Method for creating a layer thickness variation profile of a surface layer of a substrate

Description

[0001] The present invention relates to a method for creating a layer thickness variation profile of a surface layer of a substrate, comprising the following steps: Selecting a plurality of measuring fields on a substrate surface of the substrate; and measuring layer thicknesses of the surface layer of the substrate at a plurality of measuring points in the respective measuring fields with a measuring device.

[0002] During post-processing of a substrate, particularly during grinding of a silicon wafer with a transparent layer applied thereon, such as a LiTaO3 layer, characteristic grinding patterns arise on a surface layer of the substrate. The topography of these grinding patterns, i.e., the local variation in the thickness of the respective surface layer, depends on the post-processing process parameters. The respective topography reflects waviness or roughness of the surface layer of the substrate.

[0003] For certain applications, for example in the manufacture of resonators, the waviness of the surface layer of the substrate must not exceed certain limits of the peak-to-valley ratio, i.e. the distance between a valley and a peak in the layer thickness profile of the surface layer.

[0004] In order to be able to assess which areas of the substrate are suitable for further processing and which areas are not suitable for further processing due to excessive surface variation, it is important to have as complete and accurate a picture as possible of a layer thickness variation profile of the surface layer of a substrate.

[0005] To determine the layer thickness variation profile of a surface layer, it can be examined at several points using an atomic force microscope, for example.

[0006] For this purpose, measuring fields are selected on a substrate surface and then scanned line by line.

[0007] Instead of an atomic force microscope, other measuring instruments can also be used to determine a layer thickness variation profile within the respective measuring fields.

[0008] The layer thickness variation profiles determined within the measuring fields using the respective measuring instruments are very accurate, but the measurements to be taken are very time-consuming.

[0009] A further problem arises from the fact that the determination of a layer thickness variation profile is limited to individual measurement fields of the substrate surface, typically only a few square millimeters in size. The layer thickness variation profiles determined in each of the measurement fields therefore represent only samples and provide only an incomplete picture of the substrate's surface structure.

[0010] US 2018 / 0347966 A1 describes a method for measuring thickness variations in a first layer of a multilayer semiconductor structure. According to this method, the layer thickness of the first layer is measured at different measuring points using a measuring device. Furthermore, a camera takes a grayscale image of the first layer, based on which the grayscale values ​​at the previously determined measuring points are determined. The measured layer thicknesses are assigned to the corresponding grayscale values, whereby a theoretical curve can be created that includes the grayscale values ​​on the abscissa and the layer thickness of the first layer on the ordinate. This curve can then be used to verify that additional layers meet the product specifications. US 2019 / 0228515 A1 describes a method for detecting defects in a substrate.The method involves creating a grayscale image of the substrate to be analyzed. Based on the resulting grayscale differences in the grayscale image, which reveal different regions of the substrate, film thickness difference values ​​are calculated, which are used to assess the substrate.

[0011] Methods for measuring layer thickness using grayscale images are known from the documents WO 2017 / 122248 A1 and JP 2010-117161 A.

[0012] Based on this, the object of the present invention is to provide a method that enables a fast and at the same time reliable evaluation of a surface structure of substrate surfaces.

[0013] The task is solved by a method for creating a layer thickness variation profile of a surface layer of a substrate with the following steps: Taking a grayscale image of a substrate surface of the substrate with a camera; selecting a plurality of measuring fields on the substrate surface of the substrate, wherein the plurality of measuring fields are selected from areas of the grayscale image; determining grayscale value variations between grayscale values ​​of the grayscale image present at the measuring points of the respective measuring fields; measuring layer thicknesses of the surface layer of the substrate at a plurality of measuring points in the respective measuring fields with a measuring device; determining layer thickness variations by calculating the difference between the layer thicknesses measured at the measuring points of the respective measuring fields; assigning the layer thickness variations determined between the measuring points to grayscale value variations determined between these measuring points; and creating the layer thickness variation profile of the surface layer based on the grayscale values ​​of the grayscale image.

[0014] To carry out the method according to the invention, a grayscale image of a substrate surface is first captured with a camera. The grayscale image represents a contiguous area of ​​the substrate surface in great detail, i.e., with very high resolution. For example, the grayscale image represents a quarter of a disk-shaped wafer. Each pixel of the grayscale image is assigned a grayscale value between 0 and 255.

