Analysis system, exposure device, device production method, display production method, and analysis method

Abstract

The present invention provides an analysis system which makes it possible to identify, from among a plurality of substrate processing devices, a device which has caused a deformation amount of a substrate.　Given a first processing device (21) that has a first device (110) and a second device (111) and that subjects substrates to a first process and a second processing device (22) that has a third device (121) and a fourth device (122) and that subjects the substrates to a second process, when the substrates are subjected to the first process and the second process along any of a first route passing through the first device and the third device, a second route passing through the first device and the fourth device, a third route passing through the second device and the third device, and a fourth route passing through the second device and the fourth device, an analysis system (10) analyzes measurement information (MI) obtained from an inspection device (220) for measuring processing results of first to fourth substrates which have respectively passed through the first to fourth routes. The analysis system (10) comprises: a route information acquisition unit (310) that acquires substrate identification information (SID) and route identification information (RID); a measurement information acquisition unit (320) that acquires the substrate identification information and the measurement information; and a calculation unit (330) that calculates, on the basis of the route identification information and the measurement information, first to fourth device characteristics which occur in the first to fourth substrates due to the processes in the first to fourth devices.

Description

Analysis system, exposure apparatus, device manufacturing method, display manufacturing method, and analysis method

[0001] The present invention relates to an analysis system, an exposure apparatus, a device manufacturing method, a display manufacturing method, and an analysis method. This application claims priority based on Japanese Patent Application No. 2021-076855, filed on April 28, 2021, the contents of which are incorporated herein by reference.

[0002] Conventionally, in the field of semiconductor component manufacturing, there has been a technique for using an exposure apparatus to project and expose an image of a fine pattern formed on a photomask or the like onto a substrate, such as a semiconductor wafer, coated with a photosensitive agent, such as photoresist. In this exposure technique, the photomask and wafer are aligned with high precision, and the exposed pattern is overlaid on a pattern already formed on the wafer, followed by projection and exposure. When the exposure apparatus performs overlay exposure, high overlay accuracy is required for the already formed pattern. For example, variations in exposure results due to differences in the film deposition and etching equipment used to process the wafer can be significant. Specifically, when a wafer is deformed by a processing device, such as a film deposition or etching device, and an exposure apparatus exposes the deformed wafer, the exposed pattern is exposed at a position offset from the pattern formed on the wafer, resulting in inaccurate overlay of the patterns. Furthermore, the amount of wafer deformation varies depending on the processing device used. This results in variations in the exposure results, i.e., the degree of overlap between patterns. To correct this variation, a technique has been proposed in which an overlay prediction correction value is calculated based on process history and the like, and correction is performed during the exposure process (see, for example, Patent Document 1). However, in the conventional technology described above, it is not possible to identify which processing device is causing the wafer deformation, and when a specific processing device is replaced, the data accumulated up to that point becomes unusable, making it impossible to correct the exposure pattern.

[0003] Japanese Patent Application Laid-Open No. 2020-34682

[0004] The analysis system of the present invention includes a first processing apparatus having a first device and a second device for performing a first processing on a substrate, and a second processing apparatus having a third device and a fourth device for performing a second processing on the substrate, wherein the substrate passes through any one of a first path passing through the first device and the third device, a second path passing through the first device and the fourth device, a third path passing through the second device and the third device, or a fourth path passing through the second device and the fourth device, and the first processing and the second processing are performed, and measurement information is information from an inspection apparatus that measures processing results of each of the first substrate that has passed through the first path, the second substrate that has passed through the second path, the third substrate that has passed through the third path, and the fourth substrate that has passed through the fourth path. An analysis system for analyzing information, comprising: a path information acquisition unit that acquires substrate identification information that identifies each of the substrates and path identification information for each of the paths; a measurement information acquisition unit that acquires the substrate identification information and the measurement information measured by the inspection device; and a calculation unit that calculates, based on the path identification information, the substrate identification information, and the measurement information, a first device characteristic that occurs in the first substrate that has been subjected to the first processing by the first device, a second device characteristic that occurs in the second substrate that has been subjected to the first processing by the second device, a third device characteristic that occurs in the third substrate that has been subjected to the second processing by the third device, and a fourth device characteristic that occurs in the fourth substrate that has been subjected to the second processing by the fourth device.

[0005] The exposure apparatus of the present invention exposes the fifth substrate using the correction conditions obtained using the above-described analysis apparatus.

[0006] A device manufacturing method of the present invention includes exposing the fifth substrate using the exposure apparatus described above, and developing the exposed fifth substrate.

[0007] A display manufacturing method of the present invention includes exposing the fifth substrate using the above-described exposure apparatus, and developing the exposed fifth substrate.

[0008] The analysis method of the present invention includes a first processing apparatus having a first device and a second device for performing a first processing on a substrate, and a second processing apparatus having a third device and a fourth device for performing a second processing on the substrate, wherein the substrate passes through any one of a first path passing through the first device and the third device, a second path passing through the first device and the fourth device, a third path passing through the second device and the third device, or a fourth path passing through the second device and the fourth device, and the first processing and the second processing are performed, and the analysis analyzes measurement information, which is information from an inspection apparatus that measures processing results of the first substrate that has passed through the first path, the second substrate that has passed through the second path, the third substrate that has passed through the third path, and the fourth substrate that has passed through the fourth path. The analysis method includes a path information acquisition unit that acquires substrate identification information that identifies each of the substrates and path identification information that identifies the path that each of the substrates has passed through; a measurement information acquisition step that acquires the substrate identification information and the measurement information that is the processing result of each of the substrates measured by the inspection device; and a calculation step that calculates, based on the path identification information and the measurement information, a first device characteristic that occurs in the first substrate that has been subjected to the first processing by the first device, a second device characteristic that occurs in the third substrate that has been subjected to the first processing by the second device, a third device characteristic that occurs in the first substrate that has been subjected to the second processing by the third device, and a fourth device characteristic that occurs in the second substrate that has been subjected to the second processing by the fourth device.

[0009] The analysis system of the present invention includes a first processing apparatus having a first device and a second device and performing a first processing on a first substrate, a second substrate, a third substrate, and a fourth substrate, and a second processing apparatus having a third device and a fourth device and performing a second processing on the first to fourth substrates, wherein the first to fourth substrates are subjected to the first processing and the second processing by passing through either a first path passing through the first device and the third device, a second path passing through the first device and the fourth device, a third path passing through the second device and the third device, or a fourth path passing through the second device and the fourth device, and An analysis system that analyzes measurement information, which is information from an inspection device that measures the processing results of a second substrate, a third substrate that has passed through the third path, and a fourth substrate that has passed through the fourth path, includes: a path information acquisition unit that acquires substrate identification information that identifies each of the substrates and path identification information that identifies the path that each of the substrates has passed; a measurement information acquisition unit that acquires the substrate identification information and the measurement information, which is the processing results of each of the substrates measured by the inspection device; a classification unit that classifies the measurement information into predetermined classification items based on the substrate identification information and the path identification information; and a display unit that displays the results classified by the classification unit.

[0010] The analysis system of the present invention includes a first processing apparatus having a first device and a second device for performing a first processing on a substrate, and a second processing apparatus having a third device and a fourth device for performing a second processing on the substrate, wherein the substrate passes through any one of a first path passing through the first device and the third device, a second path passing through the first device and the fourth device, a third path passing through the second device and the third device, or a fourth path passing through the second device and the fourth device, and the first processing and the second processing are performed, and among the substrates, a first substrate that has passed through the first path, a second substrate that has passed through the second path, a third substrate that has passed through the third path, and a fourth path are processed. An analysis system that analyzes measurement information, which is information from an inspection device that measures processing results for each of a passed fourth substrate, includes: a path information acquisition unit that acquires path identification information, which is information about a path that each of the substrates has passed; a measurement information acquisition unit that acquires the measurement information measured by the inspection device; and a calculation unit that calculates a first device characteristic that is a characteristic of the first device, a second device characteristic that is a characteristic of the second device, a third device characteristic that is a characteristic of the third device, and a fourth device characteristic that is a characteristic of the fourth device, based on the path identification information and the measurement information for the first substrate, the second substrate, the third substrate, and the fourth substrate.

[0011] 1 is a diagram illustrating an example of a processing device according to the first embodiment. FIG. 2 is a diagram illustrating an example of a measurement result according to the first embodiment. FIG. 3 is a diagram illustrating an example of the functional configuration of an analysis system according to the first embodiment. FIG. 4 is a flowchart illustrating a series of operations related to calculation of a correction value according to the first embodiment. FIG. 5 is a diagram illustrating an example of a correspondence relationship between a path and a measurement result according to the first embodiment. FIG. 6 is a flowchart illustrating a series of operations related to visualization of an amount of error according to the first embodiment. FIG. 7 is a diagram illustrating an example of visualization of an amount of error using a plate coordinate system according to the first embodiment. FIG. 8 is a diagram illustrating an example of visualization of an amount of error using a rectangular coordinate system according to the first embodiment. FIG. 9 is a diagram illustrating an example of visualization of an amount of error using a heat map according to the first embodiment. FIG. 10 is a diagram illustrating an example of a correction value for each lens module according to the first embodiment. FIG. 11 is a diagram illustrating an example of a correspondence relationship between a processing device and a correction value according to the first embodiment. FIG. 12 is a diagram illustrating an example of a correspondence relationship between a path and a correction value according to the first embodiment. FIG. 13 is a diagram illustrating an example of the functional configuration of an analysis system related to application of a correction value according to the first embodiment. FIG. 14 is a flowchart illustrating a series of operations related to application of a correction value according to the first embodiment. FIG. 15 is a diagram illustrating an example of a case where a correction value according to the first embodiment is selected based on a registered correction value for each device 100. FIG. 16 is a diagram illustrating an example of a case where a correction value according to the first embodiment is selected based on a registered path correction value. FIG. 17 is a diagram illustrating an example of the functional configuration of an analysis system according to the second embodiment. FIG. 10 is a diagram showing an example of the functional configuration of an analysis system according to the third embodiment. FIG. 11 is a diagram showing an example of the correspondence between paths and measurement results according to the third embodiment. FIG. 12 is a diagram showing an example of the functional configuration of an analysis system according to the fifth embodiment. FIG. 13 is a diagram showing an example of changes over time in the results of statistical calculation of measurement results according to the eighth embodiment. FIG. 14 is a diagram showing an example of changes over time in measurement results for each condition according to the eighth embodiment. FIG. 15 is a diagram showing an example of the functional configuration of an analysis device according to the eighth embodiment. FIG. 16 is a diagram for explaining the effect of classifying measurement results by tray according to the eighth embodiment. FIG. 17 is a diagram showing an example of the functional configuration of an analysis device according to the ninth embodiment. FIG. 18 is a diagram for explaining a correction simulation according to the ninth embodiment.Fig. 23 is a diagram for explaining an average difference according to the ninth embodiment; Fig. 24 is a diagram for explaining calculation of a correction value for an unknown route according to a modified example of the first embodiment;

[0012] First Embodiment An embodiment of the present invention will be described below with reference to the drawings. FIG. 1 is a diagram illustrating an example of a processing apparatus according to a first embodiment. An example of a processing apparatus 20 will be described with reference to the same figure. The processing apparatus 20 is an apparatus that performs a predetermined process on a substrate, such as a semiconductor wafer or a glass plate (hereinafter collectively referred to as a substrate), in a predetermined process P. The processing apparatus 20 may be, for example, a film-forming apparatus such as a sputtering apparatus or a CVD (Chemical Vapor Deposition) apparatus, a coating apparatus that applies a photosensitive material such as a photoresist, an exposure apparatus, a developing apparatus, an etching apparatus, or a heat treatment apparatus such as an annealing apparatus. The processing apparatus 20 has multiple apparatuses or portions where processing is performed, and one of these is referred to as apparatus 100. In the example shown in the same figure, apparatus 110, apparatus 111, apparatus 121, and apparatus 122 will be described as examples of apparatus 100 of the processing apparatus 20 in process P.

