Information processing apparatus, information processing method, program, pattern forming apparatus, and article manufacturing method

Abstract

To provide a technique advantageous for efficiently determining an optimum condition of a pattern forming apparatus.SOLUTION: An information processing apparatus includes: acquisition means for acquiring a target value of a performance index of pattern formation; estimation means for determining a plurality of sample shot areas from among a plurality of shot areas on a substrate based on the target value acquired by the acquisition means for each of a plurality of regression models, and applying the regression models to the determined plurality of sample shot areas to correct the estimated arrangement of the plurality of shot areas and estimate an evaluation value of the performance index when pattern formation is performed; and control means for displaying the estimated evaluation values for each of the plurality of regression models on a display unit in a comparable format.SELECTED DRAWING: Figure 6

Description

[Technical Field]

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

[0002] A lithography apparatus is one type of pattern forming apparatus that forms a pattern on a substrate. A lithography apparatus (e.g., an exposure apparatus) can transfer a pattern from an original onto multiple layers on a substrate, overlaying them. To overlay each layer with high precision, each shot area on the substrate must be aligned with respect to the original. Alignment can be performed, for example, by detecting alignment marks arranged for each shot area on the substrate, and then based on position information of the alignment marks obtained by the detection and position information of the pattern on the original.

[0003] To achieve highly accurate alignment, it would be ideal to detect alignment marks for all shot areas on the substrate. However, this is not realistic from the viewpoint of productivity. Therefore, a global alignment method is generally adopted for aligning all shot areas on the substrate with the original (see Patent Documents 1 and 2).

[0004] The global alignment method assumes that the relative positions of all shot areas on a substrate can be expressed as a function of the position coordinates of the shot areas. Based on this assumption, alignment marks are measured only for some shot areas (sample shot areas) among the multiple shot areas on the substrate. Next, parameters of the function model are estimated using regression analysis-like statistical calculation processing from the assumed function model and the results of the position measurement. Then, using the estimated parameters and function model, the position coordinates of each shot area in the stage coordinate system are calculated and alignment is performed. The global alignment method generally uses a polynomial model with stage coordinates as variables. Scaling, rotation, uniform offset, and the like, which are first-order polynomials of the stage coordinates, are mainly used (see Patent Document 3).

[0005] A method has also been proposed that uses a regression model that takes into account the higher-order components of the arrangement of shot areas on a substrate as parameters (see Patent Document 4).Furthermore, a method has also been proposed in which multiple sample points are measured in advance, coefficients are selected using that data and a regression model with a regularization term, and the selected coefficients are used to calculate position information of the shot areas. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Unexamined Patent Publication No. 61-44429 [Patent Document 2] Japanese Patent Application Publication No. 62-84516 [Patent Document 3] Japanese Patent Application Publication No. 6-349705 [Patent Document 4] Patent No. 3230271 Summary of the Invention [Problem to be solved by the invention]

[0007] To perform alignment correction with high accuracy and high throughput, it is necessary to repeatedly test multiple regression models while changing the sample shot arrangement on the board to determine the regression model and sample shot arrangement that are optimal for the product. While there are conventional methods that automatically determine the optimal conditions for a product (optimal conditions) from multiple regression models, these methods do not allow users to intuitively confirm the improvement effect of each condition, making it difficult to determine the appropriateness of changing conditions. Furthermore, these conventional methods make it difficult to efficiently select the optimal conditions from a combination of multiple regression models and multiple sample shot arrangements.

[0008] The present invention provides an advantageous technique for efficiently determining the optimum conditions for a patterning device. [Means for solving the problem]

[0009] According to one aspect of the present invention, there is provided an information processing apparatus comprising: acquisition means for acquiring a target value of a performance index of pattern formation; estimation means for determining a plurality of sample shot areas from among a plurality of shot areas on a substrate based on the target value acquired by the acquisition means for each of a plurality of regression models, and applying the regression model to the determined plurality of sample shot areas to correct the estimated arrangement of the plurality of shot areas to estimate an evaluation value of the performance index when pattern formation is performed; and control means for displaying the estimated evaluation value for each of the plurality of regression models on a display unit in a comparable form. [Effects of the Invention]

