Information processing apparatus, information processing method, storage medium, exposure method, substrate processing apparatus, and method of manufacturing article

Abstract

The invention relates to an information processing apparatus, an information processing method, a storage medium, an exposure method, a substrate processing apparatus, and a method of manufacturing an article. The present invention is provided with a processing unit that generates a regression model using a first value based on a measurement result of a position of a mark provided in each of a plurality of imaging regions on a first substrate, and generates a regression model on the basis of a second value based on a measurement result of a position of a mark provided in a sample imaging region among the plurality of imaging regions on a second substrate and the regression model. The amount of alignment for alignment of a plurality of non-sample imaging regions different from the sample imaging region among the plurality of imaging regions on the second substrate is calculated, and the number of the plurality of imaging regions on the first substrate for measuring the position of the mark is greater than the number of the sample imaging regions. The regression model comprises regression models respectively corresponding to the plurality of non-sample shooting areas.

Description

Technical Field

[0001] The present invention relates to an information processing device, an information processing method, a storage medium, an exposure method, a substrate processing device and a method for manufacturing an article. Background Art

[0002] In the manufacturing process of semiconductor devices, liquid crystal display devices, and the like, there is a case where an alignment amount (correction amount) used when aligning each of a plurality of shot regions on a substrate is obtained.

[0003] Japanese Patent Application Laid-Open No. 2010-103216 describes measuring the position of a mark provided in a sample imaging region, approximating the deformation of the substrate, and obtaining the alignment amount corresponding to each of the plurality of imaging regions.

[0004] Here, when the position of the mark provided in the sample imaging region is measured and the alignment amount of the non-sample imaging region obtained by approximating the deformation of the substrate is used for alignment, the alignment accuracy of the non-sample imaging region may be reduced. Summary of the invention

[0005] An object of the present invention is to provide an information processing device that is useful for calculating alignment amounts corresponding to a plurality of imaging areas.

[0006] In order to achieve the above-mentioned purpose, as one aspect of the present invention, the information processing device is characterized in that it has a processing unit that uses a first value of the measurement result of the position of the mark set in the multiple shooting areas respectively on the first substrate to generate a regression model, and calculates the alignment amount for the alignment of multiple non-sample shooting areas different from the sample shooting area in the multiple shooting areas on the second substrate based on the second value of the measurement result of the position of the mark set in the sample shooting area in the multiple shooting areas on the second substrate and the regression model, the number of the multiple shooting areas on the first substrate where the position of the mark is measured is greater than the number of the sample shooting areas, and the regression model includes regression models corresponding to the multiple non-sample shooting areas respectively.

[0007] Further features of the present invention will become apparent from the following description of exemplary embodiments (with reference to the attached drawings). BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 It is a schematic diagram showing the structure of the substrate processing apparatus in the first embodiment.

[0009] Figure 2 This is a schematic diagram showing a method of measuring the position of the fine motion stage using a laser interferometer system.

[0010] Figure 3This is an example in which a plurality of shot regions on the substrate are positionally shifted.

[0011] Figure 4 This is a flowchart for generating a regression model in advance in the first embodiment.

[0012] Figure 5 This is a flow chart of the exposure of the substrate in the first embodiment.

[0013] Figure 6 This is an example of obtaining the positioning amount of the non-sample imaging region in the first embodiment.

[0014] Figure 7 This is a flowchart of the method for manufacturing an article in the second embodiment. DETAILED DESCRIPTION

[0015] The following is an explanation of the embodiments of the present invention based on the accompanying drawings. It should be noted that the following embodiments do not limit the invention involved in the claims. A plurality of features are described in the embodiments, but these plurality of features are not limited to all being features necessary for the invention, and a plurality of features may be combined arbitrarily. Furthermore, in the accompanying drawings, the same or similar structures are marked with the same reference numerals, and repeated descriptions are omitted.

[0016] In addition, in this specification and the drawings, directions are basically expressed by an XYZ coordinate system in which the vertical direction is set as the Z axis, the horizontal plane perpendicular to the vertical direction is set as the XY plane, and the axes are orthogonal to each other. However, when the XYZ coordinate system is described in each drawing, this coordinate system is given priority.

[0017] Hereinafter, in each embodiment, a specific structure will be described.

[0018] <First Embodiment>

[0019] Figure 1: is a schematic diagram showing the structure of the substrate processing device 1 in the present embodiment. In the present embodiment, the substrate processing device 1 is a projection exposure device that exposes the pattern of the original plate (mask, intermediate mask) to the substrate via a projection optical system by a step-and-repeat method or a step-and-scan method. However, the substrate processing device 1 is not limited to a projection exposure device. For example, the substrate processing device 1 may also be a drawing device that draws the substrate by an electron beam, an ion beam, etc. to form a pattern on the substrate. In addition, the substrate processing device 1 may also be other photolithography devices (substrate exposure devices), such as an imprinting device that uses a mold to shape the imprint material on the substrate to form a pattern on the substrate. Alternatively, the substrate processing device 1 may also be other devices that process substrates such as semiconductor wafers and glass plates, such as ion implantation devices, developing devices, etching devices, film forming devices, annealing devices, sputtering devices, and evaporation devices. In addition, the substrate processing device 1 may also be a flattening device that uses a flat plate to flatten the composition on the substrate.