[0013] Apparatus 110 and apparatus 111, which are first processing apparatus 21, perform process P1. Apparatus 110 will also be referred to as the first apparatus, apparatus 111 as the second apparatus, and process P1 as the first process. Apparatus 110 and apparatus 111 may be, for example, film formation apparatuses that perform a film formation process on a substrate. Apparatus 121 and apparatus 122, which are second processing apparatus 22, perform process P2. Apparatus 121 will also be referred to as the third apparatus, apparatus 122 as the fourth apparatus, and process P2 as the second process. Apparatus 121 and apparatus 122 may be photoresist coating apparatuses that coat photoresist. In the example shown in FIG. 1 , process P1 is performed by apparatus 110 or apparatus 111, and process P2 is performed by either apparatus 121 or apparatus 122. That is, in the example shown in the figure, a substrate is processed through any one of route R1, which is processed by apparatus 110 and apparatus 121, route R2, which is processed by apparatus 110 and apparatus 122, route R3, which is processed by apparatus 111 and apparatus 121, and route R4, which is processed by apparatus 111 and apparatus 122. Route R1 will also be referred to as the first route, route R2 as the second route, route R3 as the third route, and route R4 as the fourth route. A substrate that has been subjected to the first and second treatments by passing through the first route will also be referred to as the first substrate, a substrate that has been subjected to the first and second treatments by passing through the second route as the second substrate, a substrate that has been subjected to the first and second treatments by passing through the third route as the third substrate, and a substrate that has been subjected to the first and second treatments by passing through the fourth route as the fourth substrate.

[0014] FIG. 2 is a diagram showing an example of a measurement result according to the first embodiment. An example of a measurement result MR according to the first embodiment will be described with reference to the same figure. The measurement result MR is the result of measuring the amount of error that occurs in the substrate MR after a predetermined process is performed on the substrate MR. FIG. 2A shows a measurement result (processing result) MR1 as an example of the measurement result MR. The measurement result MR1 is the result of measuring the amount of error that occurs in the substrate after processing is performed by path R1. The measurement result MR1 is the measurement result for the entire substrate. Multiple product patterns are exposed onto a single substrate, and the exposure of one product is called one scan. In FIG. 2A, four products are exposed. The measurement result MR1 collectively displays measurement results MR1-1, MR1-2, MR1-3, and MR1-4, which are the measurement results for each scan. FIG. 2B shows measurement result MR2 as an example of the measurement result MR. Measurement result MR2 is the result of measuring the amount of error that occurred in the substrate after processing via path R2. Measurement result MR2 is the measurement result for the entire substrate. Measurement result MR2 collectively displays measurement results MR2-1, MR2-2, MR2-3, and MR2-4, which are the measurement results for each scan. Measurement result MR1 and measurement result MR2 are different. That is, the amount of error that occurs in the substrate differs depending on the path the substrate passes through. Here, the amount of error refers to the difference between the design value of the pattern to be formed on the substrate and the processing result (measurement result) MR obtained from the inspection device 220, and is also referred to as the amount of pattern deviation. Here, the design value of the pattern refers to the position that would be exposed if the substrate were processed by the processing device 20 (device 100) and not deformed. The amount of error may also be the amount of deformation of the entire substrate before and after processing by the processing device 20.

[0015] Calculation of the path difference correction value according to the first embodiment will be described with reference to Fig. 3 to Fig. 12. Fig. 3 is a diagram showing an example of the functional configuration of the analysis system 10 according to the first embodiment. With reference to the same figure, an example of the functional configuration of the analysis system 10 will be described. The analysis system 10 analyzes device identification information DID, which is information from a plurality of processing devices 100, and measurement information MI, which is information from the inspection device 220.

[0016] The inspection device 220 inspects substrates. Specifically, the inspection device 220 measures the processing results of a first substrate, a second substrate, a third substrate, and a fourth substrate. The first to fourth substrates are sometimes referred to as sample substrates. A sample substrate refers to a substrate that is exposed to light in order to calculate the device characteristics (error amounts) caused by each processing device 100. More specifically, the inspection device 220 measures the amount of error generated in the substrate after the substrate is subjected to a predetermined process by the processing device 20 (after the predetermined process is performed and the substrate is exposed by the exposure device). The inspection device 220 outputs measurement information MI, including measurement results MR after the substrate is subjected to the process, together with substrate identification information SID for identifying the substrate, to the analysis device 300. Note that the inspection device 220 may acquire the measurement results MR by performing measurements after a single process is performed on the substrate by a single processing device 20, or may acquire the measurement results MR by performing measurements after multiple processes are performed on the substrate by multiple processing devices 20.

[0017] The analysis device 300 includes a path information acquisition unit 310, a measurement information acquisition unit 320, a calculation unit 330, and a storage unit 340. The analysis device 300 calculates the amount of error (device characteristics) caused in the substrate by each of the devices 100 (device 110, device 111, device 121, and device 122). The analysis device 300 may acquire information related to the processing performed on the substrate along with the device identification information DID and substrate identification information SID from the device 100. The information related to the processing performed on the substrate may be, for example, the processing conditions for the processing performed on the substrate. The analysis device 300 may also calculate the amount of error caused in the substrate by the processing device 20 using a method similar to that described below.

[0018] The path information acquisition unit 310 acquires device identification information DID and substrate identification information SID from the processing device 20 or the devices 100 (devices 110, 111, 121, and 122) within the processing device. The path information acquisition unit 310 obtains path identification information RID by acquiring the device identification information DID and the substrate identification information SID, and outputs the path identification information RID to the calculation unit 330. The path identification information RID is information that identifies the multiple processing devices 100 that performed the processes on a given substrate that has been subjected to multiple processes. Specifically, the path identification information RID is information for identifying which of the paths R1, R2, R3, and R4 the substrate passed through for processing. That is, the path information acquisition unit 310 acquires the substrate identification information SID that identifies the substrate and the path identification information RID of the first, second, third, and fourth paths through which the substrate passed.

[0019] The measurement information acquisition unit 320 acquires the substrate identification information SID and the measurement information MI measured by the inspection device 220. The measurement information acquisition unit 320 also acquires the measurement information MI from each of the multiple substrates. That is, the measurement information acquisition unit 320 acquires the substrate identification information SID and measurement information that is the processing result of the first substrate, the processing result of the second substrate, the processing result of the third substrate, and the processing result of the fourth substrate measured by the inspection device 220.

[0020] The calculation unit 330 calculates an apparatus characteristic CI for each apparatus 100 based on the acquired path identification information RID, measurement information MI, and substrate identification information SID. The apparatus characteristic for each apparatus 100 may be an amount of error imparted to a substrate due to the processing of the apparatus 100, which occurs when the substrate is processed by the apparatus 100. In this embodiment, the first apparatus and the second apparatus perform a first process, and the third apparatus and the fourth apparatus perform a second process. That is, based on the path identification information RID and the measurement information MI, the calculation unit 330 calculates a first apparatus characteristic for a substrate that has been subjected to the first process by the first apparatus, a second apparatus characteristic for a substrate that has been subjected to the first process by the second apparatus, a third apparatus characteristic for a substrate that has been subjected to the second process by the third apparatus, and a fourth apparatus characteristic for a substrate that has been subjected to the second process by the fourth apparatus.

[0021] The storage unit 340 stores the characteristics calculated by the calculation unit 330. The storage unit 340 stores a first device characteristic, a second device characteristic, a third device characteristic, and a fourth device characteristic.

[0022] 4 is a flowchart showing a series of operations related to calculation of correction values ​​according to the first embodiment. With reference to this figure, a series of operations performed by the analysis system 10 when calculating correction values ​​will be described.

[0023] (Step S110) The analysis device 300 collects measurement information MI, device identification information DID, and substrate identification information SID. Specifically, the path information acquisition unit 310 acquires the device identification information DID and substrate identification information SID, and the measurement information acquisition unit 320 acquires the measurement information MI and substrate identification information SID. The path information acquisition unit 310 acquires path information RID corresponding to each substrate from the substrate identification information DID and substrate identification information SID. Furthermore, the path information acquisition unit 310 may acquire the path identification information RID directly from an external device, etc., instead of the device identification information DID.

[0024] (Step S120) The analysis device 300 organizes the measurement information MI (error amount) for each path (R1, R2, R3, and R4) and information about the device 100 through which the substrate has passed. Specifically, the analysis device 300 organizes the information about the device 100 on each path through which the substrate has passed, in association with the measurement information MI for the substrate that has passed through each path. More specifically, the analysis device 300 associates the path identification information RID with the measurement information MI based on the acquired correspondence relationship I1 between the path identification information RID and the substrate identification information SID and the correspondence relationship between the measurement information MI and the substrate identification information SID.

[0025] FIG. 5 is a diagram showing an example of the correspondence between paths and measurement results according to the first embodiment. Information organization performed by the analysis device 300 will be described with reference to the diagram. In the example shown in the diagram, information I1, which is information about the tool 100 through which each path passes, and information I2, which is measurement information MI including measurement results MR of the substrate that passed through each path, are associated with each other. The measurement information MI is measurement information including measurement results MR, which are the results of measuring a substrate processed through process 1 + process 2 + process 3 + ... process N. From this, the impact of each process is identified. Furthermore, even within a single process, if there are multiple tools 100 (e.g., tool 110 and tool 111), the degree of impact each tool has on the substrate differs. Therefore, the impact of tool 110 on the substrate and the impact of tool 111 on the substrate are identified. Information indicating the impact of tool 110 (first tool characteristic) and information indicating the impact of tool 111 (second tool characteristic) are referred to as tool characteristics.

[0026] Specifically, information I1, which is information on the apparatus 100 through which each path has passed, indicates that a first substrate that has passed through a path with substrate identification information SID "S01" and path identification information RID "R01" is processed by apparatus 110 as process P1 and by apparatus 121 as process P2 with substrate identification information SID "S03"; a second substrate that has passed through a path with path identification information RID "R02" is processed by apparatus 110 as process P1 and by apparatus 122 as process P2; a third substrate that has passed through a path with substrate identification information SID "S03" and path identification information RID "R03" is processed by apparatus 111 as process P1 and by apparatus 121 as process P2; and a fourth substrate that has passed through a path with substrate identification information SID "S04" and path identification information RID "R04" is processed by apparatus 111 as process P1 and by apparatus 122 as process P2. Furthermore, information I2, which is the measurement information MI of the substrates that have passed through each path, is associated with "Meas.1" as the measurement information MI of the first substrate that has passed through path R1, "Meas.2" as the measurement information MI of the second substrate that has passed through path R2, "Meas.3" as the measurement information MI of the third substrate that has passed through path R3, and "Meas.4" as the measurement information MI of the fourth substrate that has passed through path R4. Here, path identification information RID and measurement information MI are associated, but only measurement results MR may be associated with path identification information RID.

[0027] 4, the analysis device 300 calculates the device characteristics (error amount) for each device based on the organized information. As an example, the analysis device 300 calculates the device characteristics for each device 100 using the least squares method.

[0028] Here, an example will be described in which the analysis device 300 calculates the device characteristics for each device 100 using the least squares method. The calculation unit 330 performs analytical calculations using simultaneous equations or the least squares method based on the route identification information RID of each route and the measurement information MI. In a specific method of the least squares method, the calculation unit 330 minimizes an objective function based on squared errors shown in the following equation (1) to calculate the device characteristics for each device 100.

[0029]

[0030] Here, i is the path identification information RID, j indicates the j-th measurement point of the measurement coordinates of the substrate, β indicates the amount of error of each device at the j-th measurement point, and x is the condition vector for path i, i.e., path information.

[0031] In this case, the minimization objective function (normal equation) is expressed by the following equation (2).

[0032]

[0033] That is, the processing result (error amount) for path i can be obtained by solving the following equation (3).

[0034]

[0035] When calculating the device characteristic (error amount) by the least squares method, the device characteristic can be obtained by solving the following equation (4).

[0036]

[0037] That is, the device characteristic β is expressed by the following equation (5).

[0038]

[0039] The analysis device 300 may visualize the calculated device characteristics for each device 100 using a predetermined method. Fig. 6 is a flowchart showing a series of operations related to visualization of device characteristics according to the first embodiment. The series of operations for visualization of device characteristics will be described with reference to the same figure.