[0010] According to the present invention, it is possible to provide an advantageous technique for efficiently determining the optimum conditions for a pattern forming apparatus. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 2 is a diagram showing the configuration of an exposure apparatus and an information processing apparatus. [Figure 2] FIG. 2 is a diagram showing the configuration of an alignment optical system. [Figure 3] 10 is a flowchart of an exposure process. [Figure 4] FIG. 10 is a diagram showing an example of a sample shot area. [Figure 5] 10 is a graph illustrating the relationship between the number of sample points and the correction residual; [Figure 6] FIG. 10 is a diagram showing an example of a confirmation screen for correction residuals. [Figure 7] FIG. 10 is a diagram showing an example of a UI screen. [Figure 8] FIG. 10 is a diagram showing an example of a UI screen. [Figure 9] FIG. 10 is a diagram showing an example of a UI screen. [Figure 10] 10 is a flowchart showing the procedure of an optimization process for an exposure recipe. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. Note that the following embodiments do not limit the scope of the invention claimed. Although multiple features are described in the embodiments, not all of these multiple features are necessarily essential to the invention, and multiple features may be combined arbitrarily. Furthermore, in the accompanying drawings, the same reference numerals are used to designate the same or similar components, and redundant explanations will be omitted.

[0013] First Embodiment The present disclosure relates to an information processing apparatus, an information processing method, a program, a pattern forming apparatus, and an article manufacturing method. A pattern forming apparatus is an apparatus that forms a pattern on a substrate, and a lithography apparatus such as an exposure apparatus is one type of pattern forming apparatus. In the following embodiments, an example related to an exposure apparatus, which is an example of a lithography apparatus, will be described.

[0014] 1 is a diagram showing the configurations of an exposure apparatus 1 and a simulation apparatus 50 according to an embodiment. The exposure apparatus 1 is a type of lithography apparatus used in the manufacturing process of devices such as semiconductor elements. In this embodiment, the exposure apparatus 1 projects a pattern of an original 2 (reticle or mask) onto a substrate 4 via a projection optical system 3, thereby exposing the substrate 4 to light.

[0015] 1, the exposure apparatus 1 has a projection optical system 3 that projects (reduced projection) a pattern formed on an original 2, and a chuck 5 that holds a substrate 4 on which a base pattern and alignment marks have been formed in a previous process. The exposure apparatus 1 also has a substrate stage 6 that holds the chuck 5 and positions the substrate 4 at a predetermined position, an alignment optical system 7 that measures the position of the alignment marks provided on the substrate 4, a control unit CN, a memory unit SU, and a display unit D.

[0016] The control unit CN is configured, for example, by a computer (information processing device) including a CPU, memory, etc., and performs overall control of each unit of the exposure apparatus 1 in accordance with programs stored in the storage unit SU, etc. In this embodiment, the control unit CN not only controls the exposure process in which the substrate 4 is exposed via the original 2, but also functions as a processing unit that determines the arrangement (shot arrangement) of multiple shot areas (multiple areas on the substrate) on the substrate. The display unit D displays a user interface screen (UI screen) that shows the settings, status, etc. of the exposure process. The control unit CN can also function as a display control unit that controls the display of the UI screen.

[0017] The storage unit SU stores programs and various information (data) required to control the various parts of the exposure apparatus 1 and perform the exposure process of exposing the substrate 4. The storage unit SU also stores programs and various information (data) required for the control unit CN to determine the arrangement of the sample shot areas (sample shot array). The control unit CN, storage unit SU, and display unit D may be configured as devices external to the exposure apparatus 1. For example, an information processing device including the control unit CN, storage unit SU, and display unit D may be configured as a server device that manages the exposure apparatus 1. The control unit CN is also connected to an input device (mouse, keyboard, etc.) (not shown) that is operated by the user. The storage unit SU may be a semiconductor memory, a disk such as a hard disk, or another form of memory. The program for determining the arrangement of the sample shot areas may be stored in a computer-readable memory medium, or may be provided to the information processing device via communication facilities such as an electric communication line.

[0018] The simulation apparatus 50 (information processing device) may be configured, for example, by incorporating a simulation program 55 into a general-purpose or dedicated computer. Alternatively, the simulation apparatus 50 may be configured by 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). In one example, the simulation apparatus 50 may be configured by preparing a computer including a processor 51, a memory 52, a display 53 (display unit), and an input device 54, and storing the simulation program 55 in the memory 52. ​​The memory 52 may be a semiconductor memory, a disk such as a hard disk, or another form of memory. The input device 54 may include a mouse, a keyboard, etc. The simulation program 55 may be stored in a computer-readable memory medium or provided to the simulation apparatus 50 via communication facilities such as a telecommunications line.

[0019] 1, simulation apparatus 50 is configured as an external device of exposure apparatus 1. Alternatively, the functions of simulation apparatus 50 may be realized in exposure apparatus 1. In that case, in exposure apparatus 1, an information processing device including control unit CN, memory unit SU, and display unit D can be configured as the simulation apparatus.

[0020] 2 is a schematic diagram showing the configuration of the alignment optical system 7. The alignment optical system 7 has a function of optically detecting marks assigned to each shot area of ​​the substrate 4 and acquiring position measurement data, and in this embodiment includes a light source 8, a beam splitter 9, lenses 10 and 13, and a sensor 14.