[0020] The substrate processing device 1 has an illumination optical system 12 for irradiating light, a projection optical system 15, a reticle stage 14 for holding a reticle 13, a substrate stage 20 for holding a substrate 16 and being drivable (movable), and a measuring unit (detecting unit) 30. In addition, the substrate processing device 1 has a processing unit 40, a control unit 41, and a storage unit 42. The reticle 13 is, for example, an original plate in which a pattern to be transferred (for example, a circuit pattern) is formed on the surface of quartz glass by chromium. In addition, the substrate 16 is, for example, single crystal silicon. When the substrate processing device 1 is an exposure device, the substrate 16 transported to the substrate processing device 1 is coated with a photosensitive material (resist) on the surface. Here, the illumination optical system 12 is a pattern forming unit that forms a pattern on the substrate 16. It should be noted that in this embodiment, an example of a photolithography device that forms a pattern using light is shown, and the pattern forming unit is the illumination optical system 12, but it can also be a photolithography device that cures a thermosetting material to which a pattern is transferred by heat. The pattern forming unit in this case is, for example, a heating unit that heats a thermosetting material. The processing unit 40 is an information processing device (information processing unit) that calculates a positioning amount (correction amount) to be described later.

[0021] The processing unit 40 may include a transmission unit 43, or may be connected to the transmission unit 43 in a communicable manner. The transmission unit 43 transmits information related to the alignment amount calculated by the processing unit 40 to the control unit 41. Here, the transmission unit 43 may also transmit information related to the alignment amount calculated by the processing unit 40 to a display control unit (not shown) for controlling the display of a display unit (not shown) configured in the substrate processing device 1 or other devices. In this case, the display control unit controls the display of the display unit in a manner that enables the user to visually recognize the calculated alignment amount based on the information related to the alignment amount transmitted from the transmission unit 43. The display is displayed, for example, in a manner corresponding to the positions of a plurality of imaging regions on the substrate 16.

[0022] The control unit 41 controls various parts in the substrate processing apparatus 1. The storage unit 42 stores programs, information, and the like for performing exposure processing on the substrate 16. It should be noted that, in the present embodiment, the processing unit 40 is shown as an example in which it is inside the substrate processing apparatus 1 and is different from the control unit 41, but it may be inside the control unit 41 (it may be a part of the control unit 41) or inside an information processing device outside the substrate processing apparatus 1. In addition, regarding the storage unit 42, the present embodiment also shows an example in which it is inside the substrate processing apparatus 1 and is different from the control unit 41, but it may be inside the control unit 41 (it may be a part of the control unit 41) or inside a storage device outside the substrate processing apparatus 1.

[0023] The processing unit 40 and the control unit 41 can be composed of, for example, a PLD such as an FPGA, or an ASIC, or a computer embedded with a program, or a combination of all or part of them. FPGA is the abbreviation of Field Programmable Gate Array. PLD is the abbreviation of Programmable Logic Device. ASIC is the abbreviation of Application Specific Integrated Circuit. The processing unit 40 and the control unit 41 include a CPU, and each component functions according to the program stored in the storage unit 42. The CPU performs calculations for control according to the program. The storage unit 42 is also used as a temporary storage area for programs and data of the operating system (OS) of the processing unit 40 and the control unit 41. It is desired that the storage unit 42 is a non-volatile storage device that can store the data to be stored as permanent data in a manner that can be referenced for a long time. The storage device is mainly composed of a magnetic storage device (HDD), but it can also be a device that reads and writes data by loading external media such as CDs, DVDs, and memory cards.

[0024] The substrate stage 20 has an XY stage 23 that can move on the XY plane, a fine-motion stage 22 that is mounted on the XY stage 23 and can be fine-moved, and a substrate chuck 21 that holds the substrate 16 in a state of being held on the fine-motion stage 22. The fine-motion stage 22 can move (drive) in the X-axis direction, the Y-axis direction, the Z-axis direction (the optical axis direction of the projection optical system 15), the θx-axis direction, the θy-axis direction, and the θz-axis direction. That is, the fine-motion stage 22 can move (drive) in 6-axis directions. Here, the θx-axis direction is the rotation direction around the X-axis, the θy-axis direction is the rotation direction around the Y-axis, and the θz-axis direction is the rotation direction around the Z-axis. In addition, the position in the θx-axis direction refers to the rotation angle around the X-axis, the position in the θy-axis direction refers to the rotation angle around the Y-axis, and the θz-axis direction refers to the rotation angle around the Z-axis.

[0025] Figure 2 1 is a schematic diagram showing a method of measuring the position of the fine motion stage 22 by a laser interferometer system. The position of the fine motion stage 22 in the X-axis direction is measured using an X-strip mirror 100 and a laser interferometer 110. Moreover, the position of the fine motion stage 22 in the Y-axis direction is measured using a Y-strip mirror 200 and a laser interferometer 210. The X-strip mirror 100 is provided on the side of the fine motion stage 22 to extend in the Y-axis direction, and the Y-strip mirror 200 is provided on the side of the fine motion stage 22 to extend in the X-axis direction.

[0026] First, a method for measuring the position of the fine motion stage 22 in the X-axis direction is described. The laser interferometer 110 includes three laser interferometers. The laser interferometer 112 is configured at the same horizontal position as the laser interferometer 111 (the same position on the X-axis and the same position on the Y-axis) and at a distance Δz from the laser interferometer 111 in the height direction (Z-axis direction). According to the measurement results of the laser interferometer 111 and the laser interferometer 112, the offset θy in the rotation direction around the Y-axis can be measured. The laser interferometer 113 is configured at the same height as the laser interferometer 111 (the same position in the Z-axis direction) and at a distance Δy from the laser interferometer 111 in the horizontal direction (Y-axis direction). According to the measurement results of the laser interferometer 111 and the laser interferometer 113, the offset θz in the rotation direction around the Z-axis in the XY plane can be measured.