[0040] (Step S131) ​​The calculation unit 330 visualizes the calculated apparatus characteristics for each apparatus 100 by comparing them (in the Plate coordinate system) with the state of the substrate before the substrate is processed by the apparatus 100. More specifically, the Plate coordinate system represents the amount of deviation (error amount) of the pattern from each position on the substrate. The amount of deviation of the pattern at each position on the substrate is calculated based on the state before processing by the apparatus 100 and the state of the substrate after processing by the apparatus 100. The calculation unit 330 visualizes β, which is the result obtained by, for example, the least squares method, using the Plate coordinate system.

[0041] 7 is a diagram showing an example of visualization of apparatus characteristics using a plate coordinate system according to the first embodiment. An example of a plate coordinate system PC will be described with reference to the same figure. Four plate coordinate systems PC are shown in the same figure as an example of the plate coordinate system PC. Plate coordinate system PC1 indicates the amount of pattern misalignment caused by the apparatus 110 (hereinafter referred to as the apparatus characteristic (error amount) of the apparatus 110), plate coordinate system PC2 indicates the apparatus characteristic of the apparatus 111, plate coordinate system PC3 indicates the apparatus characteristic of the apparatus 121, and plate coordinate system PC4 indicates the apparatus characteristic of the apparatus 122.

[0042] Here, the processing device 20 has two or more devices 100 (for example, device 110 and device 111 of device 21 in process P1), but the unit of device 100 can be set arbitrarily. For example, if there are two processing devices for processing process P1, the two devices are collectively defined as processing device 20, and one of the two devices is defined as device 100 (115) and the other is defined as device 100 (118). Furthermore, if the apparatus 100 (115) has two processing sections and the two processing sections have different effects on the substrate, the apparatus 100 (115) may be further subdivided, with processing section 1 of the apparatus 100 being apparatus 100 (116) and processing section 2 of the apparatus 100 being apparatus 100 (117).Also, even if the same apparatus 100 (for example, apparatus 110) is used, if the processing conditions (processing temperature, processing time, etc.) are different and the effects on the substrate are different, the apparatus 110 may be subdivided, with apparatus 1101 being used under processing condition A and apparatus 1102 being used under processing condition B.

[0043] By visualizing the data using the plate coordinate system PC and displaying the visualized results on the characteristic display unit 331, it is possible to visualize the trends in the device characteristics (errors) of each device 100, and the trends in the device characteristics (errors) can be easily visually confirmed. Furthermore, the calculation unit 330 may visualize the device characteristics using other methods that can quantitatively determine the device characteristics, such as the method in step S132 or step S133. Here, the characteristic display unit 331 may be, for example, a liquid crystal display, an organic EL (electroluminescence) display, or the like, as long as it is capable of displaying the device characteristics.

[0044] (Step S132) Returning to Fig. 6, the calculation unit 330 uses an orthogonal function system to expand the apparatus characteristics (error amounts) of each apparatus 100. For example, the calculation unit 330 expands the apparatus characteristics on the substrate obtained in step S131 using the orthogonal function system.

[0045] FIG. 8 is a diagram showing an example of visualization of device characteristics (error amounts) using an orthogonal coordinate system according to the first embodiment. An example of an orthogonal coordinate system OF will be described with reference to the diagram. The diagram shows an example of coefficient expansion using an orthogonal function system as an example of an orthogonal function system OF. FIGS. 8A and 8B each show the device characteristics of a predetermined device 100 when Legendre polynomials are used. The example shown in the diagram shows values ​​obtained by expanding coefficients into coefficients of each term from 1 to 18 using an orthogonal function system.

[0046] By visualizing the amount of deformation component (error amount) in each term using the orthogonal function system OF, it is possible to quantitatively determine the difference in the trends of the device characteristics (error amount) of each device 100. For example, it is easy to determine whether the device characteristics of any two devices 100 (e.g., device 110 and device 111) shown in FIGS. 8A and 8B have similar trends. Furthermore, as a specific example, when the calculation unit 330 determines from FIGS. 8A and 8B that the error amount trends are common between device 110 and device 111 in the coefficients L14 and L15, the correction values ​​for L14 and L15 may be applied collectively to the correction values ​​for the recipe described below, rather than calculating them for each device 100 (device 110, device 111). This specific example also applies to the heat map described below. Furthermore, when visualization is performed using the orthogonal function system OF, the calculation unit 330 may visualize the data using other methods that facilitate comparison between multiple devices 100, such as step S133, in order to compare the data between multiple devices 100.

[0047] (Step S133) Returning to FIG. 6 , the calculation unit 330 colors the device characteristics (error amounts) of each device 100 in order of increasing to decreasing error amounts, dividing them by color and shade (heat map). By arranging these in a matrix, the coefficients of the orthogonal function system can be easily compared and visualized. For example, the calculation unit 330 visualizes the results of coefficient expansion using the orthonormal functions in step S132 using a heat map, and displays the visualized results on the characteristic display unit 331.

[0048] FIG. 9 is a diagram showing an example of visualization of device characteristics (error amounts) using a heat map according to the first embodiment. An example of the heat map HM will be described with reference to the figure. The figure shows an example of a heat map HM in which coefficients 1 to 18 are displayed when coefficients for "device 110," "device 111," "device 121," and "device 122" are expanded using an orthonormal function system. The calculation unit 330, for example, assigns the coefficients for each device 100 values ​​ranging from +3 to -3 and displays them. Visualization using the heat map HM makes it easy to compare differences in trends between multiple devices 100.

[0049] (Step S140) Returning to FIG. 4 , the calculation unit 330 calculates a correction value PM for each apparatus 100 (a first correction value PM1 for apparatus 110, a second correction value PM2 for apparatus 111, a third correction value PM3 for apparatus 121, and a fourth correction value PM4 for apparatus 122). The calculation unit 330 may calculate the apparatus characteristics (error amounts) for each apparatus 100, calculated using the least squares method, as a correction value for each lens module 370 included in the exposure apparatus, in order to apply the correction values ​​when exposing a substrate using the exposure apparatus, or may calculate the correction values ​​as a correction value for driving an actuator or the like installed in the projection optical system. Furthermore, the calculation unit 330 may calculate the apparatus characteristics for each apparatus 100 as a correction value for driving a stage on which a substrate is placed. In other words, the correction conditions may include the drive conditions of a drive unit for a substrate stage on which the substrate is held.

[0050] FIG. 10 is a diagram showing an example of correction values ​​according to the first embodiment. The calculation of the correction values ​​will be described with reference to the same figure. FIG. 10 shows an example of apparatus characteristics (error amounts) for each apparatus 100. The apparatus characteristics for each apparatus 100 may be, for example, the results obtained using the least squares method in step S131. The correction values ​​for each apparatus 100 are calculated from the apparatus characteristics for each apparatus 100 shown in FIG. 10. The calculation unit 330 uses a correction value calculation function to calculate a correction value for each lens module 371 among the multiple lens modules 370. More specifically, the calculation unit 330 calculates a correction value 411 for correcting an exposure area 401 exposed by the lens module 371 within an exposure area 400 on the entire substrate.

[0051] (Step S150) Returning to FIG. 4 , the analysis device 300 stores information about the device 100 and a correction value corresponding to the device 100. For example, the analysis device 300 stores information about the device 100 and information about the correction value PM corresponding to the device 100 as correspondence information CI in the storage unit 340. Note that the analysis device 300 may store the first processing device 20 and / or the second processing device 21 and the correction value PM in association with each other, or may store the path and the correction value in association with each other.

[0052] 11 is a diagram showing an example of the correspondence relationship between processing devices and correction values ​​according to the first embodiment. Referring to the figure, an example of storing the devices 100 (device 110, device 111, device 121, device 122) in association with the correction values ​​PM is shown. The figure shows correspondence information CI including correspondence information CI1, correspondence information CI2, correspondence information CI3, and correspondence information CI4. The device 100 is identified by device identification information DID. In the example shown in the figure, the correction value is a correction value PM calculated for each device 100. Correspondence information CI1 associates one device 100, "device 110," with a correction value PM "first correction value," correspondence information CI2 associates one device 100, "device 111," with a correction value PM "second correction value," correspondence information CI3 associates one device 100, "device 121," with a correction value PM "third correction value," and correspondence information CI4 associates one device 100, "device 122," with a correction value PM "fourth correction value." The storage unit 340 stores these pieces of correspondence information CI.

[0053] FIG. 12 is a diagram showing an example of the correspondence relationship between paths and correction values ​​according to the first embodiment. Referring to FIG. 12, an example of storing correspondence between an apparatus 100 and a path is shown. The correspondence information CI includes correspondence information CI11, correspondence information CI12, correspondence information CI13, and correspondence information CI14. Paths are identified by path identification information RID. In the example shown in FIG. 12, a substrate passing through path R1 is processed by "apparatus 110" in process P1 and by "apparatus 121" in process P2. A substrate passing through path R2 is processed by "apparatus 110" in process P1 and by "apparatus 122" in process P2. A substrate passing through path R3 is processed by "apparatus 111" in process P1 and by "apparatus 121" in process P2. Furthermore, a substrate passing through path R4 is processed by "device 111" in process P1 and by "device 122" in process P2. Path R1 is associated with "path correction value 1" which is a path correction value RPM, path R2 is associated with "path correction value 2" which is a path correction value RPM, path R3 is associated with "path correction value 3" which is a path correction value RPM, and path R4 is associated with "path correction value 4" which is a path correction value RPM. The storage unit 340 may store these correspondence information CI.

[0054] [Application of Path Difference Correction Value] The application of the path difference correction value according to the first embodiment will be described with reference to Figures 13 to 16. The analytical apparatus 300 transmits the correction value calculated by the method described above to the exposure apparatus, and the exposure apparatus applies the correction value according to the path that the substrate has passed through.

[0055] 13 is a diagram showing an example of the functional configuration of an analysis system related to application of a correction value according to the first embodiment. An example of the functional configuration of the analysis system 10 will be described with reference to the same figure. In the description of the analysis system 10, components already described with reference to FIG. 3 may be denoted by the same reference numerals and description thereof may be omitted. The analysis system 10 further includes a selection unit 350 and an output unit 360.

[0056] The correction condition calculation unit 351 calculates correction conditions when exposing the substrate. By including the selection unit 350, the correction condition calculation unit 351 selects correction values ​​related to two apparatuses through which the substrate has passed from the first correction value, second correction value, third correction value, and fourth correction value calculated by the calculation unit 330 and stored in the storage unit 340, based on the substrate identification information SID and path identification information RID of the substrate. The correction condition calculation unit 351 calculates correction conditions when exposing the substrate based on the correction values ​​related to the two selected apparatuses.

[0057] The selection unit 350 selects correction conditions AR for exposing a substrate based on multiple correction values ​​stored in the storage unit 340. Specifically, the selection unit 350 acquires path identification information RID and substrate identification information SID from the path information acquisition unit 310 and acquires a correction value corresponding to the path identification information RID from the storage unit 340. The correction value corresponding to the path identification information RID is a correction value corresponding to the device characteristics of the device 100 included in each path. Each path has two or more processes (e.g., a first process and a second process). Therefore, the correction value corresponding to the path identification information RID is based on at least a correction value related to the first processing device (a first correction value related to the device 110 or a second correction value related to the device 111) and a correction value related to the second processing device (a third correction value related to the device 121 or a fourth correction value related to the device 122). That is, the selection unit 350 selects the correction conditions AR to be applied when exposing the substrate that has been subjected to the first and second processes on the path identified by the acquired path identification information RID, based on two of the four correction values ​​(first to fourth correction values) stored in the memory unit 340.

[0058] The output unit 360 acquires the correction conditions AR selected by the selection unit 350. The output unit 360 outputs the acquired correction conditions AR to the exposure apparatus 230. Specifically, the output unit 360 outputs the correction conditions AR to the exposure apparatus 230 that exposes a substrate corresponding to the correction conditions AR. The exposure apparatus 230 exposes the substrate corresponding to the correction conditions AR based on the acquired correction conditions AR.

