error estimated by the simulation to the lithography apparatus; repeating the estimation step and the feedback step; 10. The lithography method according to item 9, (Item 11) a lithography process for forming a pattern on a substrate; obtaining an article from the patterned substrate; The lithography process includes reducing overlay errors according to the lithography method according to any one of items 1 to 10. A method for manufacturing an article. (Item 12) 1. An information processing method for generating a correction value for reducing an overlay error of a pattern formed on a substrate by a lithographic apparatus, comprising: an estimation step of estimating a distortion of the substrate that occurs when the substrate is held by a substrate holding part of the lithography apparatus by a simulation based on at least information related to the substrate holding part and information related to the substrate; determining a correction value for controlling the lithographic apparatus based on the distortion obtained in the estimation step so as to reduce the overlay error; 13. An information processing method comprising: (Item 13) The simulation includes a physical simulation. 13. The information processing method according to item 12, (Item 14) The information about the substrate includes information about at least one of a shape, a size, an amount of warping, a coefficient of friction, and a bending rigidity of the substrate. 14. The information processing method according to item 12 or 13, (Item 15) The information about the substrate holder includes information about a structure of the substrate holder. 15. The information processing method according to any one of items 12 to 14. (Item 16) The information regarding the substrate holding part includes information regarding a drive sequence of a substrate stage on which the substrate holding part is mounted. 16. The information processing method according to any one of items 12 to 15, (Item 17) the estimation step includes a generation step of generating an estimation model of an overlay error based on log data of the lithography apparatus and a measurement result of the overlay error, and estimating the overlay error by the estimation model and the simulation; 17. The information processing method according to any one of items 12 to 16, (Item 18) a feedback step of feeding back a correction value based on the estimation model and the overlay error estimated by the simulation to the lithography apparatus; repeating the estimation step and the feedback step; 18. The information processing method according to item 17, (Item 19) The generating step further includes generating the estimation model based on measurement data obtained from a pre-processing device that processes the substrate prior to the lithography device. 20. The information processing method according to item 18, (Item 20) 20. A program for causing a computer to execute the information processing method according to any one of items 12 to 19.

[0080] The invention is not limited to the above-described embodiments, and various modifications and variations are possible without departing from the spirit and scope of the invention. Accordingly, the following claims are appended to apprise the public of the scope of the invention.

Claims

1. 1. A lithography method for reducing overlay error for a pattern on a substrate, comprising: an estimation process of estimating the overlay error using a first estimation model by physical simulation generated based on information about the substrate holding part and information about the substrate, for estimating distortion of the substrate generated when the substrate is held by a substrate holding part of a lithography apparatus, and a second estimation model by statistical analysis generated based on log data of the lithography apparatus and measurement results of the overlay error; a control step of controlling the lithography apparatus based on the overlay error obtained in the estimation step; 13. A lithography method comprising:

2. The information about the substrate includes information about at least one of a shape, a size, an amount of warping, a coefficient of friction, and a bending rigidity of the substrate. A lithographic method according to claim 1 .

3. The information about the substrate holder includes information about a structure of the substrate holder. A lithographic method according to claim 1 .

4. The information regarding the substrate holding part includes information regarding a drive sequence of a substrate stage on which the substrate holding part is mounted. A lithographic method according to claim 1 .

5. In the control step, the lithography apparatus is controlled so as to reduce an alignment error of the substrate based on the overlay error obtained in the estimation step. A lithographic method according to claim 1 .

6. generating the second estimation model based on log data of the lithography apparatus and a measurement result of the overlay error; A lithographic method according to claim 1 .

7. a feedback step of feeding back a correction value based on the overlay error obtained in the estimation step to the lithography apparatus, repeating the estimation step and the feedback step; A lithographic method according to claim 1 .

8. The generating step further includes generating the second estimation model based on measurement data obtained from a pre-processing device that processes the substrate prior to the lithography device. A lithographic method according to claim 6.

9. a feedback step of feeding back a correction value based on the overlay error obtained in the estimation step to the lithography apparatus, repeating the estimation step and the feedback step; A lithographic method according to claim 8.

10. a lithography process for forming a pattern on a substrate; obtaining an article from the patterned substrate; The lithography process includes reducing overlay errors according to a lithography method according to any one of claims 1 to 9. A method for manufacturing an article.

11. 1. An information processing method for generating a correction value for reducing an overlay error related to a pattern on a substrate, comprising the steps of: an estimation process of estimating the overlay error using a first estimation model by physical simulation generated based on information about the substrate holding part and information about the substrate, for estimating distortion of the substrate generated when the substrate is held by a substrate holding part of a lithography apparatus, and a second estimation model by statistical analysis generated based on log data of the lithography apparatus and measurement results of the overlay error; determining a correction value for controlling the lithography apparatus based on the overlay error obtained in the estimation step; 13. An information processing method comprising:

12. generating the first estimation model by simulation based on information about the substrate holder and information about the substrate; 12. The information processing method according to claim 11.

13. The information about the substrate includes information about at least one of a shape, a size, an amount of warping, a coefficient of friction, and a bending rigidity of the substrate.

12. The information processing method according to claim 11.

14. The information about the substrate holder includes information about a structure of the substrate holder.

12. The information processing method according to claim 11.

15. The information regarding the substrate holding part includes information regarding a drive sequence of a substrate stage on which the substrate holding part is mounted.

12. The information processing method according to claim 11.

16. generating the second estimation model of the overlay error based on log data of the lithography apparatus and a measurement result of the overlay error; 12. The information processing method according to claim 11.

17. a feedback step of feeding back a correction value based on the overlay error obtained in the estimation step to the lithography apparatus, repeating the estimation step and the feedback step; 17. The information processing method according to claim 16.

18. The generating step further includes generating the second estimation model based on measurement data obtained from a pre-processing device that processes the substrate prior to the lithography device.

17. The information processing method according to claim 16.

19. 1. An information processing method for estimating an overlay error relating to a pattern on a substrate, comprising the steps of: acquiring information about a substrate holder of a lithographic apparatus and about the substrate; an estimation process of estimating the overlay error using a first estimation model by physical simulation generated based on information about the substrate holding part and information about the substrate, and a second estimation model by statistical analysis generated based on log data of the lithography apparatus and measurement results of the overlay error, for estimating distortion of the substrate generated when the substrate is held by the substrate holding part; 13. An information processing method comprising:

20. A program for causing a computer to execute the information processing method according to any one of claims 11 to 19.

21. An information processing apparatus for generating a correction value for reducing an overlay error related to a pattern on a substrate, comprising: estimating the overlay error using a first estimation model by physical simulation generated based on information about the substrate holding part and information about the substrate, for estimating distortion of the substrate that occurs when the substrate is held by a substrate holding part of a lithography apparatus, and a second estimation model by statistical analysis generated based on log data of the lithography apparatus and measurement results of the overlay error; a processor for determining a correction value for controlling the lithographic apparatus based on the estimated overlay error.

23. An information processing apparatus comprising:

22. An information processing apparatus for estimating an overlay error relating to a pattern on a substrate, comprising: obtaining information about a substrate holder of a lithographic apparatus and information about the substrate; a processing unit that estimates the overlay error using a first estimation model by physical simulation generated based on information about the substrate holding part and information about the substrate, and a second estimation model by statistical analysis generated based on log data of the lithography apparatus and measurement results of the overlay error, in order to estimate a distortion of the substrate that occurs when the substrate is held by the substrate holding part, 23. An information processing apparatus comprising:

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