[0027] Next, a method for measuring the position of the fine motion stage 22 in the Y-axis direction is described. The laser interferometer 210 includes three laser interferometers. The laser interferometer 212 is configured at the same horizontal position as the laser interferometer 211 (the same position on the X-axis and the same position on the Y-axis) and at a distance Δz from the laser interferometer 211 in the height direction (Z-axis direction). According to the measurement results of the laser interferometer 211 and the laser interferometer 212, the offset θx in the rotation direction around the X-axis can be measured. The laser interferometer 213 is configured at the same height as the laser interferometer 211 (the same position in the Z-axis direction) and at a distance Δx from the laser interferometer 211 in the horizontal direction (X-axis direction). According to the measurement results of the laser interferometer 211 and the laser interferometer 213, the offset θz in the rotation direction around the Z-axis in the XY plane can be measured. Through the above configuration, the position of the fine motion stage 22 is measured. It should be noted that, in this embodiment, an example of setting a strip mirror on the side of the fine motion stage 22 is shown, but the strip mirror can also be set on the upper part of the fine motion stage 22, and the setting position is not particularly limited. It should be noted that, in this embodiment, an example of measuring the position of the fine motion stage 22 by an interferometer system is shown, but the position of the fine motion stage 22 can also be measured by other components such as an encoder.

[0028] The measuring unit 30 is, for example, an off-axis scope, and is used to position the substrate 16 and measure (detect) the positions of a plurality of pattern regions on the substrate 16. Specifically, the relative positions and postures of alignment marks (not shown) respectively provided in a plurality of shooting regions on the substrate 16 and reference marks (not shown) on the fine motion stage 22 are measured (detected). In this embodiment, the relative positions and postures of the reference marks and alignment marks provided in the shooting regions measured by the measuring unit 30 are sometimes referred to as the measurement results of the positions of the marks provided in the shooting regions.

[0029] In the substrate processing apparatus 1, exposure light from a light source (not shown) illuminates the reticle 13 held on the reticle stage 14 via the illumination optical system 12. The light transmitted through the reticle 13 is irradiated toward the substrate 16 via the projection optical system 15. At this time, the light from the pattern formed on the reticle 13 forms an image on the surface of the substrate 16, and the substrate 16 (photosensitive material) is exposed to the pattern image. The substrate processing apparatus 1 exposes the shot area on the substrate 16 in this way, and exposes each of the plurality of shot areas in the same manner.

[0030] Figure 3 This is an example in which positional deviations occur in each of the plurality of imaging regions on the substrate 16 .

[0031] exist Figure 3In the example shown in FIG. 5 , the rectangular area shown by the dotted line is the ideal imaging area 50, and the rectangular area shown by the solid line is the actual imaging area 51. Figure 3 As shown, the actual shooting area 51 is sometimes positionally offset relative to the ideal shooting area 50. In the exposure process, the accuracy of overlap is important, so it is necessary to perform exposure while using the alignment amount for correcting the positional offset. Therefore, during exposure, the alignment amount is calculated based on the measurement result of the measuring unit 30 (the measurement result of the position of the mark set in the shooting area), the target position of the mark (shooting area), the positional offset, etc. Then, the calculated alignment amount is used to control the substrate stage 20 (drive the substrate stage 20) based on the measurement result of the position of the substrate stage 20 (fine motion stage 22), and align the multiple shooting areas on the substrate 16.

[0032] Here, in all substrates 16 to be exposed in the future, by calculating the alignment amount for all the shooting areas (the positions of the marks set), the positional offsets of the multiple shooting areas (the positions of the marks set) can be properly corrected. However, in this method, the number of substrates processed per unit time is reduced. Therefore, for example, there is a method as follows: the position of the mark set in the sample shooting area (shooting area of ​​the measurement object) included in the multiple shooting areas is measured, the deformation of the substrate is approximated, and the alignment amount corresponding to each of the multiple shooting areas is calculated. However, when the position of the mark set in the sample shooting area is measured and the alignment amount of the non-sample shooting area calculated by approximating the deformation of the substrate is used for alignment, the alignment accuracy of the non-sample shooting area is sometimes reduced.

[0033] Therefore, in this embodiment, a method is disclosed that is useful for calculating the alignment amounts corresponding to the plurality of shot areas. Specifically, the positions of the marks provided in the plurality of shot areas are measured in advance for one or more substrates (the first substrate, the third substrate), and the plurality of shot areas include a shot area at a position corresponding to the non-sample shot area of ​​the substrate (the second substrate) to be exposed later.

[0034] For example, in the case where there is one substrate on which the position of the mark is measured in advance, the substrate is the first substrate, and in the case where there are multiple substrates on which the position of the mark is measured in advance, the multiple substrates are the first substrate and the third substrate. It should be noted that in the present embodiment, even if there are multiple substrates on which the position of the mark is measured in advance, the first substrate and the third substrate are sometimes recorded together as the first substrate. In addition, the value of the measurement result of the position of the mark based on the multiple shooting areas set on the first substrate is the first value, and the value of the measurement result of the position of the mark based on the multiple shooting areas set on the third substrate is the third value. Here, in the present embodiment, the first value and the third value are sometimes recorded together as the first value. It should be noted that the first value and the third value in the present embodiment are a numerical value group including multiple numerical values, and the multiple numerical values ​​are values ​​based on the measurement results of the position of the mark set in the multiple shooting areas.