[0015] Based on the grayscale image, suitable measurement fields on the substrate surface are identified and selected. Suitable measurement fields include those that exhibit a large difference in grayscale values. The selection of suitable measurement fields is performed, for example, by an operator. The operator can be assisted in selecting suitable measurement fields by image recognition and / or image analysis software. However, it is also possible for the selection of suitable measurement fields to be performed entirely by machine, for example, using artificial intelligence.

[0016] The thickness of the substrate's surface layer is then determined at a number of measurement points using a measuring device in the measurement fields determined from the grayscale image. The appropriate measuring device is selected depending on the task, availability, desired accuracy, etc. Suitable measuring devices include all commonly used devices for measuring layer thickness and / or roughness of substrate surfaces, for example, those based on a contact (i.e., tactile) measurement method, such as atomic force microscopy, or those based on non-contact methods, particularly optical measurement methods from the field of confocal technology.

[0017] The measurement fields of particularly high-resolution measuring devices are typically only a few square millimeters in size, for example, 3 mm x 3 mm. Accordingly, a measurement in the respective measurement field represents only a sample of the entire substrate surface.

[0018] Following the measurement with the measuring device, layer thickness variations between the layer thicknesses measured at the measuring points of the respective measuring fields are determined by forming the difference.

[0019] In this process, a layer thickness profile of the substrate's surface layer is transformed into a layer thickness variation profile. Ultimately, absolute layer thicknesses are not important when characterizing the waviness of the substrate surface. Depending on the measuring device used, layer thickness variation profiles in the respective measurement fields are often already determined directly by the measurement method applied. As a result of this process step, the layer thickness variation profiles for the respective measurement fields are known sufficiently, i.e., with sufficient accuracy.

[0020] The measuring points in the measuring fields are then assigned to individual image points, i.e., pixels, of the grayscale image. Thus, each of the measuring points is also assigned a grayscale value, i.e., a value between 0 and 255.

[0021] In a further process step, grayscale value variations between the grayscale values ​​of the gray image recording present at the measuring points of the respective measuring fields are determined.

[0022] Both layer thickness variations and grayscale value variations are determined between the measuring points of the respective measuring fields.

[0023] In a further process step, the layer thickness variations determined between the measuring points are assigned to the grayscale value variations determined between these measuring points.

[0024] As a result, in a further process step, the layer thickness variation profile of the entire surface layer recorded in the grayscale image can be determined based on the grayscale values ​​of the grayscale image.

[0025] Based on high-resolution measurements of the layer thickness variation within the selected measuring fields, it is possible to draw conclusions about the layer thickness variation profile of the entire surface layer of the substrate shown in the grayscale image.

[0026] The process thus provides a fast and reliable method to characterize substrates with regard to their surface roughness and to determine, for example, which areas of the substrate are suitable for further processing and which are not suitable for further processing due to excessive surface roughness.

[0027] The method according to the invention can be advantageously further developed in that the layer thickness variations determined between the measuring points in each case together with the grayscale value variations determined between the respective measuring points are stored in a data set, at least one further grayscale image is recorded from a further substrate surface of a further substrate and the data set is used to create the layer thickness variation profile of a surface layer of the further substrate based on grayscale values ​​of the further grayscale image from the further substrate surface of the further substrate.

[0028] For example, the substrate surface of a first substrate is examined within the multiple measurement fields in order to determine a relationship between the layer thickness variation and the corresponding grayscale value variation. The relationship is stored in a data set. For example, the relationship can be stored in the data set in tabular form and / or as a function. The data set is then used to directly evaluate the grayscale image recorded from another substrate surface of another substrate. This makes it possible to precisely characterize substrate surfaces with regard to their surface roughness without having to perform a layer thickness measurement on the other substrate using a corresponding measuring device.

[0029] If the layer thickness variations determined between the measuring points are stored in a data set together with the grayscale value variations determined between the respective measuring points, it proves to be particularly advantageous if the layer thickness variations contained in the data set are assigned to different layer thickness variation ranges and at least one of the grayscale value variations is assigned to the individual layer thickness variation ranges.

[0030] The assignment of the layer thickness variations to different layer thickness variation ranges can, for example, be done in the form of a layer thickness variation scale which is divided into layer thickness variation ranges, i.e. into intervals.

[0031] The layer thickness variation scale does not necessarily have to be linear, i.e., it does not necessarily have to be divided into equal intervals. By assigning the layer thickness variations contained in the data set to a discrete set of layer thickness variation ranges, a simpler assignment of the layer thickness variations to the grayscale value variations, which also typically only assume discrete values, can be made.