[0021] Light from light source 8 is reflected by beam splitter 9 and passes through lens 10 to illuminate alignment mark 11 or 12 provided on substrate 4. Light diffracted by alignment mark 11 or 12 passes through lens 10, beam splitter 9, and lens 13 and is received by sensor 14.

[0022] The exposure process in the exposure apparatus 1 will be described with reference to FIG. 3. Here, an overview of the steps from aligning the substrate 4 to exposing it will be described. In S101, the substrate 4 is loaded into the exposure apparatus 1. In S102, the control unit CN performs pre-alignment. Specifically, the control unit CN detects alignment marks 11 for pre-alignment provided on the substrate 4 using the alignment optical system 7, and roughly determines the position of the substrate 4. At this time, detection of the alignment marks 11 is performed for multiple shot areas on the substrate 4, and the overall shift and first-order linear components (magnification and rotation) of the substrate 4 are determined.

[0023] In S103, the control unit CN performs fine alignment. Specifically, based on the results of the pre-alignment, the control unit CN first drives the substrate stage 6 to a position where the alignment marks 12 for fine alignment provided on the substrate 4 can be detected by the alignment optical system 7. The control unit CN then detects the alignment marks 12 provided in each of the multiple shot areas on the substrate 4 using the alignment optical system 7, and precisely calculates the overall shift and first-order linear components (magnification and rotation) of the substrate 4. At this time, by calculating the positions of multiple shot areas, it is also possible to precisely calculate higher-order deformation components of the substrate 4. This makes it possible to calculate the precise position of each shot area on the substrate 4, i.e., the shot arrangement.

[0024] In S104, the control unit CN exposes the substrate 4. Specifically, after fine alignment is performed, the control unit CN transfers the pattern of the original 2 onto each shot area of ​​the substrate 4 via the projection optical system 3. In S105, the substrate 4 is unloaded from the exposure apparatus 1.

[0025] In this embodiment, if distortion occurs in the substrate 4, higher-order deformation components are corrected in the fine alignment of S103. The correction in the exposure apparatus 1 can be achieved by driving the substrate stage 6, driving the original stage (not shown) that holds the original 2, driving the optical elements of the projection optical system 3, or the like. For example, a fifth-order polynomial model can be used as a regression model for estimating the shot arrangement. However, the regression model is not limited to this. A model of any order can be used as the regression model. Models other than polynomials (trigonometric function models and logarithmic models) may also be used. In this embodiment, a simulation is performed for multiple regression models using the simulation apparatus 50, and the user can check the results and determine an appropriate regression model.

[0026] When the deformation of the substrate is represented by a fifth-order polynomial model, the positional shift (ShiftX, ShiftY) of each shot area is expressed by the following equation (1). Note that the positional shift of each shot area may be understood as a correction value for correcting such positional shift.

[0027]

number

[0028] In formula (1), x and y represent the position of the shot area on the substrate 4. Based on the actual position measurement data of each shot area on the substrate 4, k1 to k 42 Then, based on the equation (1) in which the coefficients have been determined, the positional deviation (correction value) of each shot area is calculated.

[0029] In the global alignment method, alignment measurement is performed in sample shot areas, which are some of the multiple shot areas on a substrate. An example of a sample shot area is shown in FIG. 4. In FIG. 4, 14 sample shot areas are set on the substrate as an example. In the global alignment method, alignment optical system 7 is used to detect alignment marks 12 arranged for each of these sample shot areas.

[0030] FIG. 5 is a graph showing an example of the relationship between the number of sample shot areas (number of sample points) in a certain device and the correction residual, which is an index of correction accuracy. The correction residual refers to an arrangement error that cannot be completely removed by correction when the arrangement of multiple shot areas on a substrate is corrected using multiple determined sample shot areas. From the perspective of correction accuracy (measurement accuracy), it is desirable to have a large number of sample shot areas that are the subject of actual measurement. However, increasing the number of sample shot areas can be disadvantageous in terms of measurement throughput. The number of sample shot areas is determined appropriately in light of this trade-off relationship between correction accuracy and throughput.

[0031] Furthermore, the optimal number and arrangement of sample shot areas (hereinafter referred to as "sample shot arrangement") differs depending on the substrate processing process and device characteristics. Therefore, the optimal sample shot arrangement differs depending on the alignment accuracy and productivity (throughput) required for each device.

[0032] 6 is a diagram showing an example of a correction residual confirmation screen 60, which is displayed on display 53 of simulation apparatus 50, in relation to the determination of the sample shot arrangement. Confirmation screen 60 can include a graph display screen 61 showing the relationship between the number of sample points, the correction residual, and productivity, and a wafer map display screen 62 showing the results of the arrangement of sample shot areas on the substrate.

