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

Technical Field

[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 in Japan on April 28, 2021, and incorporates its content herein by reference.

Background Art

[0002] Conventionally, in the field of semiconductor component manufacturing technology, there has been a technique of projecting and exposing 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 using an exposure apparatus. In such an exposure technique, the photomask and the wafer are aligned with high precision, and the exposure pattern is superimposed on a pattern already formed on the wafer for projection exposure. When the exposure apparatus performs overlay exposure, high overlay accuracy is required with respect to the already formed pattern. For example, variations in exposure results due to differences in film forming apparatuses and etching apparatuses for processing wafers may not be negligible. Specifically, when a wafer is deformed by a processing apparatus such as a film forming apparatus or an etching apparatus, and exposure is performed on the deformed wafer using an exposure apparatus, the pattern is exposed at a position shifted with respect to the pattern formed on the wafer, so that the patterns are not accurately superimposed. Furthermore, the amount of deformation of the wafer varies depending on which processing apparatus has processed it. Therefore, variations occur in the exposure results, that is, the overlapping condition of the patterns. To correct this variation, there has been a technique of calculating an overlay prediction correction value based on the process history or the like and performing correction during the exposure process (see, for example, Patent Document 1). However, in the conventional technology as described above, it has been impossible to identify which processing apparatus among the processing apparatuses has caused the amount of deformation of the wafer with respect to the wafer. In addition, when a specific processing apparatus is replaced, the data accumulated until then becomes unusable, and there has been a problem that the correction of the exposure pattern cannot be performed.

Prior Art Documents

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-34682 [Overview of the project]

[0004] The analysis system of the present invention comprises a first processing apparatus having a first apparatus and a second apparatus for performing a first process on a substrate, and a second processing apparatus having a third apparatus and a fourth apparatus for performing a second process on the substrate, wherein the substrate is subjected to the first and second processes by passing through one of the following paths: a first path passing through the first and third apparatus, a second path passing through the first and fourth apparatus, a third path passing through the second and third apparatus, or a fourth path passing through the second and fourth apparatus, and the processing results of each substrate, specifically 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, are measured from an inspection apparatus. An analysis system for analyzing information, comprising: a route information acquisition unit that acquires substrate identification information for identifying each substrate and route identification information for each route; a measurement information acquisition unit that acquires the substrate identification information and measurement information measured by the inspection device; and a calculation unit that calculates, based on the route identification information, the substrate identification information and the measurement information, a first device characteristic occurring in the first substrate that has undergone the first processing by the first device; a second device characteristic occurring in the second substrate that has undergone the first processing by the second device; a third device characteristic occurring in the third substrate that has undergone the second processing by the third device; and a fourth device characteristic occurring in the fourth substrate that has undergone the second processing by the fourth device. The present invention provides an analysis system that analyzes measurement information from an inspection device that measures the processing results of a substrate after it has undergone the first and second processing, using a first or second processing device which is a first processing device that performs the first processing on a substrate, and a third or fourth processing device which is a second processing device that performs the second processing on the substrate after the first processing. The analysis system includes a calculation unit that calculates, based on substrate identification information that identifies the substrate, path identification information which is information of the devices used in the first and second processing on the substrate, and the measurement information, a first device characteristic that occurs on the substrate after the first processing is performed by the first device, a second device characteristic that occurs on the substrate after the first processing is performed by the second device, a third device characteristic that occurs on the substrate after the second processing is performed by the third device, and a fourth device characteristic that occurs on the substrate after the second processing is performed by the fourth device.

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

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

[0008] The present invention provides an analysis method comprising a first processing apparatus having a first apparatus and a second apparatus for performing a first process on a substrate, and a second processing apparatus having a third apparatus and a fourth apparatus for performing a second process on the substrate, wherein the substrate is subjected to the first and second processes by passing through one of the following paths: a first path passing through the first and third apparatus, a second path passing through the first and fourth apparatus, a third path passing through the second and third apparatus, or a fourth path passing through the second and fourth apparatus, and the analysis method provides analysis of measurement information from an inspection apparatus that measures the 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. An analysis method comprising: a path information acquisition unit that acquires substrate identification information for identifying each substrate and path identification information for identifying the path taken by each substrate; a measurement information acquisition step that acquires the substrate identification information and measurement information which is the processing result of each substrate 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 occurring in the first substrate that has undergone the first processing by the first device, a second device characteristic occurring in the third substrate that has undergone the first processing by the second device, a third device characteristic occurring in the first substrate that has undergone the second processing by the third device, and a fourth device characteristic occurring in the second substrate that has undergone the second processing by the fourth device. The present invention provides an analysis method for analyzing measurement information from an inspection device that measures the processing results of a substrate that has undergone the first and second processing by a first or second processing device which is a first processing device that performs the first processing on a substrate, and a third or fourth processing device which is a second processing device that performs the first processing on the substrate and then the second processing on the substrate, comprising: a substrate identification information acquisition step for acquiring substrate identification information for identifying the substrate; a measurement information acquisition step for acquiring the measurement information; a route identification information acquisition step for acquiring route identification information which is information of the devices used in the first and second processing on the substrate; and a calculation step for calculating, based on the substrate identification information, the measurement information, and the route identification information, a first device characteristic occurring in the substrate that has undergone the first processing by the first device, a second device characteristic occurring in the substrate that has undergone the first processing by the second device, a third device characteristic occurring in the substrate that has undergone the second processing by the third device, and a fourth device characteristic occurring in the substrate that has undergone the second processing by the fourth device.

[0009] The analysis system of the present invention comprises a first processing apparatus having a first apparatus and a second apparatus that performs 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 apparatus and a fourth apparatus that performs a second processing on the first to fourth substrates, wherein the first to fourth substrates are subjected to the first and second processing by passing through one of the following paths: a first path passing through the first apparatus and the third apparatus, a second path passing through the first apparatus and the fourth apparatus, a third path passing through the second apparatus and the third apparatus, or a fourth path passing through the second apparatus and the fourth apparatus, and the first substrate that has passed through the first path, the second substrate that has passed through the second path An analysis system for analyzing 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, comprising: a path information acquisition unit that acquires substrate identification information that identifies each of the substrates and path identification information that identifies the path through which 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 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 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 comprises a first processing apparatus having a first apparatus and a second apparatus for performing a first process on a substrate, and a second processing apparatus having a third apparatus and a fourth apparatus for performing a second process on the substrate, wherein the substrate is subjected to the first and second processes by passing through one of the following paths: a first path passing through the first apparatus and the third apparatus, a second path passing through the first apparatus and the fourth apparatus, a third path passing through the second apparatus and the third apparatus, or a fourth path passing through the second apparatus and the fourth apparatus, and the substrate is further divided into 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 path. An analysis system for analyzing measurement information, which is information from an inspection device that measures the processing results of each substrate of a fourth substrate that has passed through, comprising: a path information acquisition unit that acquires path identification information, which is information about the path that each substrate has passed through; 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, which is the characteristic of the first device, a second device characteristic, which is the characteristic of the second device, a third device characteristic, which is the characteristic of the third device, and a fourth device characteristic, which is the characteristic of the fourth device, based on the path identification information and the measurement information of the first, second, third, and fourth substrates. [Brief explanation of the drawing]

[0011] [Figure 1] This figure shows an example of a processing apparatus according to the first embodiment. [Figure 2] This figure shows an example of measurement results according to the first embodiment. [Figure 3] This figure shows an example of the functional configuration of the analysis system according to the first embodiment. [Figure 4] This is a flowchart showing a series of operations related to the calculation of the correction value according to the first embodiment. [Figure 5] This figure shows an example of the correspondence between the route and the measurement results according to the first embodiment. [Figure 6] This is a flowchart showing a series of operations related to the visualization of the error amount according to the first embodiment. [Figure 7]It is a diagram showing an example when visualizing an error amount using the Plate coordinate system according to the first embodiment. [Figure 8] It is a diagram showing an example when visualizing an error amount using the orthogonal coordinate system according to the first embodiment. [Figure 9] It is a diagram showing an example when visualizing an error amount using the heat map according to the first embodiment. [Figure 10] It is a diagram showing an example of a correction value for each lens module according to the first embodiment. [Figure 11] It is a diagram showing an example of the correspondence between the processing device and the correction value according to the first embodiment. [Figure 12] It is a diagram showing an example of the correspondence between the path and the correction value according to the first embodiment. [Figure 13] It is a diagram showing an example of the functional configuration of an analysis system regarding the application of the correction value according to the first embodiment. [Figure 14] It is a flowchart showing a series of operations regarding the application of the correction value according to the first embodiment. [Figure 15] It is a diagram for explaining an example when selecting the correction value according to the first embodiment based on the correction value for each registered device 100. [Figure 16] It is a diagram for explaining an example when selecting the correction value according to the first embodiment based on the registered path correction value. [Figure 17] It is a diagram showing an example of the functional configuration of an analysis system according to the second embodiment. [Figure 18] It is a diagram showing an example of the functional configuration of an analysis system according to the third embodiment. [Figure 19] It is a diagram showing an example of the correspondence between the path and the measurement result according to the third embodiment. [Figure 20] It is a diagram showing an example of the functional configuration of an analysis system according to the fifth embodiment. [Figure 21] It is a diagram showing an example of the time change of the result obtained by statistically calculating the measurement result according to the eighth embodiment. [Figure 22]It is a figure showing an example of the time change of the measurement result for each condition according to the eighth embodiment. [Figure 23] It is a figure showing an example of the functional configuration of the analysis device according to the eighth embodiment. [Figure 24] It is a figure for explaining the effect when the measurement results are classified for each tray according to the eighth embodiment. [Figure 25] It is a figure showing an example of the functional configuration of the analysis device according to the ninth embodiment. [Figure 26] It is a figure for explaining the correction simulation according to the ninth embodiment. [Figure 27] It is a figure for explaining the average difference according to the ninth embodiment. [Figure 28] It is a figure for explaining the calculation of the correction value of an unknown path according to a modification of the first embodiment.