[0032] If the layer thickness variations determined between the measuring points are stored together with the grayscale value variations determined between the respective measuring points in a data set, the method according to the invention can be particularly advantageously further developed if several of the data sets determined on different substrates are stored and linked in a data package, wherein the data package is used to train an artificial intelligence which creates the respective layer thickness variation profile only from the grayscale values ​​or the grayscale value variations of gray image recordings recorded on still further substrates.

[0033] The method according to the invention can be improved by omitting areas in the center of a substrate and / or at a substrate edge when selecting the multiple measurement fields. These areas are not meaningful due to production reasons or cannot be reused anyway and can distort the impression of the substrate surface. Since a reliable correlation between the grayscale value variations and the layer thickness variations must be present to achieve particularly high quality in the layer thickness variation profiles determined using the method according to the invention, it is therefore advantageous if areas in the center of the substrate and / or at the substrate edge and / or in similarly critical areas of the substrate surface are left out.

[0034] After excluding areas in the center of the substrate and / or at the substrate edge, it proves particularly advantageous for the method according to the invention if the multiple measuring fields are selected such that the strongest grayscale differences are present in the measuring fields compared to other areas of the substrate surface. Strongest grayscale differences mean that, based on a grayscale range of 0 to 255 in the gray image recording, differences between the grayscale values ​​amount to at least 10 grayscales, preferably at least 15 grayscales, particularly preferably at least 20 grayscales. At the same time, the measuring fields in areas of the strongest grayscale differences typically also have the greatest distances between peaks and valleys of a substrate surface.By broadly exploiting value ranges with regard to the layer thickness variation and also with regard to the corresponding grayscale value variation, the areas of greatest grayscale differences are particularly well suited for calibrating the method according to the invention.

[0035] The accuracy of the method according to the invention can be further increased by selecting the multiple measurement fields so that they partially overlap. Partially overlapping measurement fields prevent layer thickness or grayscale value variations in the edge regions of adjacent, non-overlapping measurement fields from being lost. Furthermore, this allows for better detection of abrupt variations from one measurement field to the adjacent one.

[0036] In a particularly advantageous embodiment of the method according to the invention, the surface layer is transparent and the measuring device is an optical measuring device.

[0037] For example, a silicon substrate coated with a LiTaO3 layer is used as the substrate. The LiTaO3 layer forms a transparent surface layer of the substrate. The thickness of this surface layer is determined using, for example, an interferometer, a reflectometer, a refractometer, or a scattered light method.

[0038] Advantageous embodiments of the method according to the invention, its implementation and advantages are explained in more detail below with reference to figures. Figure 1 schematically shows a substrate from which a layer thickness variation profile is determined according to an embodiment of the method according to the invention, in a plan view; Figure 2 schematically shows a substrate surface of the substrate Figure 1 recorded grayscale image; Figure 3 schematically shows a layer thickness profile; Figure 4 schematically shows a layer thickness variation profile for the layer thickness profile from Figure 3 ; Figure 5 schematically shows a grayscale value profile; Figure 6 schematically shows a grayscale value variation profile of the grayscale value profile from Figure 5 ; and Figure 7 schematically shows a normalized grayscale variation profile of the grayscale variation profile from Figure 6 linked to a normalized layer thickness variation profile of the layer thickness variation profile from Figure 4 .

[0039] In Figure 1 a substrate 1 with a surface layer is shown in a plan view, ie with a view of a substrate surface 3 of the substrate 1.

[0040] In the Figure 1 The substrate 1 shown is a disc-shaped silicon substrate on which the surface layer is formed by a transparent LiTaO 3 layer.

[0041] Grinding the LiTaO3 layer during post-processing of substrate 1 left an approximately radially symmetrical grinding pattern on its substrate surface 3. The thickness of the surface layer varies accordingly.

[0042] According to the invention, there are now several possibilities for determining a layer thickness variation profile of the surface layer of the substrate 1. In each case, a grayscale image 4 of the substrate surface 3 or at least a portion of the substrate surface 3 is recorded.

[0043] In Figure 1 The part of the substrate surface 3 from which the gray image 4 is recorded is schematically marked by a dashed rectangle. The gray image 4 recorded from this part of the substrate surface 3 is shown in Figure 2 shown separately.