[0033] The graph display screen 61 displays a graph showing the transition of the correction residual (first vertical axis) against the number of sample points (horizontal axis) and a graph showing the transition of productivity (second vertical axis) against the number of sample points. Here, productivity (throughput) is represented by the number of substrates processed per unit time (wph). By viewing the graph displayed on the graph display screen 61, the user can find a number of sample points that allows a compromise between the correction residual (accuracy) and productivity. In one example, the processor 51 determines a recommended value for the number of sample points (initial number of sample points) based on the relationship between the number of sample points, the correction residual, and the productivity. Thereafter, the processor 51 tentatively determines the arrangement of sample shot areas according to the initial number of sample points in accordance with a predetermined selection algorithm. A known selection algorithm can be used as the selection algorithm. The selection algorithm is an algorithm that performs selection under predetermined constraints, such as arranging a set number of sample shot areas as evenly (without bias) as possible symmetrically about the substrate center, or arranging as many sample shot areas as possible on the outer periphery of the substrate. There may be cases where a further constraint is imposed that excludes shot areas at the outermost periphery of the substrate from the selection targets.

[0034] Thereafter, the processor 51 determines the sample shot arrangement for the provisionally determined arrangement of the sample shot areas by a method using a regression model (correction model). The processor 51 controls the confirmation screen 60 so that the determined sample shot arrangement is displayed on the wafer map display screen 62.

[0035] The number of sample points can be changed by user operation. For example, when the mouse pointer is placed over the graph on the graph display screen 61, a partial area 101 including the position of the mouse pointer is displayed in a specific color due to a rollover effect. When the mouse is clicked in this state, the number of sample points corresponding to the partial area 101 is set. In this way, the number of sample shot areas can be specified by the user. That is, in this embodiment, the graph display screen 61 is also a specification screen on which the user specifies the number of sample shot areas.

[0036] Wafer map display screen 62 is a display screen that displays information about the position of each of a plurality of sample shot areas on the substrate. In response to the designation or change of the number of sample points, processor 51 redetermines the arrangement of the shot areas. Processor 51 then updates the display so that the redetermined sample shot arrangement is displayed on wafer map display screen 62. FIG. 6 shows the results when 16 sample points are set. In this way, the expected correction accuracy and productivity, as well as the optimal sample shot arrangement, are displayed for the number of sample points arbitrarily designated by the user. This allows the user to easily confirm the effect of adjusting the parameters for determining the sample shot areas.

[0037] Then, information on the correction residual for each shot area is displayed on the wafer map display screen 62. For example, as shown in FIG. 6, the correction residual for each shot area may be displayed on the wafer map display screen 62 using arrows indicating the direction and magnitude in the XY plane (substrate surface). This allows the user to easily check the correction effect for each shot area. Hereinafter, the display using arrows indicating the direction and magnitude in the XY plane will also be referred to as a "vector display." Furthermore, the display of the direction and magnitude of the correction residual for each shot area is not limited to the display format shown in FIG. 6. For example, the magnitude of the correction residual for each shot area in the X and Y directions may be displayed using arrows or numerical values. Note that FIG. 6 shows an example of one confirmation screen for one specific regression model. Multiple confirmation screens may be provided for multiple regression models.

[0038] According to the example in Figure 6, the user can check the correction results based on the calculated conditions, but it is difficult to check the improvement effect compared to the conventional conditions or other regression models.

[0039] In the following, an embodiment including a display that allows visual confirmation of the improvement effect will be described.

[0040] 7 is a diagram showing an example of a user interface (UI) screen 70 displayed on the display 53 in this embodiment. The UI screen 70 can include a correction setting screen 100, a result confirmation screen 110, and a detailed comparison screen 120.

[0041] The correction setting screen 100 is a screen for setting and checking correction conditions. The correction setting screen 100 includes a lot selection section 101. In the lot selection section 101, the user can select a lot to be corrected.

[0042] The correction setting screen 100 includes a regression model selection section 102. In the regression model selection section 102, the user can select one or more regression models to be used for calculation from among a plurality of regression models A to F prepared in advance. The plurality of regression models can include, for example, a cubic polynomial model, a quartic polynomial model, a quintic polynomial model, a Zernike polynomial model, a Gaussian function model, etc., as regression models for estimating shot arrangements.

[0043] The correction setting screen 100 further includes a target value setting unit 104 for setting a target value for each performance index of pattern formation. A user can input target values ​​for one or more of a plurality of performance indexes (e.g., throughput, correction residual (average), correction residual (edge-weighted average), etc.) into the target value setting unit 104. In this way, the correction setting screen 100 (target value setting unit 104) functions as an acquisition unit for acquiring target values ​​of performance indexes of pattern formation.