Mode for Carrying Out the Invention

[0012] [First Embodiment] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a figure showing an example of a processing device according to the first embodiment. An example of the processing device 20 will be described while referring to the figure. The processing device 20 is a device that performs a predetermined process on a substrate (hereinafter, collectively referred to as a substrate) such as a semiconductor wafer or a glass plate in a predetermined process P. The processing device 20 may be, for example, a film forming device such as a sputtering device or a CVD (Chemical Vapor Deposition) device, a coating device for applying a photosensitive material such as a photoresist, an exposure device, a developing device, an etching device, a heat treatment device such as an annealing device, etc. The processing device 20 has a plurality of devices / parts where the process is performed, and one of them is called the device 100. In an example shown in the figure, as an example of the device 100 of the processing device 20 in the process P, the devices 110, 111, 121, and 122 will be described. The processing device 20 may also be, for example, a coating device for applying a photosensitive material such as a photoresist, an exposure device, a developing device, an etching device, a heat treatment device such as an annealing device, etc. The processing device 20 has a plurality of devices / parts where the process is performed, and one of them is called the device 100. In an example shown in the figure, as an example of the device 100 of the processing device 20 in the process P, the devices 110, 111, 121, and 122 will be described.

[0013] The first processing apparatus 21, consisting of apparatus 110 and apparatus 111, performs process P1. Apparatus 110 is also 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 deposition apparatuses that perform a film deposition process on a substrate. The second processing apparatus 22, consisting of apparatus 121 and apparatus 122, performs process P2. Apparatus 121 is also 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 also be photoresist coating apparatuses that apply photoresist. In the example shown in Figure 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, the substrate is processed by one of the following paths: path R1 processed by apparatus 110 and apparatus 121, path R2 processed by apparatus 110 and apparatus 122, path R3 processed by apparatus 111 and apparatus 121, or path R4 processed by apparatus 111 and apparatus 122. Path R1 is also referred to as the first path, path R2 as the second path, path R3 as the third path, and path R4 as the fourth path. A substrate that has undergone both the first and second processes by passing through the first path is also referred to as the first substrate, a substrate that has undergone both the first and second processes by passing through the second path is also referred to as the second substrate, a substrate that has undergone both the first and second processes by passing through the third path is also referred to as the third substrate, and a substrate that has undergone both the first and second processes by passing through the fourth path is also referred to as the fourth substrate.

[0014] Figure 2 shows an example of measurement results according to the first embodiment. An example of measurement result MR according to the first embodiment will be explained with reference to this figure. Measurement result MR is the result of measuring the amount of error that occurred in the substrate after a predetermined process has been applied to the substrate MR. Figure 2(A) shows measurement result (processing result) MR1 as an example of measurement result MR. Measurement result MR1 is the result of measuring the amount of error that occurred in the substrate after processing by path R1. Measurement result MR1 is the measurement result for the entire substrate. Multiple product patterns are exposed on one substrate, and the exposure of one product is called one scan. In Figure 2(A), four products are exposed. Measurement result MR1 displays the measurement results for each scan: measurement result MR1-1, measurement result MR1-2, measurement result MR1-3, and measurement result MR1-4. Figure 2(B) shows measurement result MR2 as an example of measurement result MR. Measurement result MR2 is the result of measuring the amount of error that occurred in the substrate after processing by path R2. Measurement result MR2 is the measurement result for the entire substrate. The measurement result MR2 displays the measurement results for each scan: measurement result MR2-1, measurement result MR2-2, measurement result MR2-3, and measurement result MR2-4. 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 formed on the substrate and the processing result (measurement result) MR obtained from the inspection device 220, and is also called the amount of pattern displacement. Here, the design value of the pattern is also said to be the position that is exposed when the substrate is processed by the processing device 20 (device 100) and the substrate does not deform. 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 path difference correction value] The calculation of the path difference correction value according to the first embodiment will be explained with reference to Figures 3 to 12. Figure 3 is a diagram showing an example of the functional configuration of the analysis system 10 according to the first embodiment. An example of the functional configuration of the analysis system 10 will be described with reference to this figure. The analysis system 10 analyzes the device identification information DID, which is information from multiple processing devices 100, and the measurement information MI, which is information from the inspection device 220.

[0016] The inspection device 220 inspects the substrates. Specifically, the inspection device 220 measures the processing results of the first, second, third, and fourth substrates. The first to fourth substrates are sometimes referred to as sample substrates. A sample substrate is a substrate that is exposed in order to calculate the device characteristics (error amount) caused by each processing device 100. More specifically, the inspection device 220 measures the error amount that occurs in the substrate after it has been subjected to a predetermined processing by the processing device 20 (after the predetermined processing has been performed and the substrate has been exposed by the exposure device). The inspection device 220 outputs measurement information MI, which includes the measurement result MR after the substrate has been processed, and substrate identification information SID for identifying the substrate, to the analysis device 300. The inspection device 220 may perform a measurement and obtain the measurement result MR after a single process has been applied to the substrate by a single processing device 20, or it may perform a measurement and obtain the measurement result MR after multiple processes have been applied to the substrate by multiple processing devices 20.

[0017] The analysis device 300 comprises 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) that each device 100 (device 110, device 111, device 121, and device 122) generates on the substrate. The analysis device 300 may also acquire information regarding processing applied to the substrate from the device 100, along with device identification information DID and substrate identification information SID. Information regarding processing applied to the substrate may, for example, be the processing conditions for the processing applied to the substrate. The analysis device 300 may also calculate the amount of error that the processing device 20 generates on the substrate using a similar method as shown below.

[0018] The route information acquisition unit 310 acquires device identification information DID and substrate identification information SID from the processing unit 20 or the devices 100 within the processing unit (devices 110, 111, 121, and 122). By acquiring the device identification information DID and substrate identification information SID, the route information acquisition unit 310 calculates the route identification information RID and outputs the route identification information RID to the calculation unit 330. The route identification information RID is information that identifies the multiple processing units 100 that performed processing on a predetermined substrate that has undergone multiple processing. Specifically, the route identification information RID is information for identifying which of the routes R1, R2, R3, and R4 the substrate passed through to be processed. That is, the route information acquisition unit 310 acquires the substrate identification information SID that identifies the substrate and the route identification information RID of the first, second, third, and fourth routes that the substrate passed through.

[0019] The measurement information acquisition unit 320 acquires substrate identification information SID and measurement information MI measured by the inspection device 220. The measurement information acquisition unit 320 also acquires measurement information MI from multiple substrates. In other words, the measurement information acquisition unit 320 acquires measurement information consisting of substrate identification information SID and the processing results of the first substrate, second substrate, third substrate, and fourth substrate measured by the inspection device 220.

[0020] The calculation unit 330 calculates the device characteristics CI for each device 100 based on the acquired route identification information RID, measurement information MI, and substrate identification information SID. The device characteristics for each device 100 may be the amount of error caused to the substrate by the processing of device 100 when the substrate is processed by device 100. In this embodiment, the first and second devices perform the first processing, and the third and fourth devices perform the second processing. That is, the calculation unit 330 calculates the first device characteristics resulting from a substrate that has undergone the first processing by the first device, the second device characteristics resulting from a substrate that has undergone the first processing by the second device, the third device characteristics resulting from a substrate that has undergone the second processing by the third device, and the fourth device characteristics resulting from a substrate that has undergone the second processing by the fourth device, based on the route identification information RID and measurement information MI.

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

[0022] Figure 4 is a flowchart showing a series of operations related to the calculation of the correction value according to the first embodiment. The series of operations performed by the analysis system 10 when calculating the correction value will be explained with reference to this figure.

[0023] (Step S110) The analysis device 300 collects measurement information MI, device identification information DID, and substrate identification information SID. Specifically, the route 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 route information acquisition unit 310 acquires the route information RID corresponding to each substrate from the substrate identification information DID and substrate identification information SID. Alternatively, the route information acquisition unit 310 may acquire the route identification information RID directly from an external device 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 the information of the device 100 that the substrate passed through. Specifically, the analysis device 300 associates the information from the device 100 for each path the substrate has traveled with the measurement information MI of the substrate that has traveled through each path. More specifically, the analysis device 300 associates the path identification information RID with the measurement information MI based on the correspondence relationship I1 between the acquired 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] Figure 5 shows an example of the correspondence between the route and measurement results according to the first embodiment. The information organization performed by the analysis device 300 will be explained with reference to this figure. In the example shown in the figure, information I1, which is information about the equipment 100 that each path has passed through, and information I2, which is measurement information MI including the measurement result MR of the substrate that has passed through each path, are associated. Measurement information MI is information about the measurement, including the measurement result MR, which is the result of measuring the substrate processed through process 1 + process 2 + process 3 + ... process N. From this, the influence of each process is identified. Furthermore, even within a single process, if there are multiple equipment 100 (for example, equipment 110 and equipment 111), the degree of influence of each equipment on the substrate will differ. Therefore, the influence of equipment 110 on the substrate and the influence of equipment 111 on the substrate are identified. This information showing the degree of influence of equipment 110 (first equipment characteristics) and information showing the degree of influence of equipment 111 (second equipment characteristics) are called equipment characteristics.