[0044] As it is in the Figures 1 and 2As can be seen, approximately a quarter of the substrate surface 3 of the substrate 1 to be examined is recorded in the grayscale image 4. Depending on the camera used and the type and shape of the substrate 1 to be examined, an image section of the substrate surface 3 for a grayscale image 4 can also be selected differently and, for example, include the entire substrate surface 3.

[0045] Suitable measuring fields 2 are selected based on the grayscale image 4. When selecting suitable measuring fields 2, areas at a substrate edge and a substrate center of the substrate 1 are not taken into account, since they are generally not very meaningful.

[0046] When selecting the measurement fields 2, particular attention is given to those areas of the substrate surface 3 that exhibit the greatest grayscale value differences and, at the same time, are expected to provide a representative sample of the layer thickness variation profile. In this case, areas were identified in the grayscale image where the grayscale values ​​were between 174 and 186.

[0047] A further criterion for the selection of suitable measuring fields 2 is that the selected measuring fields 2 can be easily measured with a measuring device for determining the layer thickness d.

[0048] In this case, the measurement fields 2 are selected by a human operator. The operator can also be assisted in selecting suitable measurement fields by image recognition and / or image analysis software. In a further embodiment of the method according to the invention, the selection of suitable measurement fields is carried out exclusively by machine using artificial intelligence.

[0049] As it is in the Figures 1 and 2 As shown, the measuring fields 2 partially overlap and thus form a cluster that covers a larger, contiguous area of ​​the substrate surface 3 without gaps.

[0050] The measuring fields 2 have a plurality of measuring points, whose position on the substrate surface 3 is specified by x, y coordinates of a Cartesian coordinate system. The position of the measuring points can also be recorded and specified using another coordinate system, e.g., a polar coordinate system.

[0051] Within the measurement fields 2, layer thicknesses d are now measured at the individual measuring points using a measuring device. In this case, the layer thickness d of the transparent surface layer of substrate 1 is measured using a reflectometer.

[0052] The layer thicknesses d measured at the measuring points provide a high-resolution and sufficiently accurate layer thickness profile of the surface layer, at least for the area of ​​the substrate surface 3 in which the corresponding measuring field 2 is located.

[0053] Such a layer thickness profile is in Figure 3 shown schematically. The Figure 3 The diagram shown represents the measured layer thickness d of the surface layer in nanometers as a function of a location of the measuring point.

[0054] From the layer thickness profile, a layer thickness variation profile is determined by subtraction, which is shown schematically in Figure 4 is shown.

[0055] Depending on the measuring device used, it may already be able to determine layer thickness variation profiles. In this case, either the layer thickness d is converted directly into layer thickness variations during the measurement, or an underlying measurement method initially provides only layer thickness differences, i.e., layer thickness variations, rather than absolute layer thicknesses. This applies, for example, to tactile measuring systems for determining surface roughness.

[0056] Since the measuring fields 2 are also contained in the grayscale image 4, ie those areas on the substrate surface 4 in which a layer thickness variation profile was measured or determined can also be seen in the grayscale image 4, each of the measuring points can also be assigned a grayscale value g between 0 and 255.

[0057] The location-dependent grayscale values ​​g can be recorded as a grayscale value profile of the surface layer of the substrate 1, as shown schematically in Figure 5 is shown.

[0058] In a further step of the method according to the invention, grayscale value variations are determined from the grayscale values ​​g of the gray image 4 present at the measuring points of the respective measuring fields 2. This provides an additional grayscale value variation profile for the measuring fields 2, as shown schematically in Figure 6.

[0059] This makes it possible to assign the layer thickness variations determined between the measuring points to the grayscale value variations determined between these measuring points, as described in Figure 7 is clarified.

[0060] In the Figure 7The diagram shown shows the layer thickness variation profiles resulting from measurements with the measuring device, together with the corresponding grayscale value variation profile determined from the grayscale image 4, whereby the values ​​of the layer thickness variation profile and the grayscale value variation profile are normalized.

[0061] With this assignment, it is possible to create a layer thickness variation profile of the entire substrate surface 3 of the substrate 1 recorded in the gray image 4, which is referred to below as the first substrate 1.

[0062] In this case, the layer thickness variations determined between the measuring points are stored in a data set together with the grayscale value variations determined between the respective measuring points.

[0063] Using this data set, a layer thickness variation profile of a surface layer of another substrate can be determined. The second substrate is essentially constructed like the first substrate 1, but may have different surface properties, in particular a different surface roughness. Another grayscale image is taken of the surface of the second substrate, and the data set created based on the first substrate 1 is applied to it.