[0035] Then, in the present embodiment, a regression model is generated using values ​​(first value, third value) based on the measurement results. The first value (third value) may also be a measurement result of the position of a mark set in a plurality of shooting regions including a shooting region at a position corresponding to the non-sample shooting region of the second substrate. Alternatively, the first value (third value) may also be an alignment amount of a plurality of shooting regions including a shooting region at a position corresponding to the non-sample shooting region of the second substrate obtained based on the measurement results. Alternatively, the first value (third value) may also be a plurality of position offset amounts that are differences between the target position of a mark set in the shooting region at a position corresponding to the non-sample shooting region of the second substrate and the measurement result.

[0036] Next, based on the regression model and the value (second value) of the measurement result of the position of the mark set in the sample shooting area of ​​the substrate (second substrate) to be exposed in the future, the alignment amount of the non-sample shooting area of ​​the substrate (second substrate) to be exposed in the future is calculated. The second value may also be the measurement result of the position of the mark set in the sample shooting area of ​​the second substrate. Alternatively, the second value may also be the alignment amount of the sample shooting area of ​​the second substrate calculated based on the measurement result. Alternatively, the second value may also be the position offset amount which is the difference between the target position of the mark set in the sample shooting area and the measurement result. The regression model is generated according to the types of the first value and the second value (measurement result or alignment amount or position offset amount). It should be noted that the second value in this embodiment is a numerical value group including a plurality of numerical values, and the plurality of numerical values ​​are values ​​based on the measurement result of the position of the mark set in the sample shooting area.

[0037] Here, it is assumed that the arrangement of the sample imaging region and the non-sample imaging region set on the substrate (second substrate) to be exposed later is the same as the arrangement of the plurality of imaging regions on the substrate (first substrate) on which the marked positions are measured in advance. That is, the positions of the plurality of imaging regions on the first substrate and the positions of the plurality of imaging regions on the second substrate are consistent with each other.

[0038] Furthermore, it is assumed that the substrate processing conditions (exposure processing conditions, process) of the substrate to be exposed in the future are the same as the substrate processing conditions (exposure processing conditions, process) of the substrate whose marked position was measured in advance.

[0039] In the generation of the regression model used to obtain the alignment amount of the non-sample shooting area, the first value based on the measurement result of the position of the mark set in the shooting area on the first substrate corresponding to the non-sample shooting area on the second substrate is used in the generation of the regression model used for obtaining the alignment amount of the non-sample shooting area. Specifically, the first value based on the measurement result of the position of the mark set in the shooting area on the first substrate corresponding to at least one of the plurality of non-sample shooting areas on the second substrate is used in the generation of the regression model. Moreover, the value input to the regression model is the second value based on the measurement result of the position of the mark set in the sample shooting area of ​​the second substrate. In this way, the regression model of the present embodiment is generated using the first value including the value based on the measurement result of the shooting area corresponding to the non-sample shooting area. Therefore, the method of obtaining the alignment amount of the non-sample shooting area of ​​the present embodiment has a higher accuracy of the alignment amount than the method of obtaining the alignment amount of the non-sample shooting area by approximating the deformation of the substrate based on the measurement result of the position of the mark set in the sample shooting area. Therefore, by using the alignment amount obtained by the present embodiment to perform alignment of each of the plurality of shooting areas, the alignment accuracy (overlap accuracy) can be improved.

[0040] Figure 4This is a flowchart for generating a regression model in advance in the present embodiment. First, in order to generate a regression model, the processing unit 40 selects a substrate (first substrate) on which the position of a mark set in a shooting area is measured (detected) by the measuring unit 30 (S110). Then, the measuring unit 30 measures the position of the mark set in the shooting area for each of the multiple shooting areas on the substrate selected in step S110 (S120). Here, in step S120, the position of all shooting areas corresponding to the sample shooting area of ​​the substrate (second substrate) to be exposed in the future is measured. Furthermore, in step S120, the position of at least one of the multiple shooting areas corresponding to the multiple non-sample shooting areas different from the sample shooting area of ​​the second substrate is measured. That is, the number of the multiple shooting areas on the first substrate on which the position of the mark is measured is greater than the number of the sample shooting areas. It should be noted that it is preferred that the position of all shooting areas on the substrate is measured.

[0041] In addition, when a plurality of alignment marks are provided in one shot region, the alignment marks may be measured separately. In addition, when the measurement results of the plurality of shot regions of the first substrate are used as the first value for generating the regression model, step S130 is not performed, and step S140 is performed after step S120.

[0042] Next, the processing unit 40 obtains the alignment amount (first value) for the shooting area on the first substrate based on the measurement result of step S120 (S130). The alignment amount is a value based on the measurement result of the position of the mark set in the shooting area in step S120 (the position offset of the mark set in the shooting area), and is a value used to correct the position offset of the mark. Using this first value, the mark set in the shooting area is aligned. That is, the alignment amount is the same as the position offset. Here, in the case where a plurality of alignment marks are provided in one shooting area, the alignment amount can also be obtained based on the measurement result of each alignment mark. Alternatively, a value obtained based on the measurement result of each alignment mark can also be used as a alignment amount corresponding to one shooting area. It should be noted that when the measurement results of multiple shooting areas of the first substrate are used as the first value, step S120 is the first acquisition process, and when the alignment amount (position offset) of the first substrate is used as the first value, step S130 is the first acquisition process.