[0026] Specifically, the information I1, which is the information of the device 100 that each path has passed through, is as follows: The first substrate that passed through the path where the substrate identification information SID is "S01" and the path identification information RID is "R01" is processed by device 110 as step P1, and the substrate identification information SID is "S03" is processed by device 121 as step P2. The second substrate that passed through the path where the path identification information RID is "R02" is processed by device 110 as step P1 and by device 122 as step P2. The third substrate that passed through the path where the substrate identification information SID is "S03" and the path identification information RID is "R03" is processed by device 111 as step P1 and by device 121 as step P2. The fourth substrate that passed through the path where the substrate identification information SID is "S04" and the path identification information RID is "R04" is processed by device 111 as step P1 and by device 122 as step P2. Furthermore, the measurement information MI (Meas.1) of the substrates that passed through each path is associated as follows: "Meas.1" for the first substrate that passed through path R1, "Meas.2" for the second substrate that passed through path R2, "Meas.3" for the third substrate that passed through path R3, and "Meas.4" for the fourth substrate that passed through path R4. Here, the path identification information RID is associated with the measurement information MI, but it is also acceptable to associate only the measurement result MR with the path identification information RID.

[0027] (Step S130) Returning to Figure 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 of the 100 devices using the least squares method.

[0028] Here, we will explain an example of how the analysis device 300 calculates the device characteristics for each device 100 using the least squares method. The calculation unit 330 analytically calculates the characteristics using simultaneous equations or the least squares method based on the route identification information RID and measurement information MI for each path. Specifically, the least squares method involves the calculation unit 330 minimizing the objective function due to the squared error shown in equation (1) below, and calculating the device characteristics for each device 100.

[0029]

number

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

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

[0032]

number

[0033] In other words, the processing result (error amount) in path i can be obtained by solving equation (3) below.

[0034]

number

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

[0036]

number

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

[0038]

number

[0039] Furthermore, the analysis device 300 may be visualized using a predetermined method for the calculated device characteristics of each device 100. Figure 6 is a flowchart showing a series of operations related to the visualization of device characteristics according to the first embodiment. The series of operations for visualizing device characteristics will be explained with reference to this figure.

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

[0041] Figure 7 shows an example of visualization of device characteristics using the Plate coordinate system according to the first embodiment. An example of a Plate coordinate system PC will be explained with reference to this figure. The figure shows four Plate coordinate systems PC as an example of a Plate coordinate system PC. Plate coordinate system PC1 shows the amount of pattern deviation caused by device 110 (hereinafter referred to as the device characteristics (error amount) of device 110), Plate coordinate system PC2 shows the device characteristics of device 111, Plate coordinate system PC3 shows the device characteristics of device 121, and Plate coordinate system PC4 shows the device characteristics of device 122.

[0042] Here, the processing unit 20 has two or more devices 100 (for example, devices 110 and 111 of device 21 in process P1), but the units of device 100 can be set arbitrarily. For example, if there are two processing units that process process P1, the two devices are collectively called the processing unit 20, and one of the two devices is called device 100 (115) and the other is called device 100 (118). Furthermore, if the device 100(115) has two processing units, and the way in which these two processing units affect the substrate differs, the device 100(115) may be further subdivided so that processing unit 1 of the device 100 becomes device 100(116) and processing unit 2 of the device 100 becomes device 100(117). Also, even if the same device 100 (for example, device 110) is used, if the processing conditions (such as processing temperature and processing time) differ and the effect on the substrate differs, the device 110 may be subdivided so that when processing condition A is met, it becomes device 1101, and when processing condition B is met, it becomes device 1102.

[0043] By visualizing the data using a Plate coordinate system PC and displaying the results on the characteristic display unit 331, the characteristic (error) trend of each device 100 can be visualized, making it easy to visually identify the characteristic (error) trend. The calculation unit 330 may also visualize the device characteristics using other methods that allow for quantitative determination, such as in step S132 or step S133. Here, the characteristic display unit 331 only needs to be able to display the device characteristics, and may be, for example, a liquid crystal display, an organic EL (electroluminescence) display, etc.

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

[0045] Figure 8 shows an example of visualization of device characteristics (error amount) using the Cartesian coordinate system according to the first embodiment. An example of the Cartesian coordinate system OF will be explained with reference to this figure. The figure shows an example of an orthogonal function system OF when the coefficients are expanded using an orthonormal function system. Figures 8(A) and 8(B) show the device characteristics of a given device 100 when Legendre polynomials are used, respectively. In the example shown in the figure, the values ​​obtained when the coefficients of each term from 1 to 18 are expanded using an orthonormal function system are shown.

[0046] By visualizing the amount of deformation component (error amount) in each term using an orthogonal function system OF, it is possible to quantitatively determine the differences in the trends of the device characteristics (error amount) in each device 100. For example, it is possible to easily determine whether the device characteristics of any two devices 100 (e.g., device 110 and device 111) have similar trends, as shown in Figures 8(A) and 8(B). Furthermore, as a specific example, if the calculation unit 330 determines from Figures 8(A) and 8(B) that the error amount trends are common for both device 110 and device 111 in terms of the coefficients L14 and L15, it may apply the correction values ​​for L14 and L15 to the correction values ​​of the Recipe described later, rather than calculating them separately for each device 100 (device 110, device 111). This specific example is also applicable to the heat map described later. Furthermore, when visualization is performed using an orthogonal function system OF, the calculation unit 330 may visualize the data in a way that facilitates comparison between multiple devices 100, such as by step S133.

[0047] (Step S133) Returning to Figure 6, the calculation unit 330 colors the device characteristics (error amount) of each device 100 in order from the largest to the smallest error amount, and separates them by the intensity of the color (Heat Map). By arranging these on a matrix, the coefficients of the orthogonal function system can be easily compared and visualized. For example, the calculation unit 330 visualizes the result of the coefficient expansion using an orthonormal function in step S132 using the Heat Map, and displays the visualized result on the characteristic display unit 331.

[0048] Figure 9 shows an example of visualizing device characteristics (error amount) using a heatmap according to the first embodiment. An example of a heatmap HM will be explained with reference to this figure. The figure shows an example of how the coefficients from 1 to 18 obtained by coefficient expansion using an orthonormal function system for “device 110”, “device 111”, “device 121”, and “device 122” are displayed on a heatmap HM. The calculation unit 330, for example, assigns the coefficients for each device 100 to values ​​from +3 to -3 and displays them. By visualizing the differences in trends among multiple devices (100), it is possible to easily compare them using a heatmap (HM).

[0049] (Step S140) Returning to Figure 4, the calculation unit 330 calculates the correction value PM for each device 100 (first correction value PM1 for device 110, second correction value PM2 for device 111, and device 12 For device 1, the third correction value PM3 is calculated, and for device 122, the fourth correction value PM4 is calculated. The calculation unit 330 may, for example, calculate the device characteristics (error amount) for each device 100 obtained by the least squares method as a correction value for each lens module 370 of the exposure apparatus, in order to apply it as a correction value when the exposure apparatus exposes the substrate, or it may also be calculated as a correction value for driving actuators etc. mounted in the projection optical system. Alternatively, the device characteristics for each device 100 may be calculated as a correction value for driving the stage on which the substrate is placed to correct it. In other words, the correction conditions may include the driving conditions of the drive unit of the substrate stage on which the substrate is held.

[0050] Figure 10 shows an example of a correction value in the first embodiment. The calculation of the correction value will be explained with reference to this figure. Figure 10 shows an example of the device characteristics (error amount) for each of the 100 devices. The device characteristics for each of the 100 devices may be the result obtained by the least squares method in step S131, for example. Furthermore, the correction value for each device 100 is calculated from the device characteristics of each device 100 shown in Figure 10. The calculation unit 330 calculates the correction value for each lens module 371 among the multiple lens modules 370 using its correction value calculation function. More specifically, the calculation unit 330 calculates a correction value 411 that corrects the exposure area 401 exposed by the lens module 371 within the exposure area 400 of the entire substrate.

[0051] (Step S150) Returning to Figure 4, the analysis device 300 stores the information of the device 100 and the correction value corresponding to the device 100. For example, the analysis device 300 stores the information of the device 100 and the correction value PM corresponding to the device 100 as corresponding information CI in the storage unit 340. 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, or it may store the path and the correction value in association.

[0052] Figure 11 shows an example of the correspondence between the processing device and the correction value according to the first embodiment. Referring to this figure, an example of storing the correspondence between the device 100 (device 110, device 111, device 121, device 122) and the correction value PM is shown. In the figure, correspondence information CI is shown as correspondence information CI1, correspondence information CI2, correspondence information CI3, and correspondence information CI4. Device 100 is identified by device identification information DID. In the example shown in the figure, the correction value is the correction value PM calculated for each device 100. Correspondence information CI1 is associated with "device 110", which is one device 100, and correction value PM "first correction value"; Correspondence information CI2 is associated with "device 111", which is one device 100, and correction value PM "second correction value"; Correspondence information CI3 is associated with "device 121", which is one device 100, and correction value PM "third correction value"; Correspondence information CI4 is associated with "device 122", which is one device 100, and correction value PM "fourth correction value". The storage unit 340 stores these correspondence information CIs.