[0044] The correction setting screen 100 further includes a performance index selection unit 103 that selects a prioritized performance index from among the multiple performance indexes. In the example of Fig. 7, the performance index selection unit 103 is realized by a radio button provided for each performance index, and a state in which "throughput" is selected is shown. In this way, the correction setting screen 100 (performance index selection unit 103), which is an acquisition means, can further acquire a priority designation for one of the multiple performance indexes.

[0045] A simulate button 106 is provided at the bottom of the correction setting screen 100. In response to a user clicking the simulate button 106, the processor 51 determines a plurality of sample shot areas from among a plurality of shot areas on the substrate for each of a plurality of regression models selected by the regression model selection unit 102. Specifically, the processor 51 determines a plurality of sample shot areas for each of a plurality of regression models selected as calculation targets by the regression model selection unit 102 based on the target values ​​input to the target value setting unit 104. The processor 51 then estimates arrangement errors of the plurality of shot areas based on the determined plurality of sample shot areas. The processor 51 then estimates evaluation values ​​of each performance index when pattern formation is performed (by simulation) by correcting the arrangement of the plurality of shot areas obtained by estimation. The processor 51 then displays the calculation results (evaluation values ​​for each regression model) on a result display screen 110 on the display 53. The result display screen 110 displays the evaluation values ​​of each performance index obtained by estimation for each regression model. At this time, for regression models that do not satisfy the target performance, the evaluation value may not be displayed or may be displayed with an annotation (for example, an * mark).

[0046] The result display screen 110 displays the evaluation values ​​of each performance index estimated for each regression model in a comparable format. For example, as shown in FIG. 7, the result display screen 110 displays a list of multiple estimated evaluation values ​​for each regression model. Among the multiple performance indexes on the result display screen 110, a performance index selected by the performance index selection unit 103 (i.e., a performance index designated as a priority) is marked with a priority mark 114. In the example of FIG. 7, since "throughput" is selected by the performance index selection unit 103, the priority mark 114 is marked on "throughput" on the result display screen 110. The processor 51 sorts the list so that the evaluation values ​​of the performance indexes designated as a priority are arranged in descending order (ascending or descending order), and displays the list on the display 53. For example, if the performance index designated as a priority is throughput, which indicates the number of substrates processed per unit time, the list is sorted so that the evaluation values ​​are arranged in descending order.

[0047] The result display screen 110 has a comparison and selection section 112. In the comparison and selection section 112, the user can select two regression models to be compared (subjects to detailed comparison). In this way, the comparison and selection section 112 can further accept the designation of two regression models to be compared from the list.

[0048] A detailed comparison button 115 is provided at the bottom of result display screen 110. In response to the user clicking detailed comparison button 115, information on the positions on the substrate of the plurality of sample shot areas determined for each of the two regression models, and information on the correction residuals of the plurality of shot areas are further displayed on display 53. For example, processor 51 displays wafer maps 121 and 122 based on the two regression models selected in comparison and selection unit 112 on detailed comparison screen 120. Wafer maps 121 and 122 display the sample shot arrangements obtained by calculation, and also display information on the correction residuals of each shot area, for example, as a vector display.

[0049] Processor 51 may further display information on the difference in the correction residual between the two regression models for each of the multiple shot areas on display 53. For example, processor 51 may further display, on detailed comparison screen 120, difference map 123 indicating the difference (improvement effect) between the correction residual for each shot area shown in wafer map 121 and the correction residual for each shot area shown in wafer map 122. In FIG. 6, difference map 123 displays the magnitude of the difference between the correction residual for each shot area shown in wafer map 121 and the correction residual for each shot area shown in wafer map 122 using color, more specifically, a gradation of color. However, difference map 123 may display the magnitude of the difference between the correction residual for each shot area shown in map 121 and the correction residual for each shot area shown in wafer map 122 using a type of color (hue) instead of a gradation of color. For example, when the number of sample points is changed, shot areas in which the correction residual is improved are displayed in blue, and shot areas in which the correction residual is not improved are displayed in red.

[0050] In this way, the user checks the wafer maps 121, 122 and the difference map 123 displayed on the detailed comparison screen 120. After this check, the user finally selects one regression model in the comparison and selection unit 112 and clicks the confirm button 116 to confirm the regression model to be applied.