[0043] Here, in the case where there is a shooting area where the first value cannot be obtained, the first value of the object shooting area is interpolated (guessed). For example, in step S120, the position measurement of all the shooting areas on the substrate is not performed. Or, there is a shooting area where the measurement unit 30 does not normally measure the position of the mark set in the shooting area. Or, there is a shooting area where no mark is set. In these cases, the first value of the shooting area that is close to (for example, adjacent to) the shooting area of ​​the object is used. Alternatively, in the case of performing step S120 on multiple substrates, the first value of the shooting area that is at the same position as the object shooting area in other substrates or their average value is used. Alternatively, a regression model can be generated based on the target position of the alignment of the shooting area of ​​other substrates and the first value of the shooting area, and the first value of the shooting area of ​​the object can be obtained based on the generated regression model. The regression model is a regression model with the target position of the alignment of the shooting area of ​​the object as the explanatory variable and the first value of the shooting area of ​​the object as the target variable.

[0044] Next, the processing unit 40 determines whether the first value for generating the regression model has been obtained for all target substrates (S140). In other words, it is determined whether the substrate for which the first value has been obtained just before is the final substrate among the target substrates for which the first value has been obtained. For example, when the number of substrates for which the first value has been obtained (the first substrate) is one, the process proceeds to step S150. For example, when the number of substrates for which the first value has been obtained is 25, if the first value has been obtained for 20 substrates, the process returns to step S110 until the first value has been obtained for 25 substrates.

[0045] Next, one of the multiple shooting areas of the first substrate corresponding to the non-sample shooting area of ​​the second substrate is selected (S150). Then, a regression model is generated for the selected shooting area (S160, generation process). The regression model is generated based on the first value obtained from the measurement of the substrate for one or more previously marked positions. It should be noted that the regression model can also be generated by considering the characteristics of the position (coordinates) of the selected shooting area on the substrate (ease of deformation of the outer periphery of the substrate, etc.). The regression model uses the second value of the measurement result of the position of the mark set in the sample shooting area of ​​the substrate (the second substrate) to be exposed in the future as the input value (explanatory variable), and the alignment amount of the non-sample shooting area of ​​the second substrate as the output value (target variable, predicted value). Here, for example, in the case where multiple alignment marks are provided in one shooting area, the second values ​​based on the measurement results of each alignment mark can also be used as input values ​​(explanatory variables). Alternatively, a value obtained based on these second values ​​can also be used as the input value (explanatory variable).

[0046] Next, the processing unit 40 determines whether a regression model has been generated for all of the plurality of imaging regions of the first substrate corresponding to the non-sample imaging region of the second substrate (S170). If a regression model has been generated for all imaging regions, the process ends. If a regression model has not been generated for all imaging regions, the process returns to step S150. Thus, in this embodiment, a regression model for determining the alignment amount of each non-sample imaging region on the second substrate is generated in advance.

[0047] Figure 5 This is a flowchart for exposing a substrate in this embodiment. First, the position of the mark set in the sample shooting area of ​​the substrate 16 (second substrate) to be exposed in the future is measured (S210). It should be noted that when a plurality of alignment marks are set in one shooting area, the alignment marks can also be measured separately. Here, the measurement result of step S210 can also be used as the second value input to the regression model.

[0048] Next, the processing unit 40 respectively calculates the alignment amount (second value) for the measurement results of step S210 (S220). That is, the alignment amount (position offset) is calculated for the sample shooting area. Here, in the case where a plurality of alignment marks are provided in one shooting area, the alignment amounts can also be calculated based on the measurement results of each alignment mark. Alternatively, one alignment amount calculated based on the measurement results of each alignment mark can be used as one alignment amount corresponding to one shooting area. It should be noted that, in the case of using the measurement result of the sample shooting area of ​​the second substrate as the second value, step S210 is the second acquisition process, and in the case of using the alignment amount (position offset) of the second substrate as the second value, step S220 is the second acquisition process.

[0049] Here, the second value of the object shooting area for which the second value has not been obtained is interpolated (estimated). This is, for example, the second value of the shooting area at the same position as the object shooting area in the substrate under the same exposure processing conditions measured in the past or their average value. Alternatively, a regression model may be generated based on the aligned target position of other sample shooting areas and the second value of other sample shooting areas, and the second value of the object shooting area may be obtained based on the generated regression model. The regression model is a regression model with the aligned target position of the object shooting area as an explanatory variable and the second value of the object shooting area as a target variable.

[0050] Next, the processing unit 40 selects one of the non-sample imaging regions ( S230 ) and then obtains the alignment amount of the non-sample imaging region based on the regression model and the second value ( S240 , calculation step).

[0051] Here, the regression model used in step S240 is a regression model corresponding to the position (coordinate) on the substrate of the selected non-sample shooting area. The regression model uses the second value based on the measurement result of the position of the mark set on the sample shooting area of ​​the second substrate to be exposed later as an input value (explanatory variable) and the alignment amount of the non-sample shooting area as an output value (target variable).