[0053] Figure 12 is a diagram showing an example of the correspondence between a route and a correction value according to the first embodiment. Referring to this figure, an example of how the device 100 and a route are associated and stored is shown. In the figure, the correspondence information CI is shown as correspondence information CI11, correspondence information CI12, correspondence information CI13, and correspondence information CI14. The route is identified by the route identification information RID. In the example shown in the figure, for a substrate passing through route R1, process P1 is processed by "device 110" and process P2 is processed by "device 121", for a substrate passing through route R2, process P1 is processed by "device 110" and process P2 is processed by "device 122", for a substrate passing through route R3, process P1 is processed by "device 111" and process P2 is processed by "device 121", and for a substrate passing through route R4, process P1 is processed by "device 111" and process P2 is processed by "device 122". Route R1 is associated with route correction value RPM, which is "route correction value 1"; route R2 is associated with route correction value RPM, which is "route correction value 2"; route R3 is associated with route correction value RPM, which is "route correction value 3"; and route R4 is associated with route correction value RPM, which is "route correction value 4". The storage unit 340 may store this correspondence information CI.

[0054] [Applying path difference correction value] The application of the path difference correction value according to the first embodiment will be explained with reference to Figures 13 to 16. The analysis device 300 transmits the correction value calculated by the method described above to the exposure device, and the exposure device applies the correction value according to the path the substrate has traveled.

[0055] Figure 13 is a diagram showing an example of the functional configuration of an analysis system for applying correction values ​​according to the first embodiment. An example of the functional configuration of the analysis system 10 will be described with reference to this figure. In the description of the analysis system 10, components that have already been described with reference to Figure 3 may be omitted from the description by using the same reference numerals. The analysis system 10 further comprises a selection unit 350 and an output unit 360.

[0056] The correction condition calculation unit 351 calculates the correction conditions when exposing the substrate. The correction condition calculation unit 351 includes a selection unit 350, which selects correction values ​​for two devices that the substrate has passed through 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 route identification information RID of the substrate. The correction condition calculation unit 351 calculates the correction conditions when exposing the substrate based on the correction values ​​for the two selected devices.

[0057] The selection unit 350 selects the correction condition AR for exposure to the substrate based on a plurality of correction values ​​stored in the storage unit 340. Specifically, the selection unit 350 obtains route identification information RID and substrate identification information SID from the route information acquisition unit 310, and obtains a correction value corresponding to the route identification information RID from the storage unit 340. The correction value corresponding to the route identification information RID is a correction value corresponding to the device characteristics of the device 100 provided for each route. Each route has two or more processes (for example, a first process and a second process). Therefore, the correction value corresponding to the route identification information RID is based on at least a correction value related to the first processing device (a first correction value related to device 110 or a second correction value related to device 111) and a correction value related to the second processing device (a third correction value related to device 121 or a fourth correction value related to device 122). In other words, the selection unit 350 selects correction conditions AR to be applied when exposing a substrate that has undergone the first and second processing via a 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 storage unit 340.

[0058] The output unit 360 acquires the correction condition AR selected by the selection unit 350. The output unit 360 outputs the acquired correction condition AR to the exposure apparatus 230. Specifically, the output unit 360 outputs the correction condition AR to the exposure apparatus 230, which exposes the substrate corresponding to the correction condition AR. The exposure apparatus 230 exposes the substrate corresponding to the acquired correction condition AR.

[0059] Figure 14 is a flowchart showing a series of operations related to the application of correction values ​​according to the first embodiment. The series of operations performed by the analysis system 10 when applying correction values ​​will be explained with reference to this figure.

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

[0061] (Step S220) The analysis device 300 organizes the route information for each substrate in production. Specifically, the analysis device 300 identifies the information of the equipment 100 included in each route through which the substrate has passed. The information of the equipment 100 in each route through which the substrate has passed may be included in the route identification information RID.

[0062] (Step S230) The analysis device 300 compares the route information for each substrate in production with the route information of the registered routes. Specifically, the selection unit 350 of the correction condition calculation unit 351 matches the device 100 identified in step S220 with the correction value PM stored in the storage unit 340 and obtains the correction value PM corresponding to the device 100 in each route through which the substrate in production has passed. For example, if route R1 is a route that passes through device 110 and device 121, the selection unit 350 obtains from the storage unit 340 a first correction value which is the correction value PM corresponding to device 110 and a third correction value which is the correction value PM corresponding to device 121.

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

[0064] Figure 15 is a diagram illustrating an example of selecting a correction value according to the first embodiment, based on the correction value PM for each registered device 100. Referring to this figure, an example of selecting a correction value based on PM for each registered device 100 will be explained. The figure shows an example where the board identification information SID is "S01" and an example where the board identification information SID is "S02". Boards with board identification information SID "S01" are processed by a route with route identification information RID "R01". For a route with route identification information RID "R01", the information of the device 100 included in that route is identified in step S220. Specifically, it is identified that the route with route identification information RID "R01" includes device 110 and device 121. The selection unit 350 in the correction condition calculation unit 351 selects the first correction value, which is the correction value for device 110, and the third correction value, which is the correction value for device 121, which are stored in the storage unit 340. The correction condition calculation unit 351 then calculates the route correction value RPM by adding the first correction value and the third correction value. In the example shown in Figure 15, the "first correction value," which is the correction value PM of device 110, and the "third correction value," which is the correction value PM of device 121, are added together to form the path correction value RPM, which is applied to the board whose board identification information SID is "S01". Similarly, the sum of the correction value PM of device 110, the "first correction value," and the correction value PM of device 122, the "fourth correction value," becomes the route correction value RPM, which is applied to boards with board identification information SID "S02." The sum of the correction value PM of device 111, the "second correction value," and the correction value PM of device 121, the "third correction value," becomes the route correction value RPM, which is applied to boards with board identification information SID "S03." The sum of the correction value PM of device 111, the "second correction value," and the correction value PM of device 122, the "fourth correction value," becomes the route correction value RPM, which is applied to boards with board identification information SID "S04."

[0065] Figure 16 is a diagram illustrating an example of selecting correction values ​​according to the first embodiment, specifically when selecting based on registered path correction values. The example of selecting based on registered path correction values ​​will be explained with reference to this figure. Components already described in Figure 16 may be omitted from further explanation by using the same reference numerals. In this example, the selection unit 350 selects a route correction value RPM based on the route correction value RPM stored in the storage unit 340, and the correction condition calculation unit 351 uses the route correction RPM selected by the selection unit 350 as the correction condition. In the example shown in Figure 16, "route correction value 1" corresponding to a route with route identification information RID "R01" becomes the route correction value RPM and is applied to a board with board identification information SID "S01". Similarly, "route correction value 2" corresponding to a route with route identification information RID "R02" becomes the route correction value RPM and is applied to a board with board identification information SID "S02", "route correction value 3" corresponding to a route with route identification information RID "R03" becomes the route correction value RPM and is applied to a board with board identification information SID "S03", and "route correction value 4" corresponding to a route with route identification information RID "R04" becomes the route correction value RPM and is applied to a board with board identification information SID "S04". By pre-calculating and storing the route correction value RPM, the time required to calculate the route correction value RPM can be eliminated, thus shortening the processing time.

[0066] (Step S250) Returning to Figure 14, the analysis device 300 transmits the substrate information 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 it as the correction condition AR. The correction condition AR may also include other conditions applicable to exposure. The correction condition AR may be applied as a correction value when the exposure device exposes the substrate, and the correction condition may be output for each lens module 370 provided in the exposure device, or it may be output as a correction condition that drives actuators etc. mounted in the projection optical system. Alternatively, it may be calculated as a correction condition that drives and corrects the stage on which the substrate is placed.

[0067] (Step S260) The substrates currently in production, identified by the substrate identification information SID, are brought into the exposure apparatus 230. (Step S270) The exposure apparatus 230 adds the path correction value RPM included in the correction condition AR obtained from the analysis apparatus 300 to the correction value of Recipe and performs exposure. Here, the correction value of Recipe refers to a correction value that is applied regardless of which path is passed through, provided that the processing conditions for the substrate are the same, rather than an error amount that depends on the apparatus 100. If the processing conditions (including processing time and processing temperature) are the same even when passing through paths R1 to R4, the same correction value of Recipe is applied regardless of whether the path identification information RID is "R01", "R02", "R03", or "R04". In addition, when the output unit 250 outputs the path correction value RPM to the exposure apparatus 230 in step S250, it may also output a correction condition to the exposure apparatus 230 that includes the path correction value RPM plus the correction value of Recipe.

[0068] As a modification of the first embodiment, as shown in Figure 28, the first processing apparatus 21 in process P1 has not only apparatus 110 and apparatus 111, but also apparatus 112, and there is an unknown path that the substrate has never passed through in processes P1 and P2, or apparatus 112 is newly added to the first processing apparatus 21 in process P1, and this embodiment is also useful in such cases. More specifically, when there is a path 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 the apparatus characteristic (error amount) related to apparatus 112, using the method described above, and calculates a fifth correction value to correct the fifth apparatus characteristic. Here, even if the substrate has never passed through a path R6 that passes through apparatus 112 in process P1 and apparatus 122 in process P2, a path correction value related to path R6 can be calculated. This allows the selection unit 350 to select the fifth correction value for device 112 and the fourth correction value for device 122, and the correction condition calculation unit 351 to calculate the correction conditions for route R6 by adding up the fifth correction value and the fourth correction value.