[0051] Next, with reference to FIG. 8 , an example of preferentially displaying regression models with good correction residuals will be described. In FIG. 8 , “correction residual (average)” is selected by the performance index selection unit 103. In response to the user clicking the simulate button 106 in this state, the processor 51 determines a sample shot arrangement and calculates each evaluation value for each regression model selected as a calculation target by the regression model selection unit 102. Thereafter, the processor 51 displays the calculation results (evaluation value for each regression model) on the result display screen 110. The result display screen 110 displays evaluation values ​​calculated to satisfy the set target performance. Here, a priority mark 114 is attached to the target performance selected by the performance index selection unit 103 among the multiple target performances on the result display screen 110. In the example of FIG. 8 , “correction residual (average)” is selected by the performance index selection unit 103, and therefore a priority mark 114 is attached to “correction residual (average)” on the result display screen 110. Therefore, processor 51 sorts the list in ascending order of the corrected residual (average) and displays it on result display screen 110. This makes it easy to select a regression model with priority given to the corrected residual.

[0052] Next, with reference to FIG. 9 , an example of preferentially displaying regression models with good overall evaluation scores for multiple performances will be described. In FIG. 9 , the target value selection unit 103 selects a “score,” which indicates an overall evaluation score using multiple performance indicators (including, for example, throughput and correction residual). In this case, the user can specify an evaluation formula for calculating the score in the evaluation formula setting unit 105. In response to the user clicking the simulate button 106 in this state, the processor 51 determines a sample shot arrangement and calculates each evaluation value for each regression model selected as a calculation target in the regression model selection unit 102. Thereafter, the processor 51 displays the calculation results (evaluation values ​​for each regression model) on the result display screen 110. The result display screen 110 displays evaluation values ​​calculated to satisfy the set target performance. Here, a priority mark 114 is attached to the performance indicator selected by the performance indicator selection unit 103 among the multiple performance indicators on the result display screen 110. 9, since "score" is selected in the performance index selection unit 103, a priority mark 114 is attached to "score" on the result display screen 110. Therefore, the processor 51 sorts the list so that the score evaluation values ​​are in descending order, and displays the list on the result display screen 110.

[0053] 6 shows an example of displaying a difference map 123 regarding correction residuals. The correction residuals used here may be correction residuals calculated based on statistical values ​​(e.g., average values) of position measurement data obtained from each of a plurality of substrates for each of a plurality of different conditions (e.g., a plurality of different sample shot arrangements). Alternatively, it is also possible to display the correction residuals of each substrate under the same conditions, predicted from the position measurement data obtained from each of the plurality of substrates, and the differences therebetween, as a difference map.

[0054] Although the above example describes an example of evaluating the correction residual (improvement effect) for a sample shot arrangement in fine alignment, the present invention is not limited thereto. For example, the present invention can also be applied to evaluating the vertical positional deviation (improvement effect) of each shot area relative to a focus control parameter for controlling the vertical position of a substrate when exposing the substrate. Specifically, the processor 51 predicts the vertical positional deviation of each shot area before and after changing the focus control parameter. Then, the processor 51 generates and displays a map showing the vertical positional deviation of each shot area corresponding to each of the focus control parameters before and after the change. The processor 51 also calculates the difference in the predicted vertical positional deviation for each shot area for each of the focus control parameters before and after the change, and generates and displays a difference map.

[0055] This allows the user to easily check the change in vertical positional deviation (improvement effect) for each focus control parameter (condition) for each of multiple shot areas on the substrate, and determine the validity of the focus control parameters.

[0056] A specific example of the optimization process for the exposure recipe will be described with reference to Fig. 10. Fig. 10 is a flowchart showing the procedure (simulation method) of the optimization process for the exposure recipe.

[0057] In S1, the processor 51 selects an exposure recipe (hereinafter simply referred to as a "recipe") to be optimized. The recipe is a set of various control parameters that define the exposure conditions, the substrate processing procedure, etc. The processor 51 will perform the exposure process in accordance with the recipe.

[0058] In S2, processor 51 selects a regression model to be compared (regression model). This can be performed by the user selecting one or more regression models in regression model selection section 102 of correction setting screen 70 shown in FIG.

[0059] In S3, the processor 51 acquires the target value by obtaining the target value input to the target value setting unit 104 by the user.

[0060] In S4, the processor 51 designates a performance index to be prioritized. This is done by the user selecting the performance index in the performance index selection unit 103.

[0061] In response to the user clicking the simulate button 106, in S5 the processor 51 calculates (estimates) each evaluation value for each regression model selected by the regression model selection unit 102, and determines a sample shot arrangement.

[0062] In S6, processor 51 displays the calculation results (evaluation values ​​for each regression model) on result display screen 110. Processor 51 sorts the list so that the evaluation values ​​of the performance indexes specified in S4 are in ascending or descending order, and displays the list on result display screen 110.