[0052] Here, use Figure 6 Step S240 is described. Figure 6 This is an example of obtaining the alignment amount of the non-sample shooting area in the present embodiment. The substrate 16 (the second substrate) to be exposed in the future includes 16 shooting areas, which are numbered with numbers 1 to 16 respectively. Here, numbers 1, 4, 6, 11, 12, and 13 are sample shooting areas, and the other shooting areas are non-sample shooting areas. For each non-sample shooting area, a regression model is obtained by the aforementioned method. For example, the case of obtaining the alignment amount for the non-sample shooting area numbered 2 is described. In this case, the second value of the measurement result based on the position of the mark set in the sample shooting areas numbered 1, 4, 6, 11, 12, and 13 is input into the regression model corresponding to number 2, and the alignment amount of number 2 is obtained.

[0053] Next, the processing unit 40 determines whether to obtain the alignment amount for all non-sample shooting areas on the substrate 16 (second substrate) to be exposed in the future (S250). If the alignment amount is obtained for all non-sample shooting areas, the process proceeds to step S260. If the alignment amount is not obtained for all non-sample shooting areas, the process returns to step S230. As described above, in this embodiment, the alignment amount of the non-sample shooting area of ​​the substrate 16 to be exposed in the future is obtained. It should be noted that Figure 4 Steps S130 to S170 described in the flowchart of Figure 5 Steps S220 to S250 described in the flowchart are an information processing method performed by the processing unit 40 according to a program stored inside.

[0054] Next, while performing alignment using the obtained alignment amount (while the control unit 41 controls the substrate stage 20 for alignment), the plurality of shot areas on the substrate are exposed (S260, exposure process), and the process ends. It should be noted that before starting the exposure process, the control unit 41 obtains the alignment amount of the sample shot area obtained in step S220 and the alignment amount of the non-sample shot area obtained in step S240. It should be noted that, in the case where the substrate processing device 1 is not an exposure device, the process performed in step S260 is not a process for forming a pattern, but a process performed by the substrate processing device 1.

[0055] In the case where a regression model corresponding to the configuration of the shot area and the exposure processing conditions (process) of the second substrate to be exposed in the future has not been generated, a regression model is generated using the first substrate corresponding to the configuration of the shot area and the exposure processing conditions (process) of the second substrate and stored in the storage unit 42. In this way, the storage unit 42 stores a plurality of regression models generated according to the configuration of the shot area and the exposure processing conditions (process) in correspondence with the configuration of the shot area and the exposure processing conditions (process). Then, when processing the second substrate having the configuration of the shot area and the exposure processing conditions corresponding to the configuration of the shot area and the exposure processing conditions for which the regression model was generated, the alignment amount of the non-sample shot area of ​​the second substrate can be obtained using the regression model stored in the storage unit 42.

[0056] Alternatively, for each substrate without an appropriate regression model, a regression model may be generated based on the target position of the alignment of the sample shooting area and the second value of the sample shooting area, and the alignment amount of the non-sample shooting area may be obtained based on the generated regression model. The regression model is a regression model that uses the target position of the alignment of the non-sample shooting area as an explanatory variable and the alignment amount of the alignment of the non-sample shooting area as a target variable.

[0057] Next, the regression model is described in detail. In the following description, the regression model is described as a multiple regression that uses multiple explanatory variables to predict one target variable. x1, x2, ..., xs are set as explanatory variables (the alignment amount of the sample shooting area on the second substrate), and w0, w1, ..., ws are set as coefficients that can be calculated based on the first value obtained in advance. In addition, if s is set as the number of sample shooting areas on the second substrate and y' is set as the target variable (the alignment amount of the non-sample shooting area of ​​the object on the second substrate), the regression model is expressed as formula (1).

[0058] y'=w0+w1x1+w2x2+…+w s x s Formula (1)

[0059] The coefficient w can be obtained by equation (2). It should be noted that T represents a transposed matrix.

[0060] w=(X T X) -1 X T y Formula (2)

[0061] Here, the coefficient w obtained from the equation (2) is a vector represented by the equation (3).

[0062] w=(w0,w1,w2, …,w s ) T Formula (3)

[0063] In addition, X in equation (2) is represented by equation (4), which is a matrix in which the first values ​​of the imaging area corresponding to the sample imaging area on the second substrate are arranged for N substrates (the first substrate, the third substrate, etc.) whose first values ​​are previously calculated. The constant 1 in the matrix is ​​a constant for multiplying the intercept w0 among the coefficients w0, w1, ... ws.

[0064] [Number 1]

[0065]

[0066] In addition, y in equation (2) is represented by equation (5), which is a vector that arranges the first values ​​of the imaging area corresponding to the non-sample imaging area of ​​the object on the second substrate of N substrates (first substrate) whose first values ​​have been calculated in advance.

[0067] y=(y (1) ,y (2) , · · ·, y (N) ) T Formula (5)