[0069] [Summary of the effects of the first embodiment] As described above, according to this embodiment, the analysis device 300 includes a path information acquisition unit 310 to acquire information identifying the path the substrate has taken, and a measurement information acquisition unit 320 to identify the substrate. Furthermore, the analysis device 300 includes a calculation unit 330 to calculate the amount of error for each device 100 (device 110, device 111, device 121, device 122) included in the path, and calculates a correction value PM to correct the amount of error for each device 100. Therefore, according to this embodiment, it is possible to calculate the error generated in the substrate for each device 100 that has processed the substrate. According to this embodiment, since the amount of error can be calculated for each device, the exposure pattern can be corrected according to the device 100.

[0070] Furthermore, according to this embodiment, since a correction value can be calculated for each device 100 (device 110, device 111, device 121, device 122), even for new routes, the correction value can be predicted by combining the correction values ​​of known devices 100. According to this embodiment, since the route correction value can be expressed as the sum of the correction values ​​for each device 100, even for new routes (combinations of known devices 100), the amount of error can be calculated by summing the correction values. Furthermore, according to this embodiment, the correction condition calculation unit 351 calculates the correction condition by adding the characteristics of two of the first, second, third, and fourth devices 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 device 300 calculates the error amount for each device 100 (device 110, device 111, device 121, device 122) based on the measurement information MI acquired from multiple substrates, and calculates a correction value PM to correct the error amount of each device 100. Therefore, according to this embodiment, a more accurate and reliable error amount can be calculated for each device 100. Thus, according to this embodiment, the analysis device 300 can correct the exposure pattern according to the device 100 based on a more accurate and reliable error amount.

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

[0073] Furthermore, according to this embodiment, the analysis device 300 includes a storage unit 340 to store 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 to select correction conditions to be applied when exposing a substrate that has undergone the first and second processing along a path identified by the path identification information RID, based on the stored first, second, third, and fourth correction values. In other words, the analysis device 300 calculates the exposure conditions to be applied to the substrate from the error amount of each device 100. Therefore, according to this embodiment, it is possible to select the error amount for each path through which the substrate identified by the substrate identification information SID has passed.

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

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

[0076] Furthermore, the processing apparatus 20 may be a tray, a photomask, or an electronic mask (DMD, SLM) having multiple micro-mirrors, used when placing a substrate in the exposure apparatus. More specifically, if it is a tray, and there are multiple trays in the exposure apparatus on which substrates are placed, the entire tray may be designated as the processing apparatus 20, and one of the trays as the apparatus 100. If it is a photomask, and there are multiple masks having the same pattern, the multiple masks may be designated as the processing apparatus 20, and one of the photomasks as the apparatus 100. If it is an electronic mask, the multiple electronic masks may be designated as the processing apparatus 20, and one of the electronic masks as the apparatus 100.

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

[0078] [Second Embodiment] Figure 17 shows an example of an analysis system according to the second embodiment. An example of the analysis system 10A will be described with reference to this figure. In the description of the analysis system 10A, components similar to those in the analysis system 10 may be denoted by the same reference numerals, and their description may be omitted. The analysis system 10A differs from the analysis system 10 in that the analysis device 300 is connected to a plurality of factories M via a predetermined network NW. The predetermined network NW may be, for example, the Internet. Each of the plurality of factories M is equipped with a plurality of processing devices 100, an inspection device 220, and an exposure device 230.

[0079] The processing unit 20 or the devices 100 (or devices 110, 111, 121, and 122) within the processing unit 20 outputs the device identification information DID and the substrate identification information SID to the analysis device 300 via the network NW. The inspection device 220 outputs measurement information MI and substrate identification information SID to the analysis device 300 via the network NW. The exposure device 230 outputs the calculated correction condition AR to the exposure device 230 via the network NW. The route information acquisition unit 310 acquires route identification information RID and substrate identification information SID via the network NW, based on the device identification information DID and substrate identification information SID output by the processing unit 20 or the processing unit 100 within the processing unit 20.

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

[0081] [Summary of the effects of the second embodiment] As described above, according to this embodiment, the analysis system 10A is connected to multiple factories M via a network NW. That is, the analysis system 10A can acquire the error amounts of the devices 100 installed in multiple factories M. Therefore, the analysis system 10A can calculate a correction value based on the error amounts of many devices 100 sent from multiple factories M. Thus, the analysis system 10A can correct the exposure pattern based on a more accurate and reliable error amount.

[0082] Furthermore, according to this embodiment, a correction value can be calculated based on the error amounts of many devices 100 (devices 110, 111, 121, and 122) sent from multiple factories M. Therefore, by performing coefficient expansion of the error amount of each device 100 in an orthonormal function system and expressing it quantitatively, it becomes easy to compare the individual error amounts of a large number of devices 100. Accordingly, according to this embodiment, the trend of the error amount can be shown quantitatively, making it possible to determine the error elements of the devices 100, compare devices 100, or detect abnormal devices 100.

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

[0084] [Third Embodiment] Figure 18 is a diagram showing an example of an analysis system according to the third embodiment. An example of the analysis system 10B will be described with reference to this figure. In the description of the analysis system 10B, components similar to those in the analysis system 10 may be denoted by the same reference numerals and their description may be omitted. The analysis system 10B differs from the analysis system 10 in that it has processes P41, P42, P43, P44, and P45 as the first layer L1, and processes P46, P47, and P48 as the second layer L2. 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] Figure 18 shows three different substrates being processed via different paths. Specifically, the three different substrates are processed via paths R41, R42, and R43, respectively.

[0086] Step P41 comprises apparatus 411 and apparatus 412. After processing the substrate, apparatus 411 and apparatus 412 output the substrate identification information SID of the substrate and their own apparatus identification information DID to the analysis apparatus 300B.

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

[0088] Process P43 comprises apparatus 431, apparatus 432, and apparatus 433. After processing the substrate, apparatus 431, apparatus 432, and apparatus 433 each output the substrate identification information SID of the substrate and their own apparatus identification information DID to the analysis apparatus 300B.

[0089] Process P44 includes an inspection device 440. The inspection device 440 performs predetermined measurements on the substrate and outputs the 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. After processing the substrate, apparatus 451, apparatus 452, and apparatus 453 each output the substrate identification information SID of the substrate and their own apparatus identification information DID to the analysis apparatus 300B.

[0091] Process P46 includes apparatus 461 and apparatus 462. After processing the substrate, apparatus 461 and apparatus 462 output the substrate identification information SID of the substrate and their own apparatus identification information DID to the analysis apparatus 300B.

[0092] Process P47 includes an exposure apparatus 470. The exposure apparatus 470 acquires correction conditions AR from the analysis apparatus 300B and performs exposure based on the acquired correction conditions AR.

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

[0094] Figure 19 shows an example of the correspondence between the route and measurement results according to the third embodiment. Referring to this figure, an example of the correspondence between the route and measurement results according to the third embodiment will be explained. In the example shown in the figure, information from the device 100 that each path passes through is associated with measurement information MI, which includes the measurement result MR of the substrate that passed through each path. Specifically, the path with path identification information RID "R41" is processed by device 411 as process P41, by device 421 as process P42, by device 431 as process P43, by device 452 as process P45, by device 461 as process P46, and by device 470 as process P47, while the path with path identification information RID "R42" is processed by device 412 as process P41, by device 421 as process P42, and by device 470 as process P43. Then, processing is carried out by device 433 as process P45 by device 453, by device 462 as process P46, and by device 470 as process P47. For the route where the route identification information RID is "R43", processing is carried out by device 412 as process P41, by device 422 as process P42, by device 432 as process P43, by device 451 as process P45, by device 461 as process P46, and by device 470 as process P47. The substrates that have passed through processes P41 to P47 are inspected by inspection device 480 in process P48. "Meas.41" is associated with the measurement result of the substrate that passed through route R41, "Meas.42" is associated with the measurement result of the substrate that passed through route R42, and "Meas.43" is associated with the measurement result MR of the substrate that passed through route R43.

[0095] In Figure 19, the substrate is measured in step P44 of the first layer L1. While the measurement results MR from step P44 may be associated with paths R1 and R2 in the table in Figure 19, this association is not strictly necessary. This is because the substrate is measured again in step P48 of the second layer L2, and it is sufficient that at least the latest measurement results MR of the substrate are linked to paths R1 and R2. This also applies to the third layer and beyond, not just the second layer. This is to confirm whether the substrate is being exposed accurately by understanding the error amount of each path (R41, R42) from the latest measurement results MR of the substrate.

[0096] [Summary of the effects of the third embodiment] As described above, according to this embodiment, the analysis system 10B has multiple processing steps, and each processing step includes one or more devices. Therefore, there are a number of paths equal to the product of the number of devices included in each processing step. In this case, if the error amount for each path is calculated using the conventional method, the number of error amounts to be stored increases, and the number of measurement results required to calculate the error amount per path decreases. Also, if the error amount for each path is calculated using the conventional method, there may be paths for which the error amount is not measured. However, according to this embodiment, since the error amount is calculated for each device, the number of error amounts to be stored decreases, and the number of measurement results required to calculate the error amount per device increases. Therefore, according to this embodiment, a more accurate and reliable error amount can be calculated. Furthermore, according to this embodiment, since the error amount is calculated for each device, the error amount can be calculated 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 the path with the smallest error amount among multiple paths. The analysis system 10B can also recommend routes, for example, by including a route recommendation unit (not shown). The route recommendation unit recommends routes that pass through according to the product's grade. The route recommendation section will, for example, in the case of products or layers requiring high precision, select the route that minimizes errors. I recommend the (Golden Route).