[0064] The data set can also be applied to grayscale images 4 of other areas of the substrate surface 3 shown in Figure 1. Thus, in the Figure 1 In the example shown, the remaining substrate surface 3 is recorded with three further grayscale images 4 and a complete layer thickness variation profile of the entire substrate surface 3 is determined by applying the data set.

[0065] In this case, several of the data sets obtained on different substrates are stored and linked in a data package. This constantly growing data package is used to train an artificial intelligence system, which creates the respective layer thickness variation profile using only the grayscale values ​​g of grayscale images acquired on additional substrates. This means that after training the artificial intelligence system, it can perform a comprehensive characterization of the topography of the surface layer—in this case, the transparent LiTaO3 layer—based solely on grayscale data. Through additional reference data and a preferably constantly growing database, the accuracy and reproducibility of the method according to the invention are continuously increased.

[0066] The Figures 1 to 7The possibilities for carrying out the method according to the invention, described only as examples, can be combined with one another in a variety of ways. The same applies to individual process steps and their sequence. The method according to the invention can also be applied to a variety of different substrate surfaces and surface layers.

[0067] In further embodiments of the method according to the invention, image data of the grayscale images and / or measurement data determined with the measuring device are filtered with a bandpass filter before evaluation in order to suppress noise and macroscopic variations.

[0068] It may also be advantageous to perform a principal component analysis for each measurement field to reduce dimensionality from 2D to 1D.

[0069] Furthermore, the method according to the invention can also be used to determine the absolute thickness of a surface layer of a substrate through an additional calibration step. For this purpose, a correlation between grayscale values ​​and the absolute thickness of the surface layer is established, for example, through selected measurements with a reflectometer or another common measuring device for determining layer thicknesses.

[0070] In a further embodiment of the method according to the invention, measurements with the reflectometer for determining the layer thickness are validated by measurements with additional measuring devices. For example, the measurements with the reflectometer used here are validated using confocal microscopy.

Claims

1. Method for creating a layer thickness variation profile of a surface layer of a substrate (1), comprising the following steps: - selecting a plurality of measurement fields (2) on a substrate surface (3) of the substrate (1); - measuring layer thicknesses (d) of the surface layer of the substrate (1) with a measuring device at a plurality of measuring points in the respective measurement fields (2); - capturing a grayscale image (4) of the substrate surface (3) of the substrate (1) with a camera, wherein the plurality of measurement fields (2) is selected from regions of the grayscale image (4); - determining grayscale value variations between the grayscale values (g) present at the measuring points of the measurement fields (2) of the grayscale image (4); - determining layer thickness variations by difference calculation between the layer thicknesses measured at the measuring points of the measurement fields (2); - assigning the layer thickness variations determined between the measuring points to the corresponding grayscale value variations determined between these measuring points; and - creating the layer thickness variation profile of the surface layer on the basis of the grayscale values (g) of the grayscale image (4).

2. Method according to claim 1, characterized in that the layer thickness variations determined between the measuring points, together with the grayscale value variations determined between the respective measuring points, are stored in a data set, at least one further grayscale image is captured of a further substrate surface of a further substrate, and the data set is utilized to create the layer thickness variation profile of a surface layer of the further substrate on the basis of grayscale values (g) of the further grayscale image of the further substrate surface of the further substrate.

3. Method according to claim 2, characterized in that the layer thickness variations contained in the data set are assigned to different layer thickness variation ranges, and the individual layer thickness variation ranges are assigned to at least one of the grayscale value variations, respectively.

4. Method according to claim 2 or 3, characterized in that a plurality of the data sets which have been determined for different substrates are stored and linked in a data package, wherein the data package is used to train an artificial intelligence which creates the respective layer thickness variation profile solely from the grayscale values (g) or the grayscale value variations of grayscale images captured from yet further substrates.

5. Method according to one of the preceding claims, characterized in that, when the plurality of measurement fields (2) is selected, regions in a substrate center and / or on a substrate edge of the substrate (1) are not taken into account.

6. Method according to claim 5, characterized in that the plurality of measurement fields (2) is selected such that the grayscale contrasts are the highest in the measurement fields (2) in comparison with other regions of the substrate surface (3).

7. Method according to one of the preceding claims, characterized in that the plurality of measurement fields (2) is selected such that the measurement fields (2) partially overlap.

8. Method according to any of the preceding claims, characterized in that the surface layer is transparent, and the measuring device is an optical measuring device.