[0068] As a regression model, in addition to multiple regression, other regression methods such as n-order polynomial regression, linear regression, Ridge regression, Lasso regression, Gaussian process regression, and support vector regression can also be used. n-order polynomial regression is a method of making a new feature quantity by expanding the explanatory variables of the regression model with an n-order polynomial, and performing multiple regression based on the feature quantity, which has higher performance than multiple regression. Linear regression is a method of making a new feature quantity by passing the explanatory variables of the regression model through a feature quantity transformation function Φ, and performing multiple regression based on the feature quantity, which has higher performance than multiple regression. Ridge regression is a method of learning by applying L2 norm constraints to the values ​​of the coefficients of the linear regression model, and it is expected that the over-learning (over-fitting) suppression effect can be expected. Lasso regression is a method of learning by applying L1 norm constraints to the values ​​of the coefficients of the linear regression model so that the values ​​of the coefficients become sparse (many 0 values), and it is expected that the over-learning (over-fitting) suppression effect and the speed-up of the inference time can be expected. Gaussian process regression is a method that assumes that the relationship between the explanatory variables and the target variable follows the Gaussian process, and does not explicitly have the coefficients of the regression model. By using the kernel function to express the relationship between data samples, it has high expressiveness as a regression model. In addition, since the probability distribution of the output is obtained through Bayesian inference, not only the average of the output can be obtained, but also the variance value can be obtained, which can quantify the uncertainty of the reasoning. Support vector regression, like Gaussian process regression, uses the kernel function to express the relationship between data samples, so it has high expressiveness as a regression model. Furthermore, since a dead zone is set in the regression error, the influence of data noise can be suppressed.

[0069] The same regression method may be used for all of the plurality of imaging regions on the substrate 16, or a different regression method may be used for each imaging region. When a different regression method is used for each imaging region, it is preferred to study a regression method that can calculate an alignment amount that can achieve high alignment accuracy for each imaging region.

[0070] It should be noted that, although an example of generating a regression model for each non-sample shooting area is shown, a multi-output regression model that outputs multiple target variables by inputting multiple explanatory variables may also be used. For example, it may also be a regression model that outputs the alignment amounts of multiple non-sample shooting areas of a substrate to be exposed in the future if the alignment amounts of multiple sample shooting areas of a substrate to be exposed in the future are input. That is, it is also possible to generate a multi-output regression model that outputs the first alignment amount and the second alignment amount corresponding to the first non-sample shooting area and the second non-sample shooting area on the second substrate, respectively, by inputting a second value. In this case, the alignment amounts of multiple non-sample shooting areas can be obtained by one regression model, and the operation of obtaining the alignment amount for each non-sample shooting area may not be performed. As a multi-output regression model that takes multiple target variables, for example, a neural network is used. In a neural network, learning is performed in a manner that minimizes the errors of multiple target variables at the same time, so that regression that captures the relationship between these variables can be achieved, and improved accuracy can be expected.

[0071] As described above, by performing alignment using the alignment amount of the non-sample imaging region obtained by the method described in this embodiment, it is possible to improve the alignment accuracy (superposition accuracy).

[0072] <Second Embodiment>

[0073] Figure 7 1 is a flow chart of the manufacturing method of the article in this embodiment. First, an exposure step (S310) is performed to expose the plurality of shooting areas on the second substrate while aligning the plurality of shooting areas. Next, a manufacturing step (S320) of manufacturing the article from the second substrate exposed in the exposure step is performed.

[0074] Here, the alignment amount for alignment of the non-sample imaging region different from the sample imaging region among the plurality of imaging regions of the second substrate is obtained based on the regression model and the second value. The regression model is a model generated using the first value based on the measurement result of the position of the mark provided in the plurality of imaging regions on the first substrate, which is larger in number than the sample imaging region on the second substrate. The second value is a value based on the measurement result of the position of the mark provided in the sample imaging region of the second substrate.

[0075] Articles manufactured by this manufacturing method are, for example, semiconductor IC elements, liquid crystal display elements, color filters, MEMS, and the like.

[0076] The manufacturing process includes, for example, developing a substrate (photosensitive material) on which a pattern is formed, etching the developed substrate, and performing resist stripping, dicing, bonding, and packaging. According to the present manufacturing method, it is possible to manufacture a product of higher quality than before.

[0077] The present 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. Therefore, claims are appended to disclose the scope of the invention.

[0078] According to the present invention, it is possible to provide an information processing device which is useful for calculating the positioning amounts corresponding to each of a plurality of imaging areas.

[0079] Other embodiments

[0080] The embodiments of the present invention may also be implemented by a computer of a system or device, which reads and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be more comprehensively referred to as a "non-transitory computer-readable storage medium") to perform one or more functions of the above-mentioned embodiments and / or includes one or more circuits (e.g., application-specific integrated circuits (ASICs)) for performing one or more functions of the above-mentioned embodiments, and by a method executed by a computer of a system or device, which, for example, reads and executes computer executable instructions from a storage medium to perform one or more functions of the above-mentioned embodiments and / or controls one or more circuits to perform one or more functions of the above-mentioned embodiments. The computer may include one or more processors (e.g., a central processing unit (CPU), a microprocessor (MPU)), and may include a network of separate computers or separate processors to read and execute computer executable instructions. Computer executable instructions may be provided to a computer, for example, from a network or a storage medium. The storage medium may include, for example, one or more of a hard disk, a random access memory (RAM), a read-only memory (ROM), a storage device of a distributed computing system, an optical disk (such as a compact disk (CD), a digital versatile disk (DVD), or a Blu-ray disk (BD) TM), a flash memory device, a memory card, and the like.

[0081] While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments.The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions.

[0082] This application claims the benefit of Japanese Patent Application No. 2023-105953, filed on Jun. 28, 2023, which is hereby incorporated by reference herein in its entirety.

Claims

1. An information processing device, characterized in that: have: a processing unit that generates a regression model using a first value based on a measurement result of a position of a mark provided at each of a plurality of imaging regions on a first substrate, and obtains an alignment amount for alignment of a plurality of non-sample imaging regions different from the sample imaging region among the plurality of imaging regions on the second substrate based on a second value based on a measurement result of a position of a mark provided at a sample imaging region among the plurality of imaging regions on the second substrate and the regression model, The number of the plurality of imaging regions on the first substrate where the position of the mark is measured is greater than the number of the sample imaging regions, The regression model includes regression models corresponding to the plurality of non-sample shooting areas respectively.