[0098] Furthermore, according to this embodiment, the analysis system 10B can compare the error amounts of each of the multiple devices in order to calculate the error amount for each device. As a result of comparing the error amounts of each device, the analysis system 10B can detect devices with large error amounts and devices with small error amounts. Therefore, according to this embodiment, the analysis system 10B can detect devices with abnormally large error amounts as devices that have abnormalities. Furthermore, according to this embodiment, even within the same device, it is possible to detect areas on the circuit board where the error amount differs significantly. The analysis device 300 can infer that some kind of abnormality has occurred in areas on the circuit board where the error amount is large, and can prompt maintenance work such as repairs. Furthermore, based on the vector diagrams and orthonormal function coefficients included in the measurement information MI, it may be suggested to identify abnormal areas on the substrate and to investigate whether there are any abnormalities in the mask drawing errors of the abnormal areas, or to investigate whether there is any correlation with the Heater position of the heating process.

[0099] Furthermore, according to this embodiment, among multiple paths, the path that the substrate is expected to traverse in the future is predetermined, and the amount of plate deformation that occurs in the processing device 100 along that path is canceled out. The exposure shape may be corrected to such an extent. By correcting this, 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, correction is made to match the amount of plate deformation that occurs in the CF (Color Filter) process. This can improve the precision of CF bonding. Furthermore, by adjusting the exposure shape to match the mask deformation and alignment accuracy in the organic vapor deposition process of the OLED manufacturing process, the vapor deposition accuracy can be improved.

[0100] Furthermore, according to this embodiment, since there are multiple processing steps, the system may be configured to distinguish between steps that should be prioritized for correction and those that should not, and to weight the correction values ​​accordingly. Specifically, the correction condition calculation unit 351 may be configured to calculate the correction conditions by weighting one of the first, second, third, or fourth characteristics. By weighting the correction values, the weighting can be reduced for steps with large variability and instability, thereby suppressing the amount of correction. Also, if 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 error amount and correction value for each device 100, but it is also possible to calculate the error amount and correction value for each processing device 20 and perform the same analysis as described above.

[0102] [Fourth Embodiment] In the embodiments described above, a method for calculating the error amount for each device 100 from the measurement results for each path was explained. 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. In the fourth embodiment, while it is assumed that the amount of error for each device 100 is calculated, it differs from the embodiments described above in that it considers the correlation between processes and calculates the amount of error for each path.

[0103] [Method 1: Combine multiple processes and treat them as a single process] In this embodiment, the analysis system 10C (not shown) uses x in equation (1) described above as a condition vector in path i, i.e., path information, as a combination of multiple devices 100. In other words, 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 processes.

[0104] In this embodiment, by treating multiple processes as a single process, it is possible to formulate the method without altering the form of the least squares method. On the other hand, because it is necessary to cover all possible combinations, the amount of information to be stored becomes enormous as the number of processes and the number of devices used in each process increase. Furthermore, as the amount of information to be stored becomes enormous, the amount of data per condition tends to decrease, which may lead to unstable estimation results.

[0105] [Method 2: Introduction of interaction terms] In this embodiment, the analysis system 10C considers the interaction between processes by introducing an interaction term in addition to equation (3) described above. Specifically, it is expressed by equation (6) below.

[0106]

number

[0107] Here, γ is the difference related to the interaction, and x int This indicates the path. In other words, according to this embodiment, by adding the error amount related to the interaction in addition to the error amount for each device 100, a more accurate error amount can be calculated.

[0108] Here, the coefficient γ of the interaction term may be trained using machine learning, such as with a neural network. Alternatively, the coefficient γ of the interaction term may be trained under constraints such as L1 regularization, which requires it to take a small value. Specifically, by reducing the value of λ expressed by equation (7) below, the proportion contributed by the interaction term can be reduced.

[0109]

number

[0110] According to this embodiment, formulation is possible without breaking the form of the least squares method. Furthermore, according to this embodiment, because the interaction terms are simply added together, even if the interaction terms for all paths have not been calculated (the interaction in that path is (Simply by setting it to 0), the amount of error in that path can be calculated.

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

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

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

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

[0115] Furthermore, the faulty device detection unit 370 may detect a faulty device 100 by comparing the error amounts of identical or similar devices 100 among a plurality of devices 100 capable of collecting data.

[0116] Furthermore, the faulty device detection unit 370 may store the malfunctions that have occurred to determine whether there is a trend in the causes of malfunctions in the multiple processing units 100, and if there is a trend in the causes of malfunctions, it 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] The warning unit 380 issues a warning to the factory that has the defective device when the defective device detection unit 370 detects a defective device. For example, the warning unit 380 notifies the factory that has the device via a predetermined communication network. If the analysis device 300D is located in the same factory as the processing device 100, inspection device 220, or exposure device 230, the warning unit 380 may warn of the occurrence of a malfunction by illuminating or flashing an LED light or by sounding a buzzer. The warning unit 380 may also notify the user of the possibility of a malfunction along with the warning. For example, the warning unit 380 may notify the user of the location and type of malfunction depending on the changes in the amount of error over time.

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

[0119] [Sixth Embodiment] In the embodiments described above, an example was given of calculating the amount of error for each device 100 based on the path the substrate took. The sixth embodiment differs from the embodiments described above in that the processing conditions for each processing device 100 are treated as path information. That is, in this embodiment, even if the substrate passes through the same device 100, if the processing conditions are different, it is treated as a different path.

[0120] The route information acquisition unit 310 treats different processing conditions as different routes even if the same processing device 100 is passed through, thereby enabling the calculation unit 330 to find the device 100 that has a large impact on the amount of error when calculating the amount of error. Processing conditions may include, for example, processing temperature. When the processing conditions are processing temperature, each processing temperature within a predetermined range is treated as a different path. By configuring it in this way, processing temperatures that have a large impact on the amount of error can be detected. Furthermore, by including a suggestion unit (not shown), processing conditions that reduce the amount of error can be estimated and suggested based on the relationship between the obtained processing temperature and the amount of error.

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

[0122] [Seventh Embodiment] In the embodiments described above, an example was explained 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 when the measurement information acquisition unit 320 is unable to acquire the measurement information MI for any reason will be described.

[0123] As an example, the calculation unit 330 acquires the alignment measurement results performed by the exposure apparatus 230 and the corresponding substrate identification information SID. Based on the path taken by the substrate identified by the substrate identification information SID and the alignment measurement results of the exposure apparatus, the calculation unit 330 calculates the amount of error.

[0124] As another example, the measurement information acquisition unit 320 acquires information that is the result of inspecting the substrate in addition to, or instead of, the measurement information MI. The information that is the result of inspecting the substrate may be information indicating the degree to which the substrate is good or defective. The information indicating the degree to which the substrate is good or defective may be, for example, information on whether or not it is defective, or information such as the product grade. In this case, the calculation unit 330 calculates the defect rate in place of the error amount based on the information that is the result of inspecting the substrate.

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

[0126] [Eighth Embodiment] Figure 21 shows the time evolution of the results of statistical calculations on the measurement results according to the eighth embodiment. In this figure, the time evolution of the results of statistical calculations on the measurement results of a single substrate is shown with time on the horizontal axis. As an example of the statistical calculation results, the vertical axis shows the standard deviation. In this embodiment, the analysis device 300 can visually display the time evolution of the accuracy and the accuracy variation of a single substrate by displaying the time evolution of the standard deviation for the measurement results. In the following explanation, we will describe an example where the measurement results are obtained through statistical calculations. However, any index that can capture the time-dependent changes in accuracy or the variability in accuracy is acceptable, and predetermined features may be used instead of the results of statistical calculations.

[0127] Figure 21(A) illustrates an example of a case where the state of the substrate gradually changes. The measurement results from March 18th to March 19th are stable at around 0.6 μm, but it can be seen that the accuracy gradually deteriorates from March 19th to March 23rd. Figure 21(B) illustrates an example of a sudden change in accuracy. The measurement results from March 18th to March 20th were stable at around 0.9 μm, but between March 20th and March 21st, the accuracy suddenly improved to around 0.2 μm. However, after that, by March 23rd, the accuracy deteriorated again to around 0.9 μm. As shown in Figure 21, by observing the time evolution of the statistically calculated measurement results of a single substrate, it is possible to capture the time evolution of the accuracy of a single substrate and the accuracy variability.

[0128] Figure 22 is a diagram showing an example of the time variation of measurement results for each condition according to the eighth embodiment. Referring to this figure, an example of showing an example of the time variation of measurement results for each condition will be explained. In this figure, the time variation of the measurement results of one substrate, obtained by statistical calculation, is shown with time on the horizontal axis. As an example of the statistical calculation results, the vertical axis shows the standard deviation. In this 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 this figure, it is possible to grasp the differences in accuracy and variability for each condition.

[0129] Figure 22(A) illustrates an example 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. Therefore, it can be seen that "Condition 1" has better accuracy than "Condition 2". Figure 22(B) illustrates an example where the variation in accuracy differs depending on the condition. 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 at approximately 0.4 (μm) to 1.2 (μm). Therefore, it can be seen that "Condition 1" has better accuracy variation than "Condition 2".

[0130] Figure 23 is a diagram showing an example of the functional configuration of the analysis device according to the eighth embodiment. The analysis device 300E will be described with reference to this 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 may be denoted by the same reference numerals, and their description may be omitted.