[0059] 14 is a flowchart showing a series of operations related to application of a correction value according to the first embodiment. The series of operations performed by the analysis system 10 when applying a correction value will be described with reference to the same drawing.

[0060] (Step S210) The analysis apparatus 300 collects path information for a substrate in production. Here, the substrate in production refers to a substrate (fifth substrate) different from the substrates (first to fourth substrates) used when calculating the correction value PM in steps S110 to S150. The fifth substrate is also sometimes referred to as the object substrate. More specifically, the object substrate is not a substrate used to calculate the apparatus characteristics for each processing apparatus 100, but rather a substrate to be exposed using the calculated apparatus characteristics for each processing apparatus 100. The path information acquisition unit 310 acquires the apparatus identification information DID and the substrate identification information SID from the substrate in production, and acquires the path identification information RID. Alternatively, the path information acquisition unit 310 may acquire the path identification information RID directly from the substrate in production. The substrate in production identified by the substrate identification information SID is a substrate that has been subjected to a predetermined process by the processing apparatus 100, but has not yet been exposed to light corresponding to that predetermined process.

[0061] (Step S220) Analysis device 300 organizes the route information for each board in production. Specifically, analysis device 300 identifies information about the apparatus 100 included in each route that the board has passed through. The information about the apparatus 100 on each route that the board has passed through may be included in route identification information RID.

[0062] (Step S230) The analysis device 300 compares the path information for each board in production with the path information for the registered paths. Specifically, the selection unit 350 included in the correction condition calculation unit 351 compares the tool 100 identified in step S220 with the correction values ​​PM stored in the storage unit 340, thereby obtaining the correction values ​​PM corresponding to the tool 100 along each path through which the board in production has passed. For example, if the path R1 passes through the tool 110 and the tool 121, the selection unit 350 obtains, from the storage unit 340, a first correction value, which is the correction value PM corresponding to the tool 110, and a third correction value, which is the correction value PM corresponding to the tool 121.

[0063] (Step S240) The analytical device 300 determines the correction value PM for the substrate being produced. Specifically, the correction condition calculation unit 351 may calculate the correction value PM corresponding to the path R1 identified by the path identification information RID based on the first correction value and the third correction value PM selected in step S230, or may select the path correction value RPM for each path stored in the storage unit 340 as the path correction value RPM corresponding to the path identified by the path identification information RID.

[0064] FIG. 15 is a diagram illustrating an example of the selection of a correction value according to the first embodiment, where the correction value is selected based on the correction value PM for each registered tool 100. Referring to FIG. 15, an example of the selection of a correction value for each registered tool 100 based on the PM will be described. The diagram illustrates an example of the case where the board identification information SID is "S01" and an example of the case where the board identification information SID is "S02." A board with board identification information SID "S01" is processed by a route with route identification information RID "R01." For the route with route identification information RID "R01," information about the tools 100 included in the route is identified in step S220. Specifically, it is identified that the route with route identification information RID "R01" includes the tools 110 and 121. A selection unit 350 included in a correction condition calculation unit 351 selects a first correction value, which is a correction value for the apparatus 110, and a third correction value, which is a correction value for the apparatus 121, which are stored in the storage unit 340, and the correction condition calculation unit 351 calculates a path correction value RPM by adding up the first correction value and the third correction value. In the example shown in Fig. 15, the sum of the "first correction value" which is the correction value PM for the apparatus 110 and the "third correction value" which is the correction value PM for the apparatus 121 becomes the path correction value RPM, and is applied to a substrate having substrate identification information SID of "S01". Similarly, the sum of the "first correction value" which is the correction value PM of device 110 and the "fourth correction value" which is the correction value PM of device 122 becomes the path correction value RPM, and is applied to a substrate whose substrate identification information SID is "S02", the sum of the "second correction value" which is the correction value PM of device 111 and the "third correction value" which is the correction value PM of device 121 becomes the path correction value RPM, and is applied to a substrate whose substrate identification information SID is "S03", and the sum of the "second correction value" which is the correction value PM of device 111 and the "fourth correction value" which is the correction value PM of device 122 becomes the path correction value RPM, and is applied to a substrate whose substrate identification information SID is "S04".

[0065] FIG. 16 is a diagram illustrating an example of the selection of a correction value according to the first embodiment, where the correction value is selected based on a registered path correction value. An example of the selection based on a registered path correction value will be described with reference to the same figure. The components already described in FIG. 16 are denoted by the same reference numerals, and their description may be omitted. In this example, the selection unit 350 selects a path correction value RPM based on the path correction value RPM stored in the storage unit 340, and the correction condition calculation unit 351 uses the path correction RPM selected by the selection unit 350 as the correction condition. In the example shown in FIG. 16 , “path correction value 1” corresponding to a path whose path identification information RID is “R01” is the path correction value RPM, and is applied to a substrate whose substrate identification information SID is “S01.” Similarly, "path correction value 2" corresponding to a path having path identification information RID of "R02" becomes the path correction value RPM and is applied to a substrate having substrate identification information SID of "S02", "path correction value 3" corresponding to a path having path identification information RID of "R03" becomes the path correction value RPM and is applied to a substrate having substrate identification information SID of "S03", and "path correction value 4" corresponding to a path having path identification information RID of "R04" becomes the path correction value RPM and is applied to a substrate having substrate identification information SID of "S04". By calculating and storing the path correction value RPM in advance, the time required to calculate the path correction value RPM can be saved, thereby shortening the processing time.

[0066] (Step S250) Returning to FIG. 14 , the analysis device 300 transmits information about the substrate being produced and the path correction value RPM to the exposure device 230. Specifically, the output unit 360 receives information including the substrate identification information SID and the path correction value RPM from the correction condition calculation unit 351 to the exposure device 230, and outputs the information as the correction condition AR. The correction condition AR may also include other conditions applied to exposure. The correction condition AR may be output for each lens module 370 included in the exposure device to be used as a correction value when exposing a substrate using the exposure device, or may be output as a correction condition for driving an actuator or the like installed in the projection optical system. The correction condition AR may also be calculated as a correction condition for driving a stage on which the substrate is placed to perform correction.

[0067] (Step S260) A substrate in production identified by substrate identification information SID is loaded into exposure tool 230. (Step S270) Exposure tool 230 adds a path correction value RPM included in correction conditions AR obtained from analysis tool 300 to the correction value of Recipe, and performs exposure. Here, the recipe correction value does not refer to an error amount dependent on tool 100, but rather refers to a correction value that is applied regardless of which path a substrate passes through as long as the processing conditions for the substrate are the same. If the processing conditions (including processing time and processing temperature) are the same regardless of whether path identification information RID is "R01," "R02," "R03," or "R04," the same recipe correction value is applied. Furthermore, when output unit 250 outputs path correction value RPM to exposure tool 230 in step S250, the recipe correction value may be output to exposure tool 230 as a correction condition that includes the path correction value RPM and the recipe correction value.

[0068] 28 , as a modification of the first embodiment, this embodiment is useful when the first processing apparatus 21 in process P1 includes not only apparatus 110 and apparatus 111 but also apparatus 112, and has an unknown route through which a substrate has never passed in processes P1 and P2, or when apparatus 112 is newly added to the first processing apparatus 21 in process P1. More specifically, when there is a route R5 that passes through apparatus 112 in process P1 and apparatus 121 in process P2, the calculation unit calculates a fifth apparatus characteristic, which is an apparatus characteristic (error amount) related to apparatus 112, using the method described above, and calculates a fifth correction value that corrects the fifth apparatus characteristic. Here, even if a substrate has never passed through route R6 that passes through apparatus 112 in process P1 and apparatus 122 in process P2, it is possible to calculate a route correction value related to route R6. This is possible by selecting the fifth correction value of device 112 and the fourth correction value of device 122 in the selection unit 350, and adding up the fifth correction value and the fourth correction value in the correction condition calculation unit 351, thereby calculating the correction condition of route R6.

[0069] [Summary of Effects of First Embodiment] As described above, according to this embodiment, the analysis apparatus 300 includes a path information acquisition unit 310 to acquire information identifying the path the substrate has passed through, and a measurement information acquisition unit 320 to identify the substrate. Furthermore, the analysis apparatus 300 includes a calculation unit 330 to calculate the amount of error for each apparatus 100 (apparatus 110, apparatus 111, apparatus 121, and apparatus 122) included in the path and calculate a correction value PM that corrects the amount of error for each apparatus 100. Therefore, according to this embodiment, it is possible to calculate the error generated in the substrate for each apparatus 100 that processed the substrate. According to this embodiment, it is possible to calculate the amount of error for each apparatus, and therefore it is possible to correct the exposure pattern according to the apparatus 100.

[0070] Furthermore, according to this embodiment, since a correction value can be calculated for each tool 100 (tool 110, tool 111, tool 121, tool 122), a correction value can be predicted even for a new path by combining the correction values ​​of known tools 100. According to this embodiment, since the path correction value can be expressed as the sum of the correction values ​​for each tool 100, an error amount can be calculated even for a new path (a combination of known tools 100) by adding up the correction values. Furthermore, according to this embodiment, the correction condition calculation unit 351 calculates the correction condition by adding up the characteristics of two tools from among the first tool, second tool, third tool, and fourth tool through which the substrate has passed. Therefore, according to this embodiment, the correction condition can be easily calculated.

[0071] Furthermore, according to this embodiment, the analysis apparatus 300 calculates the amount of error for each apparatus 100 (apparatus 110, apparatus 111, apparatus 121, apparatus 122) based on measurement information MI acquired from multiple substrates, and calculates a correction value PM that corrects the amount of error for each apparatus 100. Therefore, according to this embodiment, it is possible to calculate a more accurate and reliable amount of error for each apparatus 100. Therefore, according to this embodiment, the analysis apparatus 300 can correct the exposure pattern according to the apparatus 100 based on the more accurate and reliable amount of error.

[0072] Furthermore, according to this embodiment, the measurement information acquisition unit 320 acquires measurement information MI measured after at least the first process and the second process are performed. The calculation unit 330 calculates the device characteristics (first and second device characteristics) of the two or more first processing devices 21 (devices 110 and 111) and the device characteristics (third and fourth device characteristics) of the two or more second processing devices 22 (devices 121 and 122) based on the multiple pieces of measurement information MI, and calculates correction values ​​(first to fourth correction values) for correcting each device characteristic. That is, according to this embodiment, correction values ​​for each device 100 (110, 111, 121, and 122) can be calculated from the measurement information MI after multiple processes are performed. However, according to conventional technology, it was not possible to calculate the amount of error generated by each device 100 (device 110, device 111, device 121, and device 122) from the results measured for each path. Therefore, even if the error amount for each path is stored, if the tool 100 included in the path is changed, the correction value based on the stored error amount becomes unapplicable. According to this embodiment, the error amount for each tool 100 can be calculated. Therefore, even if one tool 100 among the multiple tools 100 included in the path is changed, the error amounts for the other tools can continue to be used. Therefore, according to this embodiment, more data on the error amount for each tool can be accumulated, and the exposure pattern can be corrected according to the tool 100 based on more accurate and reliable error amounts. Furthermore, the analysis tool 300 includes a correction condition calculation unit 351, which calculates correction conditions for exposing the substrate. Furthermore, the correction condition calculation unit 351 includes a selection unit 350, which selects correction values ​​for the two tools through which the substrate has passed from the first, second, third, and fourth correction values ​​stored in the storage unit 340 based on the substrate identification information SID and the path identification information RID. The correction condition calculation unit 351 calculates correction conditions based on the correction values ​​for the two selected tools. Therefore, according to this embodiment, the analysis device 300 can calculate the correction conditions according to the path that the substrate has taken.

[0073] Furthermore, according to this embodiment, the analysis apparatus 300 includes a storage unit 340 that stores at least a first correction value, a second correction value, a third correction value, and a fourth correction value, and includes a selection unit 350 that selects, based on the stored first correction value, the second correction value, the third correction value, and the fourth correction value, correction conditions to be applied when exposing a substrate that has been subjected to the first process and the second process along a path identified by the path identification information RID. That is, the analysis apparatus 300 calculates exposure conditions to be applied to the substrate from the amount of error of each apparatus 100. Therefore, according to this embodiment, it is possible to select the amount of error for each path through which a substrate identified by the substrate identification information SID has passed.