[0063] In S7, the user can select two regression models to be subjected to detailed comparison in comparison and selection section 112. Thereafter, in response to the user clicking detailed comparison button 115, processor 51 displays wafer maps 121, 122 and difference map 123 based on the two regression models selected in comparison and selection section 112 on detailed comparison screen 120. In S8, the user can perform a detailed comparison of the two regression models using wafer maps 121, 122 and difference map 123 displayed on detailed comparison screen 120.

[0064] In S9, processor 51 selects a regression model to be applied. This is performed by the user finally selecting one regression model in comparison and selection section 112 and clicking confirmation button 116.

[0065] In S10, the processor 51 updates the recipe selected in S1 based on the simulation results when the regression model selected in S9 is applied.

[0066] According to the above-described processing procedure, the optimum conditions for the exposure apparatus can be determined efficiently.

[0067] <Embodiment of an article manufacturing method> The article manufacturing method according to an embodiment of the present invention is suitable for manufacturing articles such as microdevices, such as semiconductor devices, and elements having microstructures. The article manufacturing method according to this embodiment includes a step of transferring a pattern of an original onto a substrate using the above-described lithography apparatus (such as an exposure apparatus, imprint apparatus, or drawing apparatus), and a step of processing the substrate onto which the pattern has been transferred. Furthermore, this manufacturing method includes other well-known steps (such as oxidation, film formation, vapor deposition, doping, planarization, etching, resist stripping, dicing, bonding, and packaging). The article manufacturing method according to this embodiment is advantageous over conventional methods in at least one of the performance, quality, productivity, and production cost of the article.

[0068] <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.

[0069] The disclosure of the present specification includes at least the following techniques. (Item 1) an acquisition means for acquiring a target value of a performance index of pattern formation; an estimation means for determining a plurality of sample shot areas from among a plurality of shot areas on a substrate based on the target value acquired by the acquisition means for each of a plurality of regression models, and applying the regression models to the determined plurality of sample shot areas to correct an estimated arrangement of the plurality of shot areas and estimate an evaluation value of the performance index when pattern formation is performed; a control means for displaying the estimated evaluation values ​​for each of the plurality of regression models on a display unit in a comparable format; An information processing device comprising: (Item 2) the acquiring means acquires target values ​​for each of a plurality of performance indexes; the estimation means estimates an evaluation value of each of the plurality of performance indexes; the control means causes the display unit to display the estimated evaluation values ​​for each of the regression models. 2. The information processing device according to item 1, (Item 3) 3. The information processing apparatus according to item 2, wherein the plurality of performance indexes include a throughput and a correction residual when the arrangement of the plurality of shot areas is corrected. (Item 4) 4. The information processing device according to item 3, wherein the plurality of performance indexes further include a comprehensive evaluation score using the throughput and the correction residual. (Item 5) The acquiring means further acquires a designation of an evaluation formula for calculating the overall evaluation score, the control means calculates the overall evaluation score using the evaluation formula specified by the acquisition means and displays it on the display unit; 5. The information processing device according to item 4. (Item 6) 6. The information processing device according to any one of items 3 to 5, wherein the control means causes the display unit to display the estimated evaluation values ​​in a list for each of the regression models. (Item 7) the acquiring means further acquires a priority designation for any of the plurality of performance indexes; the control means sorts the list so that the evaluation values ​​of the performance indexes that have been prioritized are in ascending or descending order, and causes the list to be displayed on the display unit. 7. The information processing device according to item 6, (Item 8) the acquiring means further acquires designation of two regression models to be compared from the list displayed on the display unit; the control means further causes the display unit to display, for each of the two regression models, information on positions on the substrate of a plurality of sample shot areas determined, and information on the correction residuals of the plurality of shot areas. 8. The information processing device according to item 6 or 7, (Item 9) 9. The information processing device according to item 8, wherein the control means further causes the display unit to display information on the difference in the correction residual between the two regression models in each of the plurality of shot regions. (Item 10) an acquisition step of acquiring a target value of a performance index for pattern formation; an estimation step of determining a plurality of sample shot areas from among a plurality of shot areas on a substrate based on the target value acquired in the acquisition step for each of a plurality of regression models, and applying the regression models to the determined plurality of sample shot areas to correct an estimated arrangement of the plurality of shot areas and estimating an evaluation value of the performance index when pattern formation is performed; a control step of displaying the estimated evaluation values ​​for each of the plurality of regression models on a display unit in a comparable format; An information processing method comprising: (Item 11) A program for causing a computer to function as each of the means in the information processing device according to any one of items 1 to 9. (Item 12) A pattern forming apparatus for forming a pattern on a substrate, an acquisition means for acquiring a target value of a performance index of pattern formation; an estimation means for determining a plurality of sample shot areas from among a plurality of shot areas on a substrate based on the target value acquired by the acquisition means for each of a plurality of regression models, and applying the regression models to the determined plurality of sample shot areas to correct an estimated arrangement of the plurality of shot areas and estimate an evaluation value of the performance index when pattern formation is performed; a control means for displaying the estimated evaluation values ​​for each of the plurality of regression models on a display unit in a comparable format; A pattern forming apparatus comprising: (Item 13) Forming a pattern on a substrate using the pattern forming apparatus according to item 12; processing the substrate on which the pattern is formed; and A method for manufacturing an article, comprising manufacturing an article from the processed substrate.