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

[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 storage 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 105 may be, for example, a liquid crystal display, an organic EL (electroluminescence) display, etc., and the same device as the characteristic display unit 331 may be used. The display unit 392 may be provided in the analysis device 300E, installed in a factory equipped with an exposure device, 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 device 300E via a predetermined communication network.

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

[0134] The example shown in Figure 24(A) is an example of measurement results before classification, and therefore shows all results measured by a single exposure device. From the graph of pre-classification measurement results, it can be seen that the measurement results vary in size from approximately 0.4 (μm) to 0.9 (μm). Here, a single exposure device may be equipped with multiple trays (for example, two). The graph of pre-classification measurement results does not capture the variation between trays. Figure 24(B) shows an example of measurement results after classification. Specifically, the figure displays the measurement results divided into "Tray A" and "Tray B," which are provided in a single exposure device. When the classification information acquisition unit 390 acquires information to classify each "Tray" as a classification item, the classification unit 391 classifies the measurement results for each "Tray," and the display unit 392 displays the classification results. In the figure, the measurement results for "Tray A" are shown with dashed lines, and the measurement results for "Tray B" are shown with solid lines.

[0135] In the example shown in Figure 24(B), the same measurement results are shown as in Figure 24(A), but because they are classified (grouped), the trend of the measurement results for each tray can be understood. Specifically, "Tray A" shows measurement results that vary from approximately 0.4 (μm) to 0.6 (μm), while "Tray B" shows measurement results that 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 variation of accuracy for each path. In addition, because the analysis device 300E can display the accuracy and variation of accuracy for each path, users can identify the path where a problem is occurring or select a path with good accuracy for exposure of patterns that require high accuracy.

[0136] [Ninth Embodiment] Figure 25 is a diagram showing an example of the functional configuration of the analysis apparatus according to the ninth embodiment. The analysis apparatus 300F will be described with reference to this figure. The analysis apparatus 300F differs from the analysis apparatus 300E in that it includes a simulation calculation unit 396. In the description of the analysis apparatus 300F, components similar to those in the analysis apparatus 300E may be denoted by the same reference numerals, and their explanation may be omitted.

[0137] The simulation calculation unit 396 performs a corrected simulation (hereinafter simply referred to as "simulation"). The simulation calculation unit 396 performs the simulation based on the classification items acquired by the classification information acquisition unit 390 and the measurement result selection information, which is information in which measurement results have been selected. The measurement result selection information may also be acquired by the classification information acquisition unit 390. The simulation calculation unit 396 calculates the average value of each measurement point based on the selected measurement results. The simulation calculation unit 396 calculates the corrected simulation value by subtracting the calculated average value from each measurement point. The display control unit 391 displays the simulation results on the display unit 392. The simulation calculation unit 396 may perform the simulation by correcting the pattern information when the exposure device is exposed, instead of subtracting from each measurement point, or in addition to doing so.

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

[0139] Figure 26(A) 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), and the measurement results for "Condition B" range from approximately 0.6 (μm) to 0.7 (μm). For example, Condition A is defined as a substrate using device 110 in process P1, and Condition B is defined as a substrate using device 111 in process P1. The conditions are not limited to these and can be set arbitrarily. The points indicated by white circles in the figure are points selected by the simulation calculation unit 396, and the points indicated by black circles are points not selected by the simulation calculation unit 396. In the example shown in the figure, for "Condition A", 4 out of 8 measurement results were selected, and for "Condition B", 3 out of 10 measurement results were selected. The simulation calculation unit 396 calculates the average score of the selected points. By selecting some of the measurement results from all of them, even if there are measurement results whose error amounts are extremely different from other measurement results, it is possible to calculate a correction value using only the measurement results that you want to consider.

[0140] Figure 27 is a diagram illustrating the average difference according to the eighth embodiment. Referring to this figure, the simulation calculation unit 396 will explain how to calculate the average (average difference) of the selected points. Figures 27(A) and 27(B) are the measurement results before the simulation, respectively. Figure 27(A) shows the measurement results for "Condition A," and Figure 27(B) shows the measurement results for "Condition B." The arrows in Figures 27(A) and (B) indicate the measurement results (solid line) and average value (dashed line) for the processing time of each substrate. In "Condition A," four measurement results are selected, so the four measurement results are superimposed in the diagram. In "Condition B," three measurement results are selected, so the three measurement results are superimposed in the diagram. 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 of Figure 27(B) is indicated by a dashed arrow.

[0141] Next, the simulation calculation unit 396 performs the simulation by subtracting the calculated average from each point. Figures 27(C) and 27(D) show the measurement results after the simulation, respectively. Figure 27(C) shows the simulation results for "Condition A," and Figure 27(D) shows the simulation results for "Condition B." From the simulation results, it can be seen that the strain observed before the simulation is not observed in the simulation results.

[0142] Returning to Figure 26, Figure 26(B) shows the simulation results. The simulation calculation unit 396 applies the average value calculated for 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 of both "Condition A" and "Condition B" from approximately 0.2 μm to 0.4 μm.

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

[0144] Furthermore, according to the embodiment described above, by classifying the data according to the selected channels and displaying the measurement results for each group on the display unit 392, the variability between groups can be visualized. Since the analysis device 300F performs simulations for each classified group, it is possible to simulate the results when corrections are made according to the variability between groups. In other words, according to this embodiment, the effect of updating the correction values ​​for each group can be easily visualized.

[0145] Furthermore, according to the embodiment described above, the correction value calculated by the simulation calculation unit 396 is applied to all measurement results. In other words, according to this embodiment, the time change of the result after applying the correction value can be visualized. Here, since the measurement results selected for correction are finite, the correction value may not function correctly for all measurement results. In other words, 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, the effect of the correction value on the measurement results can be determined over time.

[0146] It should be noted that the simulation calculation unit 396 only needs to include measurement results other than those selected for calculating the correction value, and is not intended to apply to all measurement results 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 the simulation by applying the correction value to the selected measurement results and the measurement results that were not selected.

[0147] Furthermore, the entirety or part of the functions of each component 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, loading the program recorded on this recording medium into a computer system, and executing it. The term "computer system" here includes hardware such as an operating system and peripheral devices.

[0148] Furthermore, "computer-readable recording media" refers to portable media such as flexible disks, magneto-optical disks, ROMs, and CD-ROMs, as well as storage units such as hard disks built into computer systems. In addition, "computer-readable recording media" may also include those that dynamically hold programs for a short period of time, such as communication lines used when transmitting programs over networks such as the Internet or communication lines such as telephone lines, and those that hold programs for a certain period of time, such as volatile memory inside computer systems that act as servers or clients in such cases. Moreover, the above-mentioned programs may be for the purpose of realizing some of the functions described above, and may also be programs that can realize the aforementioned functions in combination with programs already recorded in the computer system.

[0149] Although one embodiment of this invention has been described in detail above with reference to the drawings, the specific configuration is not limited to that described above, and various design changes can be made without departing from the spirit of this invention.

[0150] Furthermore, the display manufacturing method of the present invention is suitable for the production of displays. Also, the device manufacturing method of the present invention is suitable for the production of microdevices. [Explanation of Symbols]

[0151] 10…Analysis system, 100... Processing equipment, 110, 111, 121, 122...device, 220... Inspection equipment, 230... Exposure equipment, 300...Analysis device, 310...Route information acquisition unit, 320…Measurement information acquisition unit, 330...Calculation unit, 331...Characteristics display section 340...Storage section, 350...Selection Department, 351...Correction condition calculation unit 360... Output section, P1, P2...process, R1, R2... Route, MR... Measurement results, DID... Device Identification Information, SID… Circuit board identification information, RID... Route identification information, MI... Measurement information, PM... Correction value, CI... Compatibility information, AR... Correction conditions, PC...Plate coordinate system, OF...Orthogonal function system, HM…Heatmap

Claims

1. A first processing apparatus comprising a first apparatus and a second apparatus for performing a first process on a substrate, and a second processing apparatus comprising a third apparatus and a fourth apparatus for performing a second process on the substrate, The substrate is subjected to the first and second processes by passing through one of the following paths: a first path passing through the first and third devices, a second path passing through the first and fourth devices, a third path passing through the second and third devices, or a fourth path passing through the second and fourth devices. An analysis system for analyzing measurement information, which is information from an inspection device that measures the processing results of each of the substrates, specifically the first substrate that passed through the first path, the second substrate that passed through the second path, the third substrate that passed through the third path, and the fourth substrate that passed through the fourth path, among the aforementioned substrates, A route information acquisition unit that acquires board identification information for identifying each of the aforementioned boards and route identification information for each of the aforementioned routes, A measurement information acquisition unit that acquires the substrate identification information and the measurement information measured by the inspection device, A calculation unit calculates, based on the route identification information, the substrate identification information, and the measurement information, a first device characteristic generated on the first substrate after the first processing by the first device, a second device characteristic generated on the second substrate after the first processing by the second device, a third device characteristic generated on the third substrate after the second processing by the third device, and a fourth device characteristic generated on the fourth substrate after the second processing by the fourth device. An analysis system equipped with the following features.

2. An analysis system for analyzing measurement information, which is information from an inspection device that measures the processing results of a substrate after it has undergone the first and second processing, using a first or second processing device which is a first processing device that performs the first processing on a substrate, and a third or fourth processing device which is a second processing device that performs the second processing on the substrate after the first processing. A calculation unit calculates, based on substrate identification information for identifying the substrate, path identification information which is information about the devices used on the substrate in the first and second processing, and measurement information, a first device characteristic generated on the substrate after the first processing by the first device, a second device characteristic generated on the substrate after the first processing by the second device, a third device characteristic generated on the substrate after the second processing by the third device, and a fourth device characteristic generated on the substrate after the second processing by the fourth device. An analysis system equipped with the following features.