[0074] Furthermore, according to this embodiment, analysis apparatus 300 includes output unit 360, which outputs the correction conditions selected by selection unit 350 to exposure apparatus 230. Therefore, analysis apparatus 300 can apply the selected error amount as a correction value when performing exposure.

[0075] Furthermore, according to this embodiment, exposure apparatus 230 exposes the substrate using the correction conditions obtained by analysis apparatus 300. Therefore, exposure apparatus 230 can correct the exposure pattern in accordance with processing apparatus 100 using the amount of error calculated for each apparatus 100.

[0076] Furthermore, the processing device 20 may be a tray or photomask used when placing a substrate in an exposure device, or an electronic mask (DMD, SLM) having multiple micromirrors. More specifically, in the case of trays, if there are multiple trays for placing substrates in the exposure device, the entire tray may be the processing device 20, and one of the trays may be the device 100. In the case of photomasks, if there are multiple masks having the same pattern, the multiple masks may be the processing device 20, and one of the photomasks may be the device 100. In the case of electronic masks, the multiple electronic masks may be the processing device 20, and one of the electronic masks may be the device 100.

[0077] Furthermore, according to this embodiment, the analysis device 300 calculates the amount of error and the correction value for each device 100, but it may also calculate the amount of error and the correction value for each processing device 20 and perform the same analysis as described above.

[0078] Second Embodiment FIG. 17 is a diagram showing an example of an analysis system according to a second embodiment. An example of an analysis system 10A will be described with reference to the same figure. In the description of the analysis system 10A, the same components as those in the analysis system 10 are denoted by the same reference numerals, and description thereof may be omitted. The analysis system 10A differs from the analysis system 10 in that the analysis apparatus 300 is connected to multiple factories M via a predetermined network NW. The predetermined network NW may be, for example, the Internet. Each of the multiple factories M includes multiple processing apparatuses 100, an inspection apparatus 220, and an exposure apparatus 230.

[0079] Processing device 20 or device 100 (or device 110, device 111, device 121, or device 122) within processing device 20 outputs device identification information DID and substrate identification information SID to analyzing device 300 via network NW. Inspection device 220 outputs measurement information MI and substrate identification information SID to analyzing device 300 via network NW. Exposure device 230 outputs calculated correction conditions AR to exposure device 230 via network NW. Path information acquisition unit 310 acquires path identification information RID and substrate identification information SID based on the device identification information DID and substrate identification information SID output by processing device 20 or processing device 100 within processing device 20 via network NW.

[0080] In analysis system 10A, factories M1, M2, and M3 are connected to network NW. Analysis apparatus 300 calculates correction conditions AR based on information acquired from each factory M and outputs the correction conditions AR to the exposure apparatus 230 installed in each factory M. For example, if the processing apparatus 20 or the apparatus 100 within processing apparatus 20 installed in factory M1 is similar to the processing apparatus 20 or the apparatus 100 within processing apparatus 20 installed in factory M2, analysis system 10A may use the error amount of the processing apparatus 20 or the apparatus 100 calculated based on information acquired from factory M1 as the error amount of the processing apparatus 20 or the apparatus 100 installed in factory M2, which is different from factory M1. Furthermore, analysis system 10A can estimate the error amount of the processing apparatus 20 or the apparatus 100 in a new factory M by acquiring information from many factories M.

[0081] [Summary of Effects of Second Embodiment] As described above, according to this embodiment, analysis system 10A is connected to multiple factories M via network NW. That is, analysis system 10A can acquire the error amounts of apparatuses 100 installed in the multiple factories M. Therefore, analysis system 10A can calculate correction values ​​based on the error amounts of many apparatuses 100 sent from the multiple factories M. Therefore, analysis system 10A can correct the exposure pattern based on more accurate and reliable error amounts.

[0082] Furthermore, according to this embodiment, correction values ​​can be calculated based on the error amounts of many devices 100 (device 110, device 111, device 121, device 122) sent from multiple factories M, and therefore the error amount of each device 100 can be expanded by coefficients in an orthonormal function system and quantitatively expressed, making it easy to compare the error amounts of a large number of devices 100. Therefore, according to this embodiment, the tendency of the error amount can be quantitatively shown, making it possible to determine the error elements of the device 100, compare between devices 100, or detect abnormal devices 100.

[0083] Furthermore, according to this embodiment, the analysis device 300 calculates the amount of error and the correction value for each device 100, but it may also calculate the amount of error and the correction value for each processing device 20 and perform the same analysis as described above.

[0084] Third Embodiment FIG. 18 is a diagram showing an example of an analysis system according to a third embodiment. An example of an analysis system 10B will be described with reference to the same figure. In the description of the analysis system 10B, the same components as those in the analysis system 10 are denoted by the same reference numerals, and description thereof may be omitted. The analysis system 10B differs from the analysis system 10 in that the first layer L1 includes processes P41, P42, P43, P44, and P45, and the second layer L2 includes processes P46, P47, and P48. For example, process P41 is a sputtering process, process P42 is an exposure process, process P43 is an etching process, process P44 is an inspection process, process P45 is an annealing process, process P46 is a CVD process, process P47 is an exposure process, and process P48 is an inspection process.

[0085] 18, three different substrates are processed by different routes. Specifically, the three different substrates are processed by route R41, route R42, and route R43, respectively.

[0086] Process P41 includes apparatus 411 and apparatus 412, and after each of apparatus 411 and apparatus 412 processes a substrate, it outputs substrate identification information SID of the substrate and its own apparatus identification information DID to analysis apparatus 300B.

[0087] Process P42 includes exposure apparatus 421 and exposure apparatus 422, and exposure apparatus 421 and exposure apparatus 422 each acquire correction conditions AR from analytical apparatus 300B and perform exposure based on the acquired correction conditions AR. When calculating the effect of each exposure apparatus on the substrate as an error amount, apparatus identification information DID of the exposure apparatus and substrate identification information SID of the processed substrate may be output to analytical apparatus 300A, and analytical apparatus 300B may calculate the amount of error caused in the substrate by each exposure apparatus based on substrate identification information SID acquired from the exposure apparatus and subsequent measurement results.

[0088] Process P43 comprises apparatus 431, apparatus 432, and apparatus 433, and after each of apparatus 431, apparatus 432, and apparatus 433 processes a substrate, it outputs the substrate identification information SID of the substrate and its own apparatus identification information DID to analysis apparatus 300B.

[0089] Process P44 includes an inspection device 440. The inspection device 440 performs predetermined measurements on the substrate, and outputs substrate identification information SID and measurement information MI of the measured substrate to the analysis device 300B.

[0090] Process P45 comprises apparatus 451, apparatus 452, and apparatus 453, and apparatus 451, apparatus 452, and apparatus 453 each process a substrate and then output the substrate identification information SID of the substrate and their own apparatus identification information DID to analysis apparatus 300B.

[0091] Process P46 includes apparatus 461 and apparatus 462, and after each of apparatus 461 and apparatus 462 processes a substrate, it outputs substrate identification information SID of the substrate and its own apparatus identification information DID to analysis apparatus 300B.

[0092] Process P47 includes exposure apparatus 470. Exposure apparatus 470 acquires correction conditions AR from analytical apparatus 300B, and performs exposure based on the acquired correction conditions AR.

[0093] Process P48 includes inspection device 480. Inspection device 480 performs predetermined measurements on the substrate, and outputs substrate identification information SID and measurement information MI of the measured substrate to analysis device 300B.

[0094] 19 is a diagram showing an example of the correspondence between paths and measurement results according to the third embodiment. An example of the correspondence between paths and measurement results according to the third embodiment will be described with reference to the same figure. In the example shown in the same figure, information on the apparatus 100 through which each path passed is associated with measurement information MI including measurement results MR of the substrate that passed through each path. Specifically, a path with path identification information RID of "R41" is processed by apparatus 411 as process P41, by apparatus 421 as process P42, by apparatus 431 as process P43, by apparatus 452 as process P45, by apparatus 461 as process P46, and by apparatus 470 as process P47. A path with path identification information RID of "R42" is processed by apparatus 412 as process P41, by apparatus 421 as process P42, and by apparatus 470 as process P43. The path having path identification information RID of "R43" is processed by device 412 as step P41, by device 422 as step P42, by device 432 as step P43, by device 451 as step P45, by device 461 as step P46, and by device 470 as step P47. The inspection device 480 in step P48 inspects the substrate that has passed through steps P41 to P47. "Meas.41" is associated as the measurement result for the substrate that has passed through path R41, "Meas.42" is associated as the measurement result for the substrate that has passed through path R42, and "Meas.43" is associated as the measurement result MR for the substrate that has passed through path R43.

[0095] 19, the substrate is measured in process P44 for the first layer L1, and although the measurement result MR for process P44 may be associated with routes R1 and R2 in the table of FIG. 19, this association is not necessary. This is because the substrate is measured again in process P48 for the second layer L2, and it is sufficient if at least the latest measurement result MR for the substrate is linked to routes R1 and R2. This is also true not only for the second layer, but also for the third layer and beyond. This is because the amount of error for each path (R41, R42) can be determined from the latest measurement result MR for the substrate, thereby confirming whether the substrate is being exposed with high precision.

[0096] [Summary of Effects of the Third Embodiment] As described above, according to this embodiment, the analysis system 10B has multiple processing steps, each of which includes one or more devices. Therefore, there are a number of paths equal to the number of devices included in each processing step. Here, if the error amount for each path is calculated using a conventional method, the number of error amounts to be stored will be large, and the number of measurement results required to calculate the error amount per path will be small. Furthermore, if the error amount for each path is calculated using a conventional method, there may be paths for which the error amount is not measured. However, according to this embodiment, the error amount is calculated for each device, so the error amount to be stored is small, and the number of measurement results required to calculate the error amount per device is large. Therefore, according to this embodiment, it is possible to calculate error amounts more accurately and with higher reliability. Furthermore, according to this embodiment, since the error amount is calculated for each device, it is possible to calculate error amounts for all paths.

[0097] Furthermore, according to this embodiment, it is possible to detect devices with large error amounts and devices with small error amounts among multiple devices. Therefore, according to this embodiment, it is possible to detect a route with a small error amount among multiple routes. The analysis system 10B can also recommend a route by, for example, including a route recommendation unit (not shown). The route recommendation unit recommends a route that passes through in accordance with the grade of the product. For example, in the case of a product or layer that requires strict accuracy, the route recommendation unit recommends a route with a small error (Golden Route).

[0098] Furthermore, according to this embodiment, the analysis system 10B calculates the error amount for each device, allowing comparison of the error amounts of each device among multiple devices. By comparing the error amounts of each device, the analysis system 10B can detect devices with large and small error amounts. Therefore, according to this embodiment, the analysis system 10B can detect devices with abnormally large error amounts as devices with abnormalities. Furthermore, according to this embodiment, even within the same device, it is possible to detect locations on the substrate where there are large differences in error amounts. The analysis device 300 can infer that locations on the substrate with large error amounts indicate some kind of abnormality and prompt maintenance work. Furthermore, it may find abnormal locations on the substrate from vector diagrams or orthonormal function coefficients included in the measurement information MI, and prompt the user to investigate whether there are any abnormalities in the mask writing error at the abnormal location, or to investigate whether there is any correlation with the heater position in the heating process.

[0099] Furthermore, according to this embodiment, the path that the substrate will likely pass through in the future can be determined in advance from among multiple paths, and the exposure shape can be corrected to offset the plate deformation that occurs in the processing equipment 100 of that path. By correcting to offset the plate deformation that occurs after exposure, the "final TP (total pitch) accuracy" can be improved. TP refers to the absolute value of the error obtained from the pattern design value and measurement information MI. Furthermore, by correcting to match the plate deformation that occurs in the CF (color filter) process, the CF bonding accuracy can be improved. Furthermore, deposition accuracy can be improved by adjusting the exposure shape to match the mask deformation and alignment accuracy in the organic vapor deposition process in the OLED manufacturing process.