2. The information processing device according to claim 1, characterized in that The first value and the second value are the measurement result of the position of the mark, or the difference between the measurement result of the position of the mark and the target position of the mark, that is, the position offset, or the alignment amount of the imaging area used to measure the position of the mark.

3. The information processing device according to claim 1, characterized in that The positioning amount is used for controlling a stage that can be driven while holding the second substrate.

4. The information processing device according to claim 1, characterized in that The processing unit generates the multi-output regression model, which outputs the first and second alignment amounts corresponding to the first and second non-sample imaging regions respectively included in the plurality of non-sample imaging regions when the second value is input.

5. The information processing device according to claim 1, characterized in that Any one of the positions of the plurality of shot regions on the first substrate in the first substrate and the positions of the plurality of shot regions on the second substrate in the second substrate coincides with each other.

6. The information processing device according to claim 1, characterized in that The processing unit generates the regression model using a first value based on a measurement result of a position of a mark provided in a photographing area corresponding to at least one of the plurality of non-sample photographing areas among the plurality of photographing areas on the first substrate.

7. The information processing device according to claim 1, characterized in that: The processing unit generates the regression model using a third value based on a measurement result of positions of marks provided in each of a plurality of imaging regions on a third substrate and the first value.

8. The information processing device according to claim 1, characterized in that The exposure processing conditions of the first substrate and the second substrate are the same.

9. The information processing device according to claim 8, characterized in that: have: A storage unit stores the regression model generated by the processing unit for each of the exposure processing conditions.

10. The information processing device according to claim 1, characterized in that The second value is an explanatory variable of the regression model, and the alignment amount is a target variable of the regression model.

11. The information processing device according to claim 1, characterized in that: A transmitting unit is provided for transmitting the information on the positioning amount to a control unit for controlling a mounting table that can be driven while holding the second substrate, or to a display unit for display.

12. An information processing method, characterized in that: include: a first acquisition step of acquiring a first value based on a measurement result of positions of marks provided in a plurality of imaging regions on a first substrate; a generating step of generating a regression model based on the first value acquired in the first acquiring step; a second acquisition step of acquiring a second value based on a measurement result of a position of a mark provided in a sample imaging region among a plurality of imaging regions on a second substrate different from the first substrate; as well as a calculation step in which, based on the second value acquired in the second acquisition step and the generated regression model, an alignment amount for aligning a plurality of non-sample imaging regions different from the sample imaging region among the plurality of imaging regions on the second substrate is calculated; The number of the plurality of imaging regions on the first substrate where the position of the mark is measured is greater than the number of the sample imaging regions, The regression model includes regression models corresponding to the plurality of non-sample shooting areas respectively. 13 . A non-transitory computer-readable storage medium storing a program for causing a computer to execute the information processing method according to claim 12 .

14. An exposure method, characterized in that: The method comprises an exposure step in which the plurality of shot regions on the second substrate are exposed while the plurality of shot regions on the second substrate are aligned respectively. In order to align multiple non-sample shooting areas different from the sample shooting area among the multiple shooting areas on the second substrate, the alignment amount used in controlling the mounting table holding the second substrate is calculated based on a regression model and a second value. The regression model is a regression model corresponding to the non-sample shooting area, which is an object of calculating the alignment amount, among multiple regression models generated corresponding to the multiple non-sample shooting areas respectively using a first value based on the measurement result of the position of a mark set in a plurality of shooting areas larger in number than the sample shooting area on a first substrate different from the second substrate, and the second value is a value based on the measurement result of the position of the mark set in the sample shooting area.

15. A substrate processing device, characterized in that: have: a stage capable of being driven while holding the substrate; and a control unit for controlling the driving of the mounting table in order to align the plurality of imaging regions on the second substrate, respectively; In order to align multiple non-sample shooting areas different from the sample shooting area among the multiple shooting areas on the second substrate, the alignment amount used in the control of the mounting stage is calculated based on a regression model and a second value. The regression model is a regression model corresponding to the non-sample shooting area, which is an object of calculating the alignment amount, among multiple regression models generated corresponding to the multiple non-sample shooting areas respectively using a first value based on the measurement results of the positions of marks respectively set in multiple shooting areas larger in number than the sample shooting areas on a first substrate different from the second substrate, and the second value is a value based on the measurement results of the positions of marks set in the sample shooting area.

16. A method for manufacturing an article, characterized in that: include: an exposure step in which the plurality of shot regions on the second substrate are exposed while the plurality of shot regions on the second substrate are aligned respectively; as well as a manufacturing step in which an article is manufactured from the second substrate exposed in the exposure step, In order to align multiple non-sample shooting areas different from the sample shooting area among the multiple shooting areas on the second substrate, the alignment amount used in controlling the mounting table holding the second substrate is calculated based on a regression model and a second value. The regression model is a regression model corresponding to the non-sample shooting area, which is an object of calculating the alignment amount, among multiple regression models generated corresponding to the multiple non-sample shooting areas respectively using a first value based on the measurement result of the position of a mark set in a plurality of shooting areas larger in number than the sample shooting area on a first substrate different from the second substrate, and the second value is a value based on the measurement result of the position of the mark set in the sample shooting area.

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