3. The route identification information is information indicating the route of the apparatus used for the first and second processing of the substrate, including a first route processed by the first and third apparatus, a second route processed by the first and fourth apparatus, a third route processed by the second and third apparatus, and a fourth route processed by the second and fourth apparatus. The substrate identification information is information that distinguishes between the substrates: 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. A route information acquisition unit that acquires the substrate identification information and the route identification information, The system includes a measurement information acquisition unit that acquires the substrate 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 characteristics, a second correction value for correcting the second device characteristics, a third correction value for correcting the third device characteristics, and a fourth correction value for correcting the fourth device characteristics. The system further includes a storage unit for storing 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 selects correction values ​​from the first correction value, second correction value, third correction value, and fourth correction value stored in the storage unit for two devices through which the substrate has passed, based on the substrate identification information and path identification information of a fifth substrate that is different from the first to fourth substrates among the substrates, The system further includes a correction condition calculation unit that calculates correction conditions for exposure to the fifth substrate based on the correction values ​​of the two devices selected by the selection unit. The analysis system according to claim 4, which is dependent on claim 1.

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

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

8. Based on the path identification information and the error amount, the calculation unit calculates the amount of error generated by the processing of the first device as the first device characteristic, the amount of error generated by the processing of the second device as the second device characteristic, the amount of error generated by the processing of the third device as the third device characteristic, and the amount of error generated by the processing of the fourth device as the fourth device characteristic. The analysis system according to claim 7.

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

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

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

12. The first processing apparatus has a fifth apparatus in addition to the first apparatus and the second apparatus. The route information acquisition unit acquires the substrate identification information of a sixth substrate that is different from the first to fifth substrates among the substrates that have passed through the fifth route passing through the fifth device and the third device, and the route identification information of the fifth route, and acquires The calculation unit calculates the fifth device characteristics that occur in the sixth substrate after the first processing has been performed by the fifth device, The memory unit stores the characteristics of the fifth device, The correction condition calculation unit calculates the fifth correction condition, which is the correction condition for the sixth path passing through the fifth device and the fourth device, based on the characteristics of the fifth device and the characteristics of the fourth device. The analysis system according to claim 5.

13. The system further includes a path recommendation unit that, based on the first device characteristics and second device characteristics calculated by the calculation unit, recommends the device with the smaller error amount, which is information regarding the difference between the design value and the measured value, as the path through which the fifth substrate passes. The analysis system according to claim 5.

14. The route recommendation unit recommends the route with the smallest error amount, which is information regarding the difference between the design value and the measured value, from among the first route, second route, third route, or fourth route, based on the first device characteristics, second device characteristics, third device characteristics, and fourth device characteristics calculated by the calculation unit. The analysis system according to claim 13.

15. The route information acquisition unit compares the route identification information used to identify the first route with the route identification information used to identify the first route, and if at least one of the processing conditions for the first substrate in the first device and the processing conditions for the first substrate in the third device is different, it acquires the route identification information as a seventh route, which is a different route. The analysis system according to claim 1.

16. The correction condition calculation unit calculates the correction condition by weighting at least one of the first device characteristics, the second device characteristics, the third device characteristics, or the fourth device characteristics. The analysis system according to claim 5.

17. The correction conditions include the driving conditions of the drive unit of the 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. The system further includes a fault detection unit that detects whether or not a malfunction has occurred in the first or second processing unit based on the first, second, third, and fourth processing unit characteristics. The analysis system according to claim 1 or claim 2.

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

21. The faulty device detection unit further includes a warning unit that issues a warning when it detects the fault. The analysis system according to claim 19.

22. The system further includes an output unit that outputs the correction conditions calculated by the correction condition calculation unit to the exposure device. The analysis system according to claim 5.

23. An exposure apparatus for exposing the fifth substrate using the correction conditions obtained using the analysis system described in claim 5.

24. The exposure apparatus acquires path information of the fifth substrate before it is exposed by the exposure apparatus, and performs exposure on the fifth substrate based on the correction conditions relating to the path identification information. The exposure apparatus according to claim 23.

25. Exposing the fifth substrate using the exposure apparatus described in claim 23 or claim 24, The process includes developing the exposed fifth substrate, Device manufacturing method.

26. Exposing the fifth substrate using the exposure apparatus described in claim 23 or claim 24, A method for manufacturing a display, comprising developing the exposed fifth substrate.

27. A first processing apparatus comprising a first apparatus and a second apparatus for performing a first process on a substrate, and a second processing apparatus comprising a third apparatus and a fourth apparatus for performing a second process on the substrate, The substrate is subjected to the first and second processes by passing through one of the following paths: a first path passing through the first and third devices, a second path passing through the first and fourth devices, a third path passing through the second and third devices, or a fourth path passing through the second and fourth devices. An analysis method for analyzing measurement information, which is information from an inspection device that measures the processing results of the first substrate that passed through the first path, the second substrate that passed through the second path, the third substrate that passed through the third path, and the fourth substrate that passed through the fourth path, among the aforementioned substrates, A path information acquisition step, which acquires substrate identification information that identifies each of the aforementioned substrates and path identification information that identifies the path that each of the aforementioned substrates has traveled, A measurement information acquisition step, which acquires the substrate identification information and the measurement information which is the processing result of each substrate measured by the inspection device, A calculation step of calculating, based on the route identification information and the measurement information, a first device characteristic generated in the first substrate that has undergone the first processing by the first device, a second device characteristic generated in the third substrate that has undergone the first processing by the second device, a third device characteristic generated in the first substrate that has undergone the second processing by the third device, and a fourth device characteristic generated in the second substrate that has undergone the second processing by the fourth device, Tail analysis method.

28. An analysis method for analyzing measurement information, which is information from an inspection device that measures the processing results of a substrate after the first and second processing have been performed by a first or second processing device which is a first processing device that performs a first processing on a substrate, and a third or fourth processing device which is a second processing device that performs a second processing on the substrate after the first processing, A substrate identification information acquisition step for acquiring the aforementioned substrate identification information, A measurement information acquisition step for acquiring the aforementioned measurement information, A path identification information acquisition step is to acquire path identification information, which is information about the equipment used in the first and second processes, for the substrate; An analysis method comprising a calculation step of calculating, based on the substrate identification information, the measurement information, and the path identification information, a first device characteristic occurring in the substrate after the first processing by the first device, a second device characteristic occurring in the substrate after the first processing by the second device, a third device characteristic occurring in the substrate after the second processing by the third device, and a fourth device characteristic occurring in the substrate after the second processing by the fourth device.

29. The route identification information is information indicating the route of the apparatus used for the first and second processing of the substrate, including a first route processed by the first and third apparatus, a second route processed by the first and fourth apparatus, a third route processed by the second and third apparatus, and a fourth route processed by the second and fourth apparatus. The analysis method according to claim 28, wherein the substrate identification information is information that distinguishes a first substrate passing through the first path, a second substrate passing through the second path, a third substrate passing through the third path, and a fourth substrate passing through the fourth path among the substrates.

30. A first processing apparatus comprising a first apparatus and a second apparatus for performing a first processing on a first substrate, a second substrate, a third substrate and a fourth substrate, and a second processing apparatus comprising a third apparatus and a fourth apparatus for performing a second processing on the first to fourth substrates, The first to fourth substrates are subjected to the first and second processes by passing through one of the following paths: a first path passing through the first and third apparatus, a second path passing through the first and fourth apparatus, a third path passing through the second and third apparatus, or a fourth path passing through the second and fourth apparatus. An analysis system for analyzing measurement information, which is information from an inspection device that measures the 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 route information acquisition unit acquires board identification information that identifies each of the aforementioned boards and route identification information that identifies the path that each of the aforementioned boards has traveled. A measurement information acquisition unit that acquires the substrate identification information and the measurement information which is the processing result of each substrate 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 route identification information, A display unit that displays the results classified by the aforementioned classification unit, An analysis system comprising, 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 calculates correction conditions based on the measurement information selected in the selection unit, applies the correction conditions 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 unit. Analysis system.

31. A first processing apparatus comprising a first apparatus and a second apparatus for performing a first process on a substrate, and a second processing apparatus comprising a third apparatus and a fourth apparatus for performing a second process on the substrate, The substrate is subjected to the first and second processes by passing through one of the following paths: a first path passing through the first and third devices, a second path passing through the first and fourth devices, a third path passing through the second and third devices, or a fourth path passing through the second and fourth devices. An analysis system for analyzing measurement information, which is information from an inspection device that measures the processing results of each of the substrates, specifically the first substrate that passed through the first path, the second substrate that passed through the second path, the third substrate that passed through the third path, and the fourth substrate that passed through the fourth path, among the aforementioned substrates, A route information acquisition unit acquires route identification information, which is information about the path that each of the aforementioned substrates has traveled. A measurement information acquisition unit that acquires the measurement information measured by the inspection device, A calculation unit calculates, based on the route identification information and the measurement information of the first substrate, the second substrate, the third substrate, and the fourth substrate, a first device characteristic which is the characteristic of the first device, a second device characteristic which is the characteristic of the second device, a third device characteristic which is the characteristic of the third device, and a fourth device characteristic which is the characteristic of the fourth device. An analysis system equipped with the following features.

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