[0100] Furthermore, according to this embodiment, since there are multiple processing steps, the process for which correction is to be prioritized and the process for which correction is not to be prioritized may be distinguished and weighted to the correction value. Specifically, the correction condition calculation unit 351 may be configured to weight any one of the first characteristic, the second characteristic, the third characteristic, and the fourth characteristic to calculate the correction condition. By weighting the correction value, it is possible to reduce the weight and suppress the amount of correction for a process that has large variations and is unstable. Furthermore, when the calculated correction value is large, it is possible to check the effect by gradually adding the correction value to prepare for the risk that the correction value may be incorrect.

[0101] Furthermore, according to this embodiment, the analysis device 300B calculates the amount of error and the correction value for each device 100, but it may also calculate the amount of error and the correction value for each processing device 20 and perform the same analysis as described above.

[0102] Fourth Embodiment In the above-described embodiment, a method for calculating an error amount for each device 100 from measurement results for each path has been described. By calculating the error amount for each device 100, the error amounts for each device 100 can be added together to calculate the error amount for each path. However, if there is a correlation between processes, simply adding up the error amounts for each device 100 may not be sufficient. The fourth embodiment differs from the above-described embodiments in that, while assuming that the error amount is calculated for each device 100, the correlation between processes is taken into consideration to calculate the error amount for each path.

[0103] [Method 1: Combining Multiple Processes and Treating Them as a Single Process] In this embodiment, the analysis system 10C (not shown) uses x in the above-described formula (1) as a condition vector for path i, i.e., path information, representing a combination of multiple devices 100. That is, in this embodiment, by treating multiple processes as a single process, it is possible to calculate an error amount that takes into account the correlation between the processes.

[0104] In this embodiment, by treating multiple processes as a single process, formulation is possible without destroying the format of the least squares method. However, since it is necessary to cover all conditions of the combinations, the amount of information to be stored becomes enormous as the number of processes and the number of devices used in each process increases. Furthermore, as the amount of information to be stored becomes enormous, the number of data per condition tends to decrease, which may cause the estimation results to become unstable.

[0105] [Method 2: Introducing an Interaction Term] In this embodiment, the analysis system 10C takes into account the interaction between processes by introducing an interaction term in addition to the above-mentioned equation (3). Specifically, this is expressed by the following equation (6).

[0106]

[0107] where γ is the difference quantity related to the interaction, and x ｉｎｔ indicates a path. That is, according to this embodiment, by adding the error amount related to the interaction in addition to the error amount for each device 100, it is possible to calculate a more accurate error amount.

[0108] Here, the coefficient γ of the interaction term may be learned by machine learning using a neural network or the like. The coefficient γ of the interaction term may also be learned by imposing a constraint that it takes a small value, such as L1 regularization. Specifically, the contribution of the interaction term can be reduced by reducing the value of λ expressed by the following equation (7).

[0109]

[0110] According to this embodiment, formulation is possible without changing the format of the least squares method. Furthermore, according to this embodiment, since the configuration is such that the interaction terms are simply added together, even if the interaction terms for all paths have not been calculated (the interaction for that path simply becomes 0), the amount of error for that path can be calculated.

[0111] Furthermore, according to this embodiment, the analysis system 10C calculates the amount of error and the correction value for each device 100, but it may also calculate the amount of error and the correction value for each processing device 20 and perform the same analysis as described above.

[0112] Fifth Embodiment Fig. 20 is a diagram showing an example of the functional configuration of an analysis system according to a fifth embodiment. An example of an analysis system 10D will be described with reference to the same figure. In the description of the analysis system 10D, components similar to those in the analysis system 10 are given the same reference numerals, and description thereof may be omitted. The analysis system 10D differs from the analysis system 10 in that it includes a defective device detection unit 370 and a warning unit 380.

[0113] The defective device detection unit 370 detects defective devices 100 based on the error amount for each device 100 calculated by the calculation unit 330. For example, the defective device detection unit 370 measures changes in the error amount of the same processing device 100, and if there is an abnormality in the change over time, determines that there is an abnormality in that processing device 100. In order to detect changes over time, the defective device detection unit 370 may store a correspondence relationship between the error amount stored in the storage unit 340 and the time when the substrate inspection was performed (or the time when the data was acquired).

[0114] Furthermore, the defective device detection unit 370 may correct the correction value PM that corrects the amount of error based on changes over time. For example, if there is a pattern to the changes in the amount of error over time, the defective device detection unit 370 may correct the correction value PM based on the current date and time or the time elapsed since the data was calculated (or acquired).

[0115] Furthermore, the defective device detection section 370 may compare the error amounts of the same processing device 100 or similar devices 100 among a plurality of devices 100 from which data can be collected, to detect a processing device 100 having an abnormality.

[0116] Furthermore, by storing the malfunctions that have occurred, the defective device detection unit 370 may determine whether there is a trend in the causes of malfunctions among the multiple processing devices 100, and if there is a trend in the causes of malfunctions, may identify the causes of the malfunctions. By identifying the causes of malfunctions, it is possible to prevent malfunctions that are predicted to occur in the future.

[0117] When defective device detection unit 370 detects a device having a defect, warning unit 380 issues a warning to the factory in which the device is located. For example, warning unit 380 notifies the factory in which the device is located via a predetermined communication network. If analysis device 300D is located in the same factory as processing device 100, inspection device 220, or exposure device 230, warning unit 380 may warn of the occurrence of a defect by lighting or flashing an LED light or sounding a buzzer. Note that warning unit 380 may also notify of the possibility of a defect in addition to the warning. For example, warning unit 380 may notify of the location of the defect, the possible type of defect, etc., depending on the status of changes in the amount of error over time.

[0118] Furthermore, according to this embodiment, the analysis system 10D uses the amount of error for each device 100, but the amount of error for each processing device 20 may be used to perform the same analysis as described above.

[0119] Sixth Embodiment In the above-described embodiment, an example of calculating the amount of error for each apparatus 100 based on the path taken by the substrate has been described. The sixth embodiment differs from the above-described embodiments in that the processing conditions for each processing apparatus 100 are treated as path information. That is, in this embodiment, even if substrates pass through the same apparatus 100, if the processing conditions are different, they are treated as different paths.

[0120] The path information acquisition unit 310 treats different processing conditions as different paths even if the path passes through the same processing device 100. This allows the calculation unit 330 to find the device 100 that has a large impact on the error amount when the error amount is calculated. The processing condition may be, for example, a processing temperature. When the processing condition is processing temperature, each processing temperature within a predetermined range is treated as a different path. This configuration makes it possible to detect processing temperatures that have a large impact on the error amount. Furthermore, by providing a suggestion unit (not shown), it is possible to estimate and propose processing conditions that will reduce the error amount based on the obtained relationship between the processing temperature and the error amount.

[0121] Furthermore, according to this embodiment, the analysis device 330 calculates the amount of error for each device 100 and proposes processing conditions, but it may also calculate the amount of error for each processing device 20 and propose processing conditions, and perform the same analysis as described above.

[0122] Seventh Embodiment In the above-described embodiment, an example has been described in which the calculation unit 330 calculates the amount of error based on the measurement information MI acquired by the measurement information acquisition unit 320. In the seventh embodiment, a method for calculating the amount of error in a case in which the measurement information acquisition unit 320 is unable to acquire the measurement information MI for some reason will be described.

[0123] As an example, the calculation unit 330 acquires the alignment measurement result and the corresponding substrate identification information SID performed by the exposure tool 230. The calculation unit 330 calculates the amount of error based on the path traveled by the substrate identified by the substrate identification information SID and the alignment measurement result of the exposure tool.

[0124] As another example, the measurement information acquisition unit 320 acquires information resulting from the inspection of the substrate in addition to or instead of the measurement information MI. The information resulting from the inspection of the substrate may be information indicating the degree of pass / fail of the substrate. The information indicating the degree of pass / fail of the substrate may be, for example, information on whether the substrate is defective or not, or information on the product grade, etc. In this case, the calculation unit 330 calculates the defect rate instead of the error amount based on the information resulting from the inspection of the substrate.

[0125] Furthermore, according to this embodiment, the calculation unit 330 calculates the amount of error and the defective rate for each device 100, but it may also calculate the amount of error and the defective rate for each processing device 20 and perform the same analysis as described above.

[0126] Eighth Embodiment FIG. 21 is a diagram showing the time change of the results of statistical calculation of measurement results according to an eighth embodiment. In this figure, the horizontal axis shows the time change of the results of statistical calculation of measurement results of one substrate. As an example of the statistical calculation result, the vertical axis shows the standard deviation. In this embodiment, the analysis device 300 displays the time change of the standard deviation of the measurement results, thereby visually displaying the time change of the accuracy of one substrate and the accuracy variation. Note that, in the following description, an example of the result of statistical calculation of measurement results will be described. However, any index capable of capturing the time change of accuracy and the accuracy variation may be used, and a predetermined feature amount or the like may be used instead of the statistical calculation result.

[0127] FIG. 21(A) is a diagram illustrating an example of a gradual transition in the state of a substrate. The measurement results from March 18 to March 19 are stable at approximately 0.6 μm, but the accuracy gradually deteriorates from March 19 to March 23. FIG. 21(B) is a diagram illustrating an example of a sudden change in accuracy. The measurement results from March 18 to March 20 are stable at approximately 0.9 μm, but the accuracy suddenly improves to approximately 0.2 μm between March 20 and March 21. However, the accuracy then deteriorates again to approximately 0.9 μm by March 23. As shown in FIG. 21 , by observing the time changes in the results of statistical calculations of the measurement results of a single substrate, it is possible to capture the time changes in the accuracy of a single substrate and the variation in accuracy.

[0128] FIG. 22 is a diagram showing an example of the change over time in measurement results for each condition according to the eighth embodiment. With reference to the figure, an example of the change over time in measurement results for each condition will be described. In the figure, the horizontal axis shows the change over time in the results of statistical calculation of the measurement results of one substrate. As an example of the statistical calculation results, the vertical axis shows the standard deviation. In the figure, the measurement results under "condition 1" are shown with a solid line, and the measurement results under "condition 2" are shown with a dashed line. The conditions can be set arbitrarily. By referring to the figure, it is possible to grasp the differences in accuracy and variation for each condition.

[0129] FIG. 22A is a diagram illustrating an example of a case where accuracy differs depending on the conditions. The measurement results under "Condition 1" are stable at approximately 0.4 μm to 0.6 μm, while the measurement results under "Condition 2" are stable at approximately 0.6 μm to 0.9 μm. This shows that "Condition 1" has better accuracy than "Condition 2." FIG. 22B is a diagram illustrating an example of a case where accuracy variation differs depending on the conditions. The measurement results under "Condition 1" are stable at approximately 0.6 μm to 0.9 μm, while the measurement results under "Condition 2" vary from approximately 0.4 μm to 1.2 μm. This shows that "Condition 1" has better accuracy variation than "Condition 2."

[0130] 23 is a diagram showing an example of the functional configuration of an analysis device according to the eighth embodiment. An analysis device 300E will be described with reference to the same figure. The analysis device 300E differs from the analysis device 300 in that it includes a classification information acquisition unit 390, a classification unit 391, and a display unit 392. In the description of the analysis device 300E, components similar to those of the analysis device 300 are denoted by similar reference numerals, and a description thereof may be omitted.

[0131] The classification information acquisition unit 390 acquires classification items (grouping information) selected by user operation. Classification items are information including information on groups, which are units for grouping and visualizing measurement results. A group may be, for example, a processing device 20, a device 100, a recipe, processing conditions (processing time, processing temperature, etc.), a mask, a tray, or product information. Furthermore, a device 100 refers to one or more processing units or processing locations incorporated within the device 20 that actually perform a predetermined process on a substrate. Specifically, product information may be a product, layer number, lot number, etc.