[0070] The invention is not limited to the above-described embodiments, and various changes and modifications can be made 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. [Explanation of symbols]

[0071] 1: exposure device, 2: original, 3: projection optical system, 4: substrate, 6: substrate stage, 7: alignment optical system, CN: control unit, 50: simulation device, 51: processor, 53: display

Claims

1. an acquisition means for acquiring a target value of a performance index of pattern formation; an estimation means for determining a plurality of sample shot areas from among a plurality of shot areas on a substrate based on the target value acquired by the acquisition means for each of a plurality of regression models, and applying the regression models to the determined plurality of sample shot areas to correct an estimated arrangement of the plurality of shot areas and estimate an evaluation value of the performance index when pattern formation is performed; a control means for displaying the estimated evaluation values ​​for each of the plurality of regression models on a display unit in a comparable format; An information processing device comprising:

2. the acquiring means acquires target values ​​for each of a plurality of performance indexes; the estimation means estimates an evaluation value of each of the plurality of performance indexes; the control means causes the display unit to display the estimated evaluation values ​​for each of the regression models.

2. The information processing apparatus according to claim 1, wherein:

3. 3. The information processing apparatus according to claim 2, wherein the plurality of performance indexes include a throughput and a correction residual when the arrangement of the plurality of shot areas is corrected.

4. 4. The information processing apparatus according to claim 3, wherein the plurality of performance indices further includes a comprehensive evaluation score using the throughput and the correction residual.

5. The acquiring means further acquires a designation of an evaluation formula for calculating the overall evaluation score, the control means calculates the overall evaluation score using the evaluation formula specified by the acquisition means and displays it on the display unit; 5. The information processing apparatus according to claim 4,

6. 4. The information processing apparatus according to claim 3, wherein the control means causes the display unit to display a list of the estimated evaluation values ​​for each of the regression models.

7. the acquiring means further acquires a priority designation for any of the plurality of performance indexes; the control means sorts the list so that the evaluation values ​​of the performance indexes that have been prioritized are in ascending or descending order, and causes the list to be displayed on the display unit.

7. The information processing apparatus according to claim 6,

8. the acquiring means further acquires designation of two regression models to be compared from the list displayed on the display unit; the control means further causes the display unit to display, for each of the two regression models, information on positions on the substrate of a plurality of sample shot areas determined, and information on the correction residuals of the plurality of shot areas.

7. The information processing apparatus according to claim 6,

9. 9. The information processing apparatus according to claim 8, wherein the control means further causes the display unit to display information on a difference in the correction residual between the two regression models for each of the plurality of shot regions.

10. an acquisition step of acquiring a target value of a performance index for pattern formation; an estimation step of determining a plurality of sample shot areas from among a plurality of shot areas on a substrate based on the target value acquired in the acquisition step for each of a plurality of regression models, and applying the regression models to the determined plurality of sample shot areas to correct an estimated arrangement of the plurality of shot areas and estimating an evaluation value of the performance index when pattern formation is performed; a control step of displaying the estimated evaluation values ​​for each of the plurality of regression models on a display unit in a comparable format; An information processing method comprising:

11. A program for causing a computer to function as each of the means in the information processing device according to any one of claims 1 to 9.

12. A pattern forming apparatus for forming a pattern on a substrate, an acquisition means for acquiring a target value of a performance index of pattern formation; an estimation means for determining a plurality of sample shot areas from among a plurality of shot areas on a substrate based on the target value acquired by the acquisition means for each of a plurality of regression models, and applying the regression models to the determined plurality of sample shot areas to correct an estimated arrangement of the plurality of shot areas and estimate an evaluation value of the performance index when pattern formation is performed; a control means for displaying the estimated evaluation values ​​for each of the plurality of regression models on a display unit in a comparable format; A pattern forming apparatus comprising:

13. forming a pattern on a substrate using the pattern forming apparatus according to claim 12; processing the substrate on which the pattern is formed; and A method for manufacturing an article, comprising manufacturing an article from the processed substrate.

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