[0132] The classification unit 391 classifies the measurement results based on the classification items acquired by the classification information acquisition unit 390 and the measurement results included in the measurement information MI stored in the memory unit 340, and the display unit 392 displays the results classified by the classification unit. The display unit 392 displays various information according to the classification items classified by the classification unit 391. The display unit 392 may be, for example, a liquid crystal display or an organic electroluminescence (EL) display, or the same device as the characteristic display unit 331. The display unit 392 may be provided in the analysis apparatus 300E, installed in a factory or the like where the exposure apparatus is installed, or provided in a general-purpose terminal such as a personal computer or tablet terminal. The display unit 392 may be connected to the analysis apparatus 300E via a predetermined communication network.

[0133] Fig. 24 is a diagram for explaining the effect of classifying measurement results by tray according to the eighth embodiment. The graph shown in Fig. 24 is an example of information displayed on the display unit 392. Fig. 24(A) is a diagram showing an example of measurement results before classification, and Fig. 24(B) is a diagram showing an example of measurement results after classification. In this diagram, the horizontal axis shows time as the result of statistical calculation of the measurement results of one exposure apparatus. As an example of the statistical calculation result, the vertical axis shows standard deviation.

[0134] The example shown in FIG. 24A is an example of measurement results before classification, and therefore shows all results measured by a single exposure tool. From the graph of the measurement results before classification, it can be seen that the measurement results vary from approximately 0.4 μm to 0.9 μm. Here, a single exposure tool may be equipped with multiple trays (e.g., two trays). The graph of the measurement results before classification cannot capture the variation between trays. The example shown in FIG. 24B is an example of measurement results after classification. Specifically, the figure displays the measurement results divided into "Tray A" and "Tray B" equipped in a single exposure tool. When the classification information acquisition unit 390 acquires information for classification by "Tray" as a classification item, the classification unit 391 classifies the measurement results by "Tray" and displays the classification results on the display unit 392. In the figure, the measurement results for "Tray A" are shown by a broken line, and the measurement results for "Tray B" are shown by a solid line.

[0135] The example shown in FIG. 24B shows the same measurement results as those shown in FIG. 24A, but because they are categorized (grouped), the trends in the measurement results for each tray can be seen. Specifically, the measurement results for "Tray A" vary from approximately 0.4 μm to 0.6 μm, and the measurement results for "Tray B" vary from approximately 0.6 μm to 0.9 μm. In other words, the analysis device 300E can display the variation for each tray. Furthermore, by acquiring path information as a classification item, the analysis device 300E can display the accuracy and accuracy variation for each path. Furthermore, the analysis device 300E can display the accuracy and accuracy variation for each path, allowing the user to identify paths where problems are occurring or select paths with high accuracy for exposure of patterns requiring high accuracy.

[0136] 25 is a diagram showing an example of the functional configuration of an analysis device according to a ninth embodiment. Analysis device 300F will be described with reference to the same figure. Analysis device 300F differs from analysis device 300E in that analysis device 300F includes a simulation calculation unit 396. In the description of analysis device 300F, components similar to those of analysis device 300E are denoted by similar reference numerals, and description thereof may be omitted.

[0137] The simulation calculation unit 396 performs a correction simulation (hereinafter simply referred to as a simulation). The simulation calculation unit 396 performs a simulation based on the classification items acquired by the classification information acquisition unit 390 and measurement result selection information, which is information on which measurement results are selected. The measurement result selection information may be acquired by the classification information acquisition unit 390. The simulation calculation unit 396 calculates an average value for each measurement point based on the selected measurement results. The simulation calculation unit 396 calculates a simulation value after correction by subtracting the calculated average value from each measurement point. The display control unit 391 displays the simulation results on the display unit 392. Note that the simulation calculation unit 396 may perform a simulation by correcting pattern information used when exposing using an exposure apparatus, instead of or in addition to subtracting from each measurement point.

[0138] 26 is a diagram for explaining a correction simulation according to the eighth embodiment. The correction simulation will be explained with reference to the same figure. In the same figure, the time change of the measurement result when "Condition A" is selected as a classification item is shown by a solid line for "Condition A" and a dashed line for "Condition B", with the horizontal axis representing time. The vertical axis represents standard deviation.

[0139] FIG. 26A shows the measurement results before the simulation. The white circles in the figure indicate the measurement results selected for the simulation. The measurement results for "Condition A" range from approximately 0.8 μm to 1.0 μm, while the measurement results for "Condition B" range from approximately 0.6 μm to 0.7 μm. For example, for Condition A, the substrate processed using the apparatus 110 in process P1 is Condition A, and the substrate processed using the apparatus 111 in process P1 is Condition B. The conditions may be set arbitrarily. The white circles in the figure indicate points selected by the simulation calculation unit 396, while the black circles indicate points not selected by the simulation calculation unit 396. In the example shown in the figure, four out of eight measurement results are selected for "Condition A," and three out of ten measurement results are selected for "Condition B." The simulation calculation unit 396 calculates the average score of the selected points. By selecting a portion of all measurement results, even if there are measurement results whose error amounts are significantly different from the other measurement results, it is possible to calculate correction values ​​using only the measurement results that you want to consider.

[0140] FIG. 27 is a diagram illustrating the average difference according to the eighth embodiment. Referring to FIG. 27, the simulation calculation unit 396 will explain how it calculates the average (average difference) of selected points. FIGS. 27A and 27B show measurement results before simulation. FIG. 27A shows the measurement results for "Condition A," and FIG. 27B shows the measurement results for "Condition B." The arrows in FIGS. 27A and 27B indicate the measurement results (solid lines) and average values ​​(dashed lines) for the processing time of each substrate. Because four measurement results were selected for "Condition A," the four measurement results are superimposed. Because three measurement results were selected for "Condition B," the three measurement results are superimposed. The simulation calculation unit 396 calculates the average of the measurement results for each point. For example, the average of the points shown in the lower right corner of FIG. 27B is indicated by a dashed arrow.

[0141] Next, the simulation calculation unit 396 performs a simulation by subtracting the calculated average from each point. Figures 27(C) and 27(D) show the measurement results after the simulation. Figure 27(C) shows the simulation results for "Condition A," and Figure 27(D) shows the simulation results for "Condition B." It can be seen from the simulation results that the distortion observed before the simulation is no longer observed in the simulation results.

[0142] Returning to FIG. 26, FIG. 26(B) shows the simulation results. The simulation calculation unit 396 applies the average value calculated using the selected points to all points. Specifically, the simulation calculation unit 396 subtracts the average calculated under "condition A" from the measurement results under "condition A," and subtracts the average calculated under "condition B" from the measurement results under "condition B." The simulation results show that applying the correction improved the accuracy from 0.2 (μm) to approximately 0.4 (μm) for both "condition A" and "condition B."

[0143] According to the embodiment described above, analysis apparatus 300F includes simulation calculation unit 396, and thereby simulates correction by calculating the average value of selected measurement results and subtracting the calculated average value from all measurement data. Analysis apparatus 300F displays the simulation results on display unit 392. Therefore, according to this embodiment, a simulation can be performed within the visualization tool. Furthermore, according to this embodiment, since the results of applying correction values ​​can be simulated, the results of applying the correction values ​​can be known before actually applying the correction values ​​to the exposure apparatus.

[0144] Furthermore, according to the embodiment described above, by classifying the selected channels and displaying the measurement results for each group on the display unit 392, it is possible to visualize the variation for each group. Because the analysis device 300F performs a simulation for each classified group, it is possible to simulate the results when correction is made according to the variation for each group. In other words, according to this embodiment, it is possible to easily visualize the effect of updating the correction value for each group.

[0145] Furthermore, according to the embodiment described above, the correction value calculated by the simulation calculation unit 396 is applied to all measurement results. That is, according to this embodiment, it is possible to visualize the change over time in the results of applying the correction value. Here, since the number of measurement results selected for correction is finite, the correction value may not function correctly for all measurement results. That is, the correction may have an adverse effect on the measurement results. According to this embodiment, since the correction value is applied to all measurement results, it is possible to determine the effect of the correction value on the measurement results over time.

[0146] Note that all measurement results to which the simulation calculation unit 396 applies the correction value may include measurement results other than those selected for calculating the correction value, and is not intended to be applied to all measurement results that are actually measured. For example, the simulation calculation unit 396 selects multiple points for calculating the correction value, calculates the correction value based on the selected measurement results, and performs a simulation by applying the correction value to the selected measurement results and non-selected measurement results.

[0147] Note that all or part of the functions of each unit of the analysis system 10 in the above-described embodiment may be realized by recording a program for realizing these functions on a computer-readable recording medium, and reading and executing the program recorded on the recording medium into a computer system. Note that the term "computer system" here includes hardware such as an OS and peripheral devices.

[0148] Furthermore, "computer-readable recording media" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as storage units such as hard disks built into computer systems. Furthermore, "computer-readable recording media" may also include devices that dynamically store programs for a short period of time, such as communication lines used when transmitting programs over networks like the Internet or communication lines like telephone lines, or devices that store programs for a fixed period of time, such as volatile memory within computer systems that serve as servers or clients in such cases. Furthermore, the above-mentioned programs may be programs that realize some of the aforementioned functions, or may be programs that can realize the aforementioned functions in combination with programs already stored in the computer system.

[0149] One embodiment of the present invention has been described in detail above with reference to the drawings, but the specific configuration is not limited to that described above, and various design changes and the like are possible within the scope that does not deviate from the gist of the present invention.

[0150] The display manufacturing method of the present invention is suitable for producing displays, and the device manufacturing method of the present invention is suitable for producing microdevices.

[0151] REFERENCE SIGNS LIST 10...analysis system, 100...processing device, 110, 111, 121, 122...device, 220...inspection device, 230...exposure device, 300...analysis device, 310...path information acquisition unit, 320...measurement information acquisition unit, 330...calculation unit, 331...characteristic display unit, 340...storage unit, 350...selection unit, 351...correction condition calculation unit, 360...output unit, P1, P2...process, R1, R2...path, MR...measurement result, DID...device identification information, SID...substrate identification information, RID...path identification information, MI...measurement information, PM...correction value, CI...correspondence information, AR...correction condition, PC...plate coordinate system, OF...orthogonal function system, HM...heat map

Claims

1. A first processing apparatus having a first device and a second device and performing a first process on a substrate, and a second processing apparatus having a third device and a fourth device and performing a second process on the substrate, the substrate is subjected to the first process and the second process by passing through any one of a first path passing through the first device and the third device, a second path passing through the first device and the fourth device, a third path passing through the second device and the third device, or a fourth path passing through the second device and the fourth device; an analysis system that analyzes measurement information, which is information from an inspection device that measures processing results of each of the substrates, a first substrate that has passed through the first path, a second substrate that has passed through the second path, a third substrate that has passed through the third path, and a fourth substrate that has passed through the fourth path, a path information acquisition unit that acquires board identification information that identifies each of the boards and path identification information of each of the paths; a measurement information acquiring unit that acquires the substrate identification information and the measurement information measured by the inspection device; a calculation unit that calculates, based on the path identification information, the substrate identification information, and the measurement information, a first device characteristic generated on the first substrate subjected to the first processing by the first device, a second device characteristic generated on the second substrate subjected to the first processing by the second device, a third device characteristic generated on the third substrate subjected to the second processing by the third device, and a fourth device characteristic generated on the fourth substrate subjected to the second processing by the fourth device; An analysis system comprising:

2. An analysis system for analyzing measurement information, which is information from an inspection device that measures a processing result of a substrate that has been subjected to a first processing and a second processing, using a first device or a second device that is a first processing device that performs a first processing on the substrate, and a third device or a fourth device that is a second processing device that performs a second processing on the substrate after the first processing, comprising: a calculation unit that calculates a first apparatus characteristic generated on the substrate subjected to the first process by the first apparatus, a second apparatus characteristic generated on the substrate subjected to the first process by the second apparatus, a third apparatus characteristic generated on the substrate subjected to the second process by the third apparatus, and a fourth apparatus characteristic generated on the substrate subjected to the second process by the fourth apparatus, based on substrate identification information that identifies the substrate, path identification information that is information on apparatuses used in the first process and the second process on the substrate, and the measurement information. An analysis system comprising:

3. The path identification information is information indicating paths of devices through which the substrate is processed in the first process and the second process, the paths including a first path processed by the first device and the third device, a second path processed by the first device and the fourth device, a third path processed by the second device and the third device, and a fourth path processed by the second device and the fourth device, the board identification information is information for distinguishing, among the boards, a first board passing through the first path, a second board passing through the second path, a third board passing through the third path, and a fourth board passing through the fourth path; a path information acquisition unit that acquires the board identification information and the path identification information; a measurement information acquisition unit that acquires the board identification information and the measurement information, The analysis system according to claim 2 .

4. the calculation unit calculates a first correction value for correcting the first device characteristic, a second correction value for correcting the second device characteristic, a third correction value for correcting the third device characteristic, and a fourth correction value for correcting the fourth device characteristic; a storage unit configured to store the first correction value, the second correction value, the third correction value, and the fourth correction value. The analysis system according to claim 1 or 2.

5. a selection unit that selects correction values ​​of two devices through which the substrate has passed from the first correction value, the second correction value, the third correction value, and the fourth correction value stored in the storage unit, based on the substrate identification information and the path identification information of a fifth substrate, which is different from the first to fourth substrates among the substrates; a correction condition calculation unit that calculates a correction condition when exposing the fifth substrate based on the correction values ​​of the two devices selected by the selection unit The analysis system according to claim 4 dependent on claim 1.

6. the correction condition calculation unit calculates at least one of a first correction condition that is the correction condition based on the first correction value and the third correction value, a second correction condition that is the correction condition based on the first correction value and the fourth correction value, a third correction condition that is the correction condition based on the second correction value and the third correction value, and a fourth correction condition that is the correction condition based on the second correction value and the fourth correction value; Calculating the correction condition to be applied when exposing the fifth substrate based on the substrate identification information and the path identification information of the fifth substrate acquired by the path information acquisition unit and the calculated correction condition. The analysis system according to claim 5 .

7. The measurement information includes an amount of error, which is information regarding a difference between a design value of the first to fourth substrates and a measurement value of the first to fourth substrates. The analysis system according to claim 1 or 3.

8. The calculation unit calculates, based on the route identification information and the error amount, an amount of error caused by processing of the first device among the error amounts as the first device characteristic, an amount of error caused by processing of the second device as the second device characteristic, an amount of error caused by processing of the third device as the third device characteristic, and an amount of error caused by processing of the fourth device as the fourth device characteristic. The analysis system according to claim 7.

9. a characteristic display unit that displays at least one of the first device characteristic, the second device characteristic, the third device characteristic, and the fourth device characteristic calculated by the calculation unit; The analysis system according to claim 1 or 2.

10. the characteristic display unit displays the first device characteristic, the second device characteristic, the third device characteristic, or the fourth device characteristic in a heat map. The analysis system according to claim 9 .

11. the correction condition calculation unit calculates the correction condition by adding up apparatus characteristics of two of the first apparatus, the second apparatus, the third apparatus, and the fourth apparatus through which the substrate has passed. The analysis system according to claim 5 .

12. the first processing device includes a fifth device in addition to the first device and the second device, the path information acquisition unit acquires board identification information of a sixth board, which is different from the first to fifth boards, among the boards that have passed through a fifth path that passes through the fifth device and the third device, and the path identification information of the fifth path; The calculation unit calculates a fifth device characteristic occurring on the sixth substrate that has been subjected to the first process by the fifth device; The storage unit stores the fifth device characteristic; The correction condition calculation unit calculates a fifth correction condition, which is the correction condition for a sixth route passing through the fifth device and the fourth device, based on the fifth device characteristic and the fourth device characteristic. The analysis system according to claim 5 .

13. a path recommendation unit that recommends, as a path for the fifth substrate to pass, one of the first device and the second device having a small amount of error, which is information about a difference between a design value and a measured value, based on the first device characteristic and the second device characteristic calculated by the calculation unit. The analysis system according to claim 5 .

14. the route recommendation unit recommends a route having a smaller amount of error, which is information regarding a difference between a design value and a measured value, from among the first route, the second route, the third route, and the fourth route, based on the first device characteristic, the second device characteristic, the third device characteristic, and the fourth device characteristic calculated by the calculation unit; The analysis system according to claim 13.

15. the route information acquisition unit acquires the route identification information for identifying the first route when at least one of the processing conditions for the first substrate in the first apparatus and the processing conditions for the first substrate in the third apparatus is different from the processing conditions for the first substrate in the first apparatus, as a seventh route which is a different route. The analysis system according to claim 1 .

16. the correction condition calculation unit weights at least one of the first device characteristic, the second device characteristic, the third device characteristic, or the fourth device characteristic to calculate the correction condition. The analysis system according to claim 5 .

17. the correction conditions include drive conditions of a drive unit of a substrate stage that holds the substrate; The analysis system according to claim 5 .

18. The correction condition calculation unit calculates the correction condition for each projection optical system when the fifth substrate is exposed from a plurality of projection optical systems. The analysis system according to claim 5 .

19. a defective device detection unit that detects whether or not a defect occurs in the first processing device or the second processing device based on the first device characteristic, the second device characteristic, the third device characteristic, and the fourth device characteristic. The analysis system according to claim 1 or 2.

20. the defective device detection unit detects whether or not a defect has occurred in the first device, the second device, the third device, and the fourth device based on the first device characteristic, the second device characteristic, the third device characteristic, and the fourth device characteristic. The analysis system according to claim 19.

21. the defective device detection unit further includes a warning unit that issues a warning when the defective device detection unit detects the defect; The analysis system according to claim 19.

22. an output unit that outputs the correction condition calculated by the correction condition calculation unit to an exposure apparatus, The analysis system according to claim 5 .

23. An exposure apparatus that exposes the fifth substrate using the correction condition obtained using the analysis system according to claim 5.

24. the exposure apparatus acquires route information through which the fifth substrate passes before being exposed by the exposure apparatus, and exposes the fifth substrate based on the correction condition related to the route identification information; The exposure apparatus according to claim 23.

25. exposing the fifth substrate using the exposure apparatus according to claim 23 or 24; developing the exposed fifth substrate; Device manufacturing method.

26. exposing the fifth substrate using the exposure apparatus according to claim 23 or 24; and developing the exposed fifth substrate.

27. A first processing apparatus having a first device and a second device and performing a first process on a substrate, and a second processing apparatus having a third device and a fourth device and performing a second process on the substrate, the substrate is subjected to the first process and the second process by passing through any one of a first path passing through the first device and the third device, a second path passing through the first device and the fourth device, a third path passing through the second device and the third device, or a fourth path passing through the second device and the fourth device; An analysis method for analyzing measurement information, which is information from an inspection device that measures processing results of a first substrate that has passed through the first path, a second substrate that has passed through the second path, a third substrate that has passed through the third path, and a fourth substrate that has passed through the fourth path, among the substrates, comprising: a path information acquisition unit that acquires board identification information that identifies each of the boards and path identification information that identifies a path that each of the boards has passed; a measurement information acquisition step of acquiring the substrate identification information and the measurement information which is a processing result of each substrate measured by the inspection device; a calculation step of calculating, based on the path identification information and the measurement information, a first device characteristic generated on the first substrate on which the first processing has been performed by the first device, a second device characteristic generated on the third substrate on which the first processing has been performed by the second device, a third device characteristic generated on the first substrate on which the second processing has been performed by the third device, and a fourth device characteristic generated on the second substrate on which the second processing has been performed by the fourth device; The analysis method has the following features:

28. 1. An analysis method for analyzing measurement information, which is information from an inspection apparatus that measures a processing result of a substrate that has been subjected to a first processing and a second processing, using a first apparatus or a second apparatus that is a first processing apparatus that performs a first processing on the substrate, and a third apparatus or a fourth apparatus that is a second processing apparatus that performs a second processing on the substrate after the first processing, comprising: a substrate identification information acquisition step of acquiring substrate identification information for identifying the substrate; a measurement information acquiring step of acquiring the measurement information; a path identification information acquisition step of acquiring path identification information which is information on devices used in the first process and the second process on the substrate; an analysis method comprising a calculation step of calculating a first apparatus characteristic generated on the substrate subjected to the first processing by the first apparatus, a second apparatus characteristic generated on the substrate subjected to the first processing by the second apparatus, a third apparatus characteristic generated on the substrate subjected to the second processing by the third apparatus, and a fourth apparatus characteristic generated on the substrate subjected to the second processing by the fourth apparatus based on the substrate identification information, the measurement information, and the path identification information.

29. The path identification information is information indicating paths of devices through which the substrate is processed in the first process and the second process, the paths including a first path processed by the first device and the third device, a second path processed by the first device and the fourth device, a third path processed by the second device and the third device, and a fourth path processed by the second device and the fourth device, 29. The analysis method of claim 28, wherein the substrate identification information is information that distinguishes between a first substrate that passes through the first path, a second substrate that passes through the second path, a third substrate that passes through the third path, and a fourth substrate that passes through the fourth path.

30. A first processing apparatus having a first device and a second device and performing a first process on a first substrate, a second substrate, a third substrate, and a fourth substrate, and a second processing apparatus having a third device and a fourth device and performing a second process on the first to fourth substrates, the first to fourth substrates are subjected to the first process and the second process by passing through any one of a first path passing through the first device and the third device, a second path passing through the first device and the fourth device, a third path passing through the second device and the third device, or a fourth path passing through the second device and the fourth device; An analysis system that analyzes measurement information, which is information from an inspection device that measures processing results of a first substrate that has passed through the first path, a second substrate that has passed through the second path, a third substrate that has passed through the third path, and a fourth substrate that has passed through the fourth path, a path information acquisition unit that acquires board identification information that identifies each of the boards and path identification information that identifies a path that each of the boards has passed; a measurement information acquisition unit that acquires the substrate identification information and the measurement information that is a processing result of each of the substrates measured by the inspection device; A classification unit that classifies the measurement information into predetermined classification items based on the board identification information and the path identification information; a display unit that displays the results of the classification by the classification unit; An analysis system comprising:

31. the measurement information acquisition unit acquires an amount of error between a design value of each of the substrates and a measurement value of each of the substrates measured by a measurement device; The analysis system according to claim 30.

32. 32. The analysis system according to claim 30 or 31, wherein the specified classification item is at least one of the first processing apparatus, the first apparatus, a tray for carrying the substrate into an exposure apparatus, a name of a layer for exposing the substrate, and a mask having a specified pattern and exposed onto the substrate using a projection optical system.

33. a selection unit for selecting at least a portion of the measurement information displayed on the display unit; a correction condition calculation simulation calculation unit that calculates a correction condition based on the measurement information selected by the selection unit, applies the correction condition to the measurement information to perform a simulation, and calculates corrected measurement information; The display unit displays the corrected measurement information calculated by the correction condition calculation simulation operation unit.

32. The analysis system according to claim 30 or 31.

34. A first processing apparatus having a first device and a second device and performing a first process on a substrate, and a second processing apparatus having a third device and a fourth device and performing a second process on the substrate, the substrate is subjected to the first process and the second process by passing through any one of a first path passing through the first device and the third device, a second path passing through the first device and the fourth device, a third path passing through the second device and the third device, or a fourth path passing through the second device and the fourth device; an analysis system that analyzes measurement information, which is information from an inspection device that measures processing results of each of the substrates, a first substrate that has passed through the first path, a second substrate that has passed through the second path, a third substrate that has passed through the third path, and a fourth substrate that has passed through the fourth path, a route information acquisition unit that acquires route identification information, which is information regarding the route along which each of the boards has passed; a measurement information acquiring unit that acquires the measurement information measured by the inspection device; a calculation unit that calculates a first device characteristic that is a characteristic of the first device, a second device characteristic that is a characteristic of the second device, a third device characteristic that is a characteristic of the third device, and a fourth device characteristic that is a characteristic of the fourth device based on the path identification information and the measurement information of the first board, the second board, the third board, and the fourth board; An analysis system comprising: