Substrate inspection apparatus and substrate inspection method

Abstract

The invention provides a substrate inspection apparatus and a substrate inspection method. The substrate inspection method includes: selecting an optimal inspection value using a test substrate; and determining at least one of a degree of crystallization and abnormal crystallization of the target substrate using the optimal inspection value. Selecting an optimal inspection value includes: capturing a focal region located in at least a portion of a test substrate; quantizing at least one of a degree of crystallization and abnormal crystallization of the test substrate; and selecting at least one of the optimal process energy density value (OPED) and the optimal abnormal crystallization determination value (OACD) as an optimal check value.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2024-0035880, filed on March 14, 2024, in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present disclosure relates to a substrate inspection apparatus and a substrate inspection method. Background Art

[0004] As the information society develops, demands for display devices for displaying images in various forms are increasing. A display device may include a transistor for driving a light-emitting element and a polysilicon crystal layer for placing the transistor.

[0005] The crystallization quality of a polysilicon substrate as a substrate including a polysilicon crystal layer is closely related to the electron mobility of transistors of a display device, and is a factor that affects the quality of the display device.

[0006] Among the factors that determine the quality of crystallization of a polycrystalline silicon substrate, the degree of crystallization can be significantly affected by the energy (or crystallization energy) of the laser used to crystallize amorphous silicon into polycrystalline silicon. For example, if the crystallization energy is low, the formation of crystal grains may be insufficient. As a result, the electron mobility of the aforementioned transistor may be reduced, leading to degradation in the quality of the display device.

[0007] Among the factors that determine the quality of polycrystalline silicon substrate crystallization, abnormal crystallization can be significantly affected by design methods, such as the placement and structure of the optical system included in the laser device that crystallizes amorphous silicon into polycrystalline silicon. For example, if some components of the optical system are misaligned in a specific direction, abnormal crystallization, such as oblique moire, may occur. As a result, the electron mobility of the transistor may decrease, leading to deterioration in the quality of the display device.

[0008] Therefore, it is desirable to specify and quantify the crystallization quality of a polycrystalline silicon substrate, such as the degree of crystallization and abnormal crystallization.

[0009] It is to be understood that this background technology section is intended, in part, to provide a useful background for understanding the technology. However, this background technology section may also include ideas, concepts, or cognitions that were not known or apparent to those skilled in the relevant art before the corresponding effective filing date of the subject matter disclosed herein. Summary of the Invention

[0010] Aspects of the present disclosure provide a substrate inspection apparatus capable of inspecting the degree of crystallinity and abnormal crystallization of a polycrystalline silicon substrate.

[0011] Aspects of the present disclosure also provide a substrate inspection method capable of quantifying the degree of crystallinity and abnormal crystallization.

[0012] However, aspects of the present disclosure are not limited to the aspects set forth herein. The above and other aspects of the present disclosure will become more apparent to those skilled in the art to which the present disclosure pertains by referencing the detailed description of the present disclosure given below.

[0013] According to aspects of the present disclosure, a substrate inspection method is provided. The method may include: selecting an optimal inspection value using a test substrate; and determining at least one of a degree of crystallization and abnormal crystallization of a target substrate using the optimal inspection value. Selecting the optimal inspection value may include: capturing a focal region located in at least a portion of the test substrate; quantifying at least one of the degree of crystallization and abnormal crystallization of the test substrate; and selecting at least one of an optimal process energy density value (OPED) and an optimal abnormal crystallization determination value (OACD) as the optimal inspection value.

[0014] In an embodiment, quantifying at least one of the degree of crystallization and abnormal crystallization of the test substrate may include quantifying the abnormal crystallization, and quantifying the abnormal crystallization may include extracting a color table value; and calculating a statistical value using the color table value.

[0015] In an embodiment, the color table used in extracting the color table value may be at least one of an RGB color table, a grayscale color table, a YCbCr color table, and an HSV color table.

[0016] In an embodiment, calculating the statistical value may include: extracting line integral data for each angle in the focal region; extracting two or more trend lines of the line integral data for each angle; and calculating the statistical value of the two or more trend lines.

[0017] In an embodiment, the line integral data may include data obtained by adding multiple color table values ​​of multiple pixels located at the same position in multiple extended lines, and the multiple extended lines may extend at the same angle in the focal area and be arranged side by side with each other in a direction different from the angle.

[0018] In an embodiment, line integral data may be extracted for each component of the color table value.

[0019] In an embodiment, each of the two or more trend lines may be a curved line of a function generated by connecting points corresponding to average values ​​of every n data included in the online integrated data, where n is a natural number.

[0020] In an embodiment, the two or more trend lines may include a first trend line and a second trend line, the first trend line may be a curve of a function generated by connecting points corresponding to the average value of each x data, the second trend line may be a curve of a function generated by connecting points corresponding to the average value of each y data, and x and y may be natural numbers equal to or less than n, and may be derived from a situation where the ratio of the statistical value of normal substrates among the test substrates to the statistical value of abnormal substrates among the test substrates is minimized.

[0021] In an embodiment, the statistical value may be calculated using the square of the deviation between two or more trend lines.

[0022] In an embodiment, the statistical value may be at least one of an average value, a standard deviation, a maximum value, and a minimum value of squares of deviations between two or more trend lines.

[0023] In an embodiment, selecting the optimal inspection value may further include manufacturing a test substrate, determining at least one of a degree of crystallization and abnormal crystallization of the target substrate may include manufacturing the target substrate, and the test substrate and the target substrate may include polysilicon formed by a laser annealer.

[0024] In an embodiment, in manufacturing a target substrate, the crystallization energy of the laser annealer may use OPED.

[0025] In an embodiment, determining at least one of a degree of crystallinity and abnormal crystallization of a target substrate may include: capturing a focal region located on at least a portion of the target substrate; and quantifying at least one of the degree of crystallinity and abnormal crystallization of the target substrate. Capturing the focal region of the target substrate may be performed in the same manner as capturing the focal region of a test substrate, and quantifying at least one of the degree of crystallinity and abnormal crystallization of the target substrate may be performed in the same manner as quantifying at least one of the degree of crystallinity and abnormal crystallization of a test substrate.

[0026] According to another aspect of the present disclosure, a substrate inspection apparatus is provided. The apparatus may include an annealer for crystallizing amorphous silicon on a substrate into polycrystalline silicon, an imaging assembly for capturing a focal region located in at least a portion of the polycrystalline silicon, and a controller for analyzing images provided by the imaging assembly. The imaging assembly may include a dark-field microscope and a differential interference contrast microscope.

[0027] In an embodiment, a dark field microscope may include a first light source, a first reflector, a guide, a first objective lens, a first tube lens, and a first camera.

[0028] In embodiments, a differential interference contrast microscope may include a second light source, a second reflector, a prism, a second objective lens, a second tube lens, and a second camera.

[0029] In an embodiment, the prism may include a refractive prism, and the refractive prism may separate light incident from the second light source into at least two beams.

[0030] In an embodiment, the at least two beams separated by the prism may be reflected at different points in the focal region.

[0031] In an embodiment, a dark field microscope may include a first light source, a first reflector, a guide, a first objective lens, a first tube lens, and a first camera, and the first camera and the second camera may be configured as one camera.

[0032] In an embodiment, the first and second light sources, the first and second reflectors, the first and second objective lenses, and the first and second tubular lenses may be configured as one light source, one reflector, one objective lens, and one tubular lens, respectively.

[0033] The substrate inspection apparatus according to the embodiment of the present disclosure can inspect the degree of crystallization and abnormal crystallization of a polycrystalline silicon substrate.

[0034] The substrate inspection method according to an embodiment of the present disclosure can quantify the degree of crystallinity and abnormal crystallization.

[0035] However, the effects of the present disclosure are not limited to those described herein. The above and other effects of the present disclosure will become more apparent to those skilled in the art to which the present disclosure pertains. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0037] These and / or other aspects will become apparent and more readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, in which:

[0038] Figure 1 is a flowchart illustrating a substrate inspection method according to an embodiment;

[0039] Figure 2 It is an icon Figure 1 Flowchart of detailed operation of operation S100;

[0040] Figure 3 It is an icon Figure 1 Flowchart of detailed operation of operation S200;

[0041] Figure 4 It is an icon Figure 2 Operation S130 and Figure 3 Flowchart of detailed operation of operation S230;

[0042] Figure 5 It is an icon Figure 4Flowchart of detailed operations of operation S131 and operation S231;

[0043] Figure 6 It is an icon Figure 4 Flowchart of detailed operations of operation S132 and operation S232;

[0044] Figure 7 is a schematic perspective view of an annealing device of a substrate inspection apparatus;

[0045] Figure 8 is a schematic cross-sectional view of a dark-field microscope of a substrate inspection apparatus;

[0046] Figure 9 is a schematic cross-sectional view of a differential interference contrast microscope of a substrate inspection apparatus;

[0047] Figure 10 It is an icon Figure 7 a schematic plan view of a focal region of a polysilicon test substrate;

[0048] Figure 11 is a schematic block diagram of a control unit of a substrate inspection device;

[0049] Figure 12 It is shown in the figure Figure 5 Operation S131a and Figure 6 Schematic example diagram of a color table for extracting a color table value in operation S132a;

[0050] Figure 13 is an enlarged schematic perspective view of some of a plurality of focal regions of a polysilicon test substrate;

[0051] Figure 14 and Figure 15 It is relatively Figure 5 Operation S131a and Figure 6 A schematic diagram of a crystallization energy range that affects the extraction of a color table value in operation S132a;

[0052] Figure 16 is a schematic diagram illustrating how the incident angle of light varies depending on the height of a hillock of a polysilicon crystal;

[0053] Figure 17 The diagram is from Figure 16 Schematic diagram of constructive interference of a first reflected beam reflected from a first hillock;

[0054] Figure 18 The diagram is from Figure 16 Schematic diagram of constructive interference of the second reflected beam reflected from the second hillock;

[0055] Figure 19is a schematic graph illustrating the variation of the constructive interference wavelength with respect to the diffraction angle;

[0056] Figure 20 is a schematic graph illustrating changes in crystallinity values ​​relative to the intensity of crystallization energy;

[0057] Figure 21 and Figure 22 It is shown in the figure Figure 6 Schematic diagram of extraction of line integral data in operation S132b;

[0058] Figure 23 and Figure 24 It is shown in the figure Figure 6 A schematic graph of a trend line of the line integration data in operation S132c;

[0059] Figure 25 It is shown in the figure Figure 6 A schematic graph of statistical values ​​of the trend line in operation S132d;

[0060] Figure 26 It is shown in the figure Figure 6 A schematic curve graph and a schematic table of statistical values ​​of the trend line in operation S132d;

[0061] Figure 27 It is shown in the figure Figure 6 A schematic table of a method for selecting a trend line of line integral data in operation S132c;

[0062] Figure 28 It is an icon Figure 3 A schematic perspective view of operation S210;

[0063] Figure 29 It is an icon Figure 28 a schematic plan view of a focal region of a polycrystalline silicon target substrate;

[0064] Figure 30 is Figure 3 A schematic graph of an image of a focus area captured in operation S220;

[0065] Figure 31 is a schematic table showing crystallization energy, crystallinity values, and crystallization pictures of various samples of polycrystalline silicon target substrates; and

[0066] Figure 32 Schematic diagram comparing a captured image of a focal region of an abnormally crystallized substrate and a captured image of a focal region of a normal substrate. DETAILED DESCRIPTION

[0067] The present disclosure will now be described more fully hereinafter with reference to the accompanying drawings, in which embodiments are shown. However, the present disclosure may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be thorough and complete and will fully convey the scope of the present disclosure to those skilled in the art.

[0068] In the drawings, the size, thickness, ratio, and dimensions of elements may be exaggerated for convenience of description and for clarity. Like reference numerals refer to like elements throughout the specification.

[0069] As used herein, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0070] For purposes of their meaning and interpretation, in the specification and claims, the term "and / or" is intended to include any combination of the terms "and" and "or." For example, "A and / or B" may be understood to mean "A, B, or A and B." The terms "and" and "or" may be used in conjunction or disjunctive conjunction sense and may be understood to be equivalent to "and / or."

[0071] For purposes of its meaning and interpretation, in the specification and claims, the phrase "at least one of" is intended to include the meaning of "at least one selected from the group of." For example, "at least one of A and B" can be understood to mean "A, B, or A and B."

[0072] It will be understood that although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element without departing from the scope of this disclosure.

[0073] For ease of description, spatially relative terms such as "below," "beneath," "lower," "above," "upper," etc. may be used herein to describe the relationship between one element or component and another element or component as illustrated in the accompanying drawings. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the accompanying drawings. For example, where the device illustrated in the figures is flipped, a device positioned "below" or "beneath" another device may be placed "above" the other device. Thus, the illustrative term "below" may include both a lower position and an upper position. The device may also be oriented in other directions, and thus the spatially relative terms may be interpreted differently depending on the orientation.

[0074] The terms “comprise,” “comprising,” “include,” “including,” “has,” “have,” “having,” and variations thereof, when used in this specification, specify the presence of stated features, integers, steps, operations, elements, components, and / or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0075] As used herein, "about," "approximately," or "substantially" are inclusive of the stated value and mean within an acceptable range of deviation for the particular value as determined by one of ordinary skill in the art, taking into account the relevant measurements and errors associated with measurement of the particular quantity (i.e., limitations of the measurement system). For example, "about" can mean within one or more standard deviations, or within ±30%, 20%, 10%, or 5% of the stated value.

[0076] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which the present disclosure belongs. It will also be understood that terms (such as those defined in commonly used dictionaries) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an idealized or overly formal sense unless expressly defined as such herein.

[0077] Figure 1 is a flowchart illustrating a substrate inspection method S1 according to an embodiment.

[0078] refer to Figure 1 The substrate inspection method S1 according to an embodiment may be a method for inspecting the crystallization quality of a polycrystalline silicon substrate. Factors determining the crystallization quality of a polycrystalline silicon substrate may include crystallization degree and abnormal crystallization. In some embodiments, the substrate inspection method S1 may be a method for inspecting the crystallization degree and abnormal crystallization of a polycrystalline silicon substrate.

[0079] The substrate inspection method S1 may include: selecting an optimal inspection value using a test substrate (operation S100); and determining a degree of crystallization and abnormal crystallization of a target substrate (operation S200).

[0080] In selecting the optimal inspection value using the test substrate (operation S100 ), an optimal inspection value for determining a degree of crystallization of the polycrystalline silicon substrate and an optimal inspection value for determining abnormal crystallization of the polycrystalline silicon substrate may be selected.

[0081] The optimal inspection value for determining the crystallinity of the polycrystalline silicon substrate may be an optimal process energy density value (OPED). The optimal inspection value for determining abnormal crystallization of the polycrystalline silicon substrate may be an optimal abnormal crystallization determination value (OACD).

[0082] In determining the degree of crystallization and abnormal crystallization of the target substrate (operation S200 ), an inspection value of the target substrate may be compared with an optimal inspection value previously selected using a test substrate.

[0083] For example, to determine the crystallinity of the target substrate, the process energy density value (PED) of the target substrate can be compared with the previously selected OPED. To determine the abnormal crystallization of the target substrate, the abnormal crystallization determination value (ACD) of the target substrate can be compared with the previously selected OACD.

[0084] Figure 2 It is an icon Figure 1 FIG. 1 is a flowchart of detailed operations of operation S100 . Figure 3 It is an icon Figure 1 FIG. 1 is a flowchart of detailed operations of operation S200 .

[0085] Apart from Figure 1 In addition, refer to Figure 2 and Figure 3 ,like Figure 2 , selecting an optimal inspection value using a test substrate (operation S100 ) may include: manufacturing a polysilicon test substrate (operation S110 ); capturing a focus region (operation S120 ); quantifying a crystallinity degree and abnormal crystallization (operation S130 ); and selecting OPED and OACD (operation S140 ).

[0086] like Figure 3 As shown in FIG, determining the crystallinity and abnormal crystallization of a target substrate (operation S200) may include: manufacturing a polycrystalline silicon target substrate (operation S210); capturing a focus area (operation S220); quantifying the crystallinity and abnormal crystallization (operation S230); and determining the crystallinity and abnormal crystallization (operation S240).

[0087] Selecting an optimal inspection value using a test substrate (operation S100) and Figure 3 The determining of the crystallinity degree and abnormal crystallization of the target substrate (operation S200 ) illustrated in FIG. 2 may be performed by a substrate inspection apparatus.

[0088] The substrate inspection apparatus may include an annealing device (annealer) 10 (see FIG. 1 ) for manufacturing a test substrate and a target substrate in operations S110 and S210, respectively. Figure 7 ); an imaging device (component) capturing the focus area in operations S120 and S220 (see Figure 8 and Figure 9 and a control unit (controller) 60 (see FIG. 1 ) for quantifying the degree of crystallization and abnormal crystallization in operations S130 and S230, selecting OPED and OACD in operation S140, and determining the degree of crystallization and abnormal crystallization in operation S240. Figure 11 ).

[0089] Will refer to it later Figures 7 to 11 Substrate inspection equipment is described.

[0090] Figure 4 It is an icon Figure 2 Operation S130 and Figure 3 FIG. 1 is a flowchart of detailed operations of operation S230 . Figure 5 It is an icon Figure 4 Flowchart of detailed operations of operation S131 and operation S231. Figure 6 It is an icon Figure 4 Flowchart of detailed operations of operation S132 and operation S232.

[0091] Apart from Figures 1 to 3 In addition, refer to Figures 4 to 6 ,like Figure 4 As shown in FIG, quantifying the degree of crystallization and abnormal crystallization (operation S130 and operation S230 ) may include: quantifying the degree of crystallization (operation S131 and operation S231 ); and quantifying abnormal crystallization (operation S132 and operation S232 ).

[0092] like Figure 5 As shown in FIG. 1 , quantifying the degree of crystallinity (operation S131 and operation S231 ) may include extracting color table values ​​(operation S131 a and operation S231 a ); and calculating statistical values ​​of the color table values ​​of all pixels in the focus area (operation S131 b and operation S231 b ).

[0093] like Figure 6 As shown in the figure, quantifying abnormal crystallization (operation S132 and operation S232) may include: extracting color table values ​​(operation S132a and operation S232a); extracting line integral data for each angle in the focal area (operation S132b and operation S232b); extracting two or more trend lines of the line integral data for each angle (operation S132c and operation S232c); and calculating statistical values ​​of the two or more trend lines (operation S132d and operation S232d).

[0094] Will refer to it later Figures 12 to 27 Detailed operations of quantifying the degree of crystallinity (operation S131 and operation S231 ) and quantifying abnormal crystallization (operation S132 and operation S232 ) are described.

[0095] Now refer to Figures 7 to 11 A substrate inspection method S1 and a substrate inspection apparatus for performing the substrate inspection method S1 are described.

[0096] Figure 7 is a schematic perspective view of an annealing device 10 of a substrate inspection apparatus.

[0097] Apart from Figure 2 and Figure 3 In addition, refer to Figure 7 , the substrate inspection apparatus may include an annealing device 10 to manufacture a test substrate and a target substrate in operations S110 and S210 , respectively.

[0098] The annealing apparatus 10 may include a laser irradiation unit 11, a stage 12, and a chamber 13. The annealing apparatus 10 may form polycrystalline silicon 36 by crystallizing amorphous silicon. The annealing apparatus 10 may be an excimer laser annealing (ELA) crystallization facility.

[0099] The laser irradiation unit 11 may irradiate the laser LS to the amorphous silicon. The stage 12 may provide a space in which the base substrate 32 can be placed. The chamber 13 may define a reaction space in which the annealing process can be performed.

[0100] The laser irradiation unit 11 may include a laser oscillator that generates laser light LS and an optical system that adjusts a beam size and a path of the laser light LS generated by the laser oscillator.

[0101] In manufacturing a polysilicon test substrate (operation S110), the annealing apparatus 10 may manufacture a polysilicon test substrate 30. The polysilicon test substrate 30 may include a base substrate 32, a buffer layer 34, and polysilicon 36. In some embodiments, the buffer layer 34 may be omitted.

[0102] Fabricating the polycrystalline silicon test substrate (operation S110 ) may include sequentially stacking a buffer layer 34 and amorphous silicon on a base substrate 32 ; and forming polycrystalline silicon 36 by crystallizing the deposited amorphous silicon.

[0103] For example, the base substrate 32 may be placed on the workbench 12 in the chamber 13. The buffer layer 34 and the amorphous silicon may be placed on the base substrate 32. The buffer layer 34 and the amorphous silicon may be stacked by a deposition process, but the present disclosure is not limited thereto. When the laser irradiation unit 11 irradiates the amorphous silicon with laser light LS, the amorphous silicon may be crystallized to form polycrystalline silicon 36.

[0104] Specifically, the laser LS may be focused on amorphous silicon. In a plan view, the laser LS focused on the amorphous silicon may have a linear shape extending along a first direction DR1. The laser focus region having a linear shape in a plan view may crystallize the amorphous silicon while moving along a second direction DR2.

[0105] In the figures, the first direction DR1 and the second direction DR2 are transverse directions and intersect each other. For example, the first direction DR1 and the second direction DR2 may be orthogonal to each other. In addition, the third direction DR3 may be a longitudinal direction that intersects (for example, is orthogonal to) the first direction DR1 and the second direction DR2. Unless otherwise specified, in the specification, the direction indicated by the arrow in each of the first direction DR1 to the third direction DR3 may be referred to as the first side, and the opposite direction may be referred to as the second side.

[0106] like Figure 7 As shown in FIG. 1 , the polysilicon 36 of the polysilicon test substrate 30 formed by the annealing apparatus 10 may include a plurality of regions formed by different crystallization energies. For example, the first region R1, the second region R2, the third region R3, and the fourth region R4 may be formed in this order by gradually decreasing the crystallization energy. Figure 10 The first to fourth regions R1 to R4 are described.

[0107] Figure 8 is a schematic cross-sectional view of a dark field microscope 40 of a substrate inspection apparatus. Figure 9 is a schematic cross-sectional view of a differential interference contrast microscope 50 of a substrate inspection apparatus.

[0108] Apart from Figure 2 and Figure 3 In addition, refer to Figure 8 and Figure 9 The substrate inspection apparatus may include an imaging device for capturing the focus area in operations S120 and S220. The imaging device may include a dark field microscope 40 and a differential interference contrast microscope 50.

[0109] The dark field microscope 40 may include a first light source unit (light source) 41, a first reflecting unit (reflector) 42, a guiding unit (guide) 43, a first objective lens 44, a first tube lens 45, and a first camera 46. The differential interference contrast microscope 50 may include a second light source unit 51, a second reflecting unit 52, a prism 53, a second objective lens 54, a second tube lens 55, and a second camera 56.

[0110] In some embodiments, the darkfield microscope 40 and the differential interference contrast microscope 50 may constitute a single imaging device. For example, the darkfield microscope 40 and the differential interference contrast microscope 50 may be included in a single imaging device. The imaging device may be a multifunctional microscope including the darkfield microscope 40 and the differential interference contrast microscope 50.

[0111] In some embodiments, when the darkfield microscope 40 and the differential interference contrast microscope 50 are included in one imaging device, the first light source unit 41 of the darkfield microscope 40 and the second light source unit 51 of the differential interference contrast microscope 50 may be configured as one light source unit, the first objective lens 44 of the darkfield microscope 40 and the second objective lens 54 of the differential interference contrast microscope 50 may be configured as one lens, the first tube lens 45 of the darkfield microscope 40 and the second tube lens 55 of the differential interference contrast microscope 50 may be configured as one lens, and the first camera 46 of the darkfield microscope 40 and the second camera 56 of the differential interference contrast microscope 50 may be configured as one camera. For example, the darkfield microscope 40 and the differential interference contrast microscope 50 may share components that perform the same function.

[0112] In the capture focus region (operation S120), the dark field microscope 40 and the differential interference contrast microscope 50 may capture the focus region FR of the polysilicon test substrate 30. In the capture focus region (operation S220), the dark field microscope 40 and the differential interference contrast microscope 50 may capture the polysilicon target substrate 30' (see FIG. Figure 28 ) of the focal area FR.

[0113] Capturing the focal region (operation S120 and operation S220 ) may include: magnifying the focal region FR using optical system components of the dark field microscope 40 and the differential interference contrast microscope 50 ; and capturing the magnified focal region FR using a camera.

[0114] For example, in magnifying the focal region FR using the dark field microscope 40, the first light LI1 may be emitted from the first light source unit 41 to the second side in the second direction DR2 and provided to the focal region FR through the first optical system. The first light LI1 reflected from the focal region FR may pass through the first objective lens 44 and the first tube lens 45 and reach the first camera 46. Figure 8 , the first optical system may include a first reflection unit 42 that changes a path of the first light LI1 emitted from the first light source unit 41 by reflection and a guide unit 43 that guides the first light LI1 whose optical path has been changed by the first reflection unit 42 .

[0115] The first reflecting unit 42 may separate the first light LI1 emitted from the first light source unit 41 into two beams. For example, the first reflecting unit 42 may include first, second, third, fourth, fifth, and sixth sub-reflectors 42a, 42b, 42c, 42d, 42e, and 42f.

[0116] The first light LI1 emitted from the first light source unit 41 may be separated into the first light LI1 traveling toward a first side in the third direction DR3 through the first sub-reflector 42a and the first light LI1 traveling toward a second side in the third direction DR3 through the second sub-reflector 42b located below the first sub-reflector 42a. The first light LI1 traveling toward the first side in the third direction DR3 may be reflected to the second side in the second direction DR2 by the third sub-reflector 42c, and the first light LI1 traveling toward the second side in the third direction DR3 may be reflected to the second side in the second direction DR2 by the fourth sub-reflector 42d. The first light LI1 reflected to the second side in the third direction DR2 by the third sub-reflector 42c may be reflected to the second side in the third direction DR3 by the fifth sub-reflector 42e, and the first light LI1 reflected to the second side in the third direction DR2 by the fourth sub-reflector 42d may be reflected to the second side in the third direction DR3 by the sixth sub-reflector 42f. The first light LI1 reflected to the second side in the third direction DR3 by the fifth sub-reflector 42e and the first light LI1 reflected to the second side in the third direction DR3 by the sixth sub-reflector 42f may each be irradiated to a point F1 in the focal region FR through the guide unit 43. The first light LI1 reflected from the focal region FR may sequentially pass through the first objective lens 44 and the first tubular lens 45 and reach the first camera 46.

[0117] In magnifying the focal region FR using the differential interference contrast microscope 50, the second light LI2 may be emitted from the second light source unit 51 to the second side in the second direction DR2 and provided to the focal region FR through the second optical system. The second light LI2 reflected from the focal region FR may pass through the second optical system again and reach the second camera 56. Figure 9 As shown in the figure, the second optical system may include a second reflecting unit 52 for changing the path of the second light LI2 emitted from the second light source unit 51 by reflection, a prism 53 for separating the second light LI2 into a first sub-light SLI1 and a second sub-light SLI2, and a second objective lens 54 for focusing the first sub-light SLI1 and the second sub-light SLI2 to a point in the focal region FR.

[0118] The prism 53 may separate the second light LI2 emitted from the second light source unit 51. In some embodiments, the prism 53 may be a refractive prism.

[0119] For example, due to the different refractive indices depending on the wavelength, the second light LI2 incident on the prism 53 may be separated by wavelength. The first sub-light SLI1 and the second sub-light SLI2 separated from each other may be focused by the second objective lens 54 and incident on different points F2 and F3 in the focal region FR, respectively. The first sub-light SLI1 and the second sub-light SLI2 incident on the different points F2 and F3, respectively, may pass through the second objective lens 54 and the prism 53 again and thus may be combined into one second light LI2. The combined second light LI2 may pass through the second barrel lens 55 and reach the second camera 56.

[0120] Darkfield microscope 40 can obtain a high-contrast image of the surface shape. Differential interference contrast microscope 50 can obtain a clearer image by utilizing the differential interference effect to highlight the surface shape. Since the substrate inspection apparatus according to the embodiment can include both darkfield microscope 40 and differential interference contrast microscope 50 as imaging devices, the substrate inspection apparatus can obtain a high-resolution, clear image of the crystal shape of polycrystalline silicon 36.

[0121] Figure 10 It is an icon Figure 7 FIG. 1 is a schematic plan view of a focal region FR of a polycrystalline silicon test substrate 30 .

[0122] Apart from Figure 7 In addition, refer to Figure 10 A plurality of focus regions FR may be defined in the polysilicon 36 of the polysilicon test substrate 30 . The focus region FR refers to an image capturing region of the imaging device and may be defined on the surface of the polysilicon 36 .

[0123] The accompanying drawings illustrate thirty-six focal regions FR. However, the present disclosure is not limited thereto. The number of focal regions FR can be adjusted in various ways, taking into account the reliability and cost of crystallization quality inspection. For example, increasing the number of focal regions FR to be inspected can improve the reliability of crystallization quality inspection, while decreasing the number of focal regions FR can reduce inspection time and costs.

[0124] Figure 11 is a schematic block diagram of a control unit 60 of a substrate inspection apparatus.

[0125] Apart from Figures 4 to 6 and Figure 10 In addition, refer to Figure 11 , the substrate inspection apparatus may include a control unit 60 that quantifies the degree of crystallization and abnormal crystallization in operations S130 and S230 , selects OPED and OACD in operation S140 , and determines the degree of crystallization and abnormal crystallization in operation S240 .

[0126] The control unit 60 may include a color table value extraction unit 61 , a statistical value calculation unit 63 , an optimal inspection value selection unit 65 , and a crystal quality determination unit 67 .

[0127] In quantifying the degree of crystallinity and abnormal crystallization (operation S130 and operation S230 ), the color table value extraction unit 61 may extract color table values, and the statistic calculation unit 63 may calculate a statistic using the extracted color table values.

[0128] like Figure 4 As shown in FIG. 1 , quantifying the degree of crystallization and abnormal crystallization (operation S130 and operation S230) may include: quantifying the degree of crystallization (operation S131 and operation S231); and quantifying abnormal crystallization (operation S132 and operation S232). Figures 12 to 27 Quantifying the degree of crystallinity (operation S131 and operation S231 ) and quantifying abnormal crystallization (operation S132 and operation S232 ) will be described.

[0129] In selecting OPED and OACD (operation S140), the optimal inspection value selection unit 65 may select OPED and OACD as optimal inspection values ​​by comparing calculated statistical values ​​of abnormal substrates and normal substrates among the polycrystalline silicon test substrates 30. For example, OPED and OACD may be selected by comparing statistical values ​​such as an average value, a maximum value, a minimum value, and / or a standard deviation of the normal substrates with statistical values ​​such as an average value, a maximum value, a minimum value, and / or a standard deviation of the abnormal substrates.

[0130] In determining the crystallinity and abnormal crystallization (operation S240), the crystal quality determination unit 67 may determine the crystallinity and abnormal crystallization by comparing PED and ACD as inspection values ​​of the target substrate with OPED and OACD as optimal inspection values. Figures 28 to 32 Determining the degree of crystallinity and abnormal crystallization (operation S240 ) will be described.

[0131] Now refer to Figures 12 to 20 Quantifying the degree of crystallinity (operation S131 and operation S231 ) will be described.

[0132] Figure 12 It is shown in the figure Figure 5 Operation S131a and Figure 6 Schematic example diagram of a color table for extracting a color table value in operation S132a of FIG. Figure 13 3 is an enlarged schematic perspective view of some of the plurality of focus regions FR of the polysilicon test substrate 30 . Figure 14 and Figure 15 It is relatively Figure 5 Operation S131a and Figure 6A schematic graph of a crystallization energy range that affects the extracted color table value in operation S132a.

[0133] Apart from Figure 5 、 Figure 6 and Figure 11 In addition, refer to Figures 12 to 15 In quantifying the degree of crystallinity (operation S131 and operation S231), the color table value extraction unit 61 of the control unit 60 may extract the color table value ( Figure 5 Operation S131a and operation S231a).

[0134] A color table is a table that defines and displays colors. A color table may include information about variables used to define and display colors, as well as information about the values ​​of the variables. A color table value may include information about the variables and information about the values ​​of the variables.

[0135] In the substrate inspection method S1 according to the embodiment, the type of the color table can be determined by the type of the variable. For example, if the variables are the three primary colors of red, green, and blue (RGB), the color table can be an RGB color table. As another example, if the variables are two colors of black and white, the color table can be a grayscale color table. As another example, if the variables are brightness (Y) and chrominance components (Cb-Cr), the color table can be a YCbCr color table. As another example, if the variables are hue (H), saturation (S), and value (V), the color table can be an HSV color table.

[0136] Although Figure 12 The RGB color table is illustrated as an example, but the present disclosure is not limited thereto. For ease of description, the case of extracting a color table value using the RGB color table is described as an example below.

[0137] like Figure 13 , the surface of the polysilicon 36 may include a plurality of hillocks 36a rising to a first side in the third direction DR3. Each hillock 36a may have a predetermined or selected height h, and a distance d between adjacent hillocks 36a may be defined as a grain size.

[0138] The height h of each hillock 36a and the distance d between adjacent hillocks 36a may be factors affecting the color table value extracted in extracting color table values ​​(operations S131a and S231a). That is, extracting color table values ​​(operations S131a and S231a) may be based on the height h of each of the plurality of hillocks 36a of the captured focus area FR and the arrangement of the plurality of hillocks 36a.

[0139] like Figure 14 and Figure 15, if the PED is within a specific range, the color table value extraction (operation S131a and operation S231a) may be performed based on the height h of each of the plurality of hillocks 36a. If the PED is outside the specific range, the color table value extraction (operation S131a and operation S231a) may be performed based on the arrangement of the plurality of hillocks 36a.

[0140] For example, if PED is within the range of α to β, the color table value extraction (operation S131a and operation S231a) may be performed based on the height h of each of the plurality of hillocks 36a. If PED is within the range of less than α or greater than β, the color table value extraction (operation S131a and operation S231a) may be performed based on the arrangement of the plurality of hillocks 36a.

[0141] Now refer to Figures 16 to 19 A mechanism for performing extraction of a color table value based on the height h of each of the plurality of hillocks 36 a is described in the case where the distance d between adjacent hillocks 36 a is constant.

[0142] Figure 16 Schematic diagram illustrating how the incident angle of light changes depending on the height of a hillock of a polysilicon crystal.

[0143] Figure 17 The diagram is from Figure 16 Schematic diagram of constructive interference of the first reflected beam reflected by the first hillock 36aa. Figure 18 The diagram is from Figure 16 Schematic diagram of constructive interference of the second reflected beam reflected by the second hillock 36ab. Figure 19 is a schematic graph illustrating the variation of the constructive interference wavelength with respect to the diffraction angle.

[0144] refer to Figures 16 to 19 , the hillock 36a may include a first hillock 36aa having a first height h1 and a second hillock 36ab having a second height h2 smaller than the first height h1.

[0145] Even if the beam L is incident on the first hillock 36aa and the second hillock 36ab at the same angle, the incident angle θ1 of the beam L at the contact surface with the first hillock 36aa and the incident angle θ2 of the beam L at the contact surface with the second hillock 36ab may be different. For example, the incident angle θ1 of the first hillock 36aa having the first height h1 may be smaller than the incident angle θ2 of the second hillock 36ab having the second height h2.

[0146] The beam L may be reflected from the contact surfaces with the first hillock 36aa and the second hillock 36ab to become a reflected beam LR1 and a reflected beam LR2. The reflection angle θ3 of the reflected beam LR1 and the reflection angle θ4 of the reflected beam LR2 may be equal to the incident angle θ1 and the incident angle θ2, respectively. CL1 represents a normal to the first hillock 36aa, and CL2 represents a normal to the second hillock 36ab.

[0147] The amplitude of beam L is Figure 17 and Figure 18 is a1, and the wavelength of the beam L is Figure 17 is λ1 in Figure 18 where λ is 2. Figure 17 shows a case where the focal region FR of the polysilicon 36 includes a first hillock 36aa, and Figure 18 A case is shown in which the focal region FR of the polysilicon 36 includes the second hillock 36 ab .

[0148] like Figure 17 As shown in FIG, in the adjacent first hillock 36aa, the constructive interference of the beam L having an amplitude of a1 and a reflection angle of θ3 is satisfied (amplitude is 2 a1 ) condition can be λ1.

[0149] like Figure 18 In the figure, the constructive interference of the beam L having an amplitude of a1 and a reflection angle of θ4 is satisfied in the adjacent second hillock 36ab (amplitude is 2 a1 ) condition can be λ2.

[0150] Here, the wavelength λ1 and the wavelength λ2 may satisfy Equation 1 according to the Bragg law in low energy electron diffraction (LEED).

[0151] n*λ=d*sinθ, Equation 1

[0152] Here, n is a natural number, λ is the wavelength of the beam L (λ1, λ2), d is the distance between adjacent hillocks 36a (d1, d2) (or the particle diameter), and θ is the reflection angle (θ3, θ4).

[0153] The wavelength λ2 of the beam L satisfying the constructive interference condition in the second hillock 36 ab may be greater than the wavelength λ1 of the beam L satisfying the constructive interference condition in the first hillock 36 aa .

[0154] Therefore, the color table of the reflection beam LR1 constructively interfering from the first hillock 36aa generally has a color in a low wavelength band, and the color table of the reflection beam LR2 constructively interfering from the second hillock 36ab generally has a color in a high wavelength band.

[0155] like Figure 19As shown in the figure, as the diffraction angle 2θ (θ1+θ3 or θ2+θ4 above, that is, 2θ1 or 2θ2) increases, the constructive interference wavelength can increase, and as the diffraction angle 2θ decreases, the constructive interference wavelength can decrease. That is, in the focal region FR with high crystallinity, because the height h of each hillock 36a is high, the incident angles θ1 and θ2 and the diffraction angle 2θ can be small. Therefore, light can have a color in the low wavelength band. On the other hand, in the focal region FR with low crystallinity, because the height h of each hillock 36a is low, the incident angles θ1 and θ2 and the diffraction angle 2θ can be large. Therefore, light can have a color in the high wavelength band.

[0156] In the drawings, a mechanism for extracting a color table value based on the arrangement of the plurality of hillocks 36a or the distance d between adjacent hillocks 36a is not shown when the height h of each of the plurality of hillocks 36a is constant. However, a person skilled in the art will understand the relationship between the distance d between adjacent hillocks 36a and the wavelength band of constructive interference based on Equation 1.

[0157] For example, if the distance d is large, the wavelength of constructive interference can be increased. Therefore, the light can have a color in the high wavelength band. If the distance d is small, the wavelength of constructive interference can be reduced. Therefore, the light can have a color in the low wavelength band.

[0158] Figure 20 It is a schematic graph showing changes in the crystallinity value with respect to the intensity of crystallization energy.

[0159] Apart from Figures 2 to 5 and Figure 13 In addition, refer to Figure 20 In quantifying the crystallinity (operation S131 and operation S231), the statistical value calculation unit 63 of the control unit 60 may calculate the statistical value ( Figure 5 Operation S131b and operation S231b).

[0160] For example, in calculating the statistical values ​​of the color table values ​​of all pixels in the focus region (operation S131b and operation S231b), the statistical value calculation unit 63 of the control unit 60 may calculate statistical values ​​such as an average value, a maximum value, a minimum value, a standard deviation, etc. of the R value, the G value, and the B value extracted from all pixels in the focus region FR.

[0161] Figure 20 The horizontal axis of the graph shown in FIG represents PED. Figure 20 The vertical axis of the graph illustrated in represents a value obtained by normalizing the statistical value of the color table value detected in the hillock 36a crystallized by the corresponding PED to a scale of 0 to 1. For example, Figure 20The statistical value used in is the average value of the color table values ​​detected in the hillock 36a. The statistical value calculation unit 63 may also use various statistical values ​​such as a maximum value, a minimum value, and / or a standard deviation.

[0162] Figure 20 The vertical axis of the graph shown in represents R average data AVG_R which is the average value of R component values ​​of the RGB color table extracted from the hillock 36 a , B average data AVG_B which is the average value of B component values, and G average data AVG_G which is the average value of G component values.

[0163] In selecting the OPED and the OACD (operation S140 ), the optimal inspection value selection unit 65 of the control unit 60 may select the OPED by comparing the statistical value of the abnormal substrate and the statistical value of the normal substrate.

[0164] In manufacturing a polycrystalline silicon target substrate (operation S210) in determining the crystallinity and abnormal crystallization of the target substrate (operation S200), a polycrystalline silicon target substrate 36' (see Figure 28 ).

[0165] The imaging device can capture the manufactured polysilicon target substrate 36' (see Figure 28 ) of the focus area (operation S220), and the color table value extraction unit 61 of the control unit 60 may quantify the polysilicon target substrate 36' (see Figure 28 )'s crystallinity and abnormal crystallization (operation S230).

[0166] In determining the crystallinity and abnormal crystallization (operation S240 ), the crystal quality determination unit 67 of the control unit 60 may determine the crystallinity of the target substrate by comparing the statistical value of the target substrate with the statistical value corresponding to the OPED.

[0167] Now refer to Figures 21 to 27 The description of quantifying abnormal crystallization (operation S132 and operation S232) will be omitted or briefly given for reference. Figures 12 to 20 The contents described in the quantification of the degree of crystallinity (operation S131 and operation S231) are the same as those described above, and the differences will be described.

[0168] Figure 21 and Figure 22 It is shown in the figure Figure 6 Schematic diagram of extraction of line integral data in operation S132b.

[0169] Apart from Figure 5 、 Figure 6 and Figure 11 In addition, refer to Figure 21 and Figure 22 In quantifying abnormal crystallization (operation S132 and operation S232), the color table value extraction unit 61 of the control unit 60 may extract the color table value ( Figure 5 Operation S132a and operation S232a).

[0170] Extracting color table values ​​(operation S132a and operation S232a) in quantifying abnormal crystallization (operation S132 and operation S232) may be performed in substantially the same manner as extracting color table values ​​(operation S131a and operation S231a) in quantifying the degree of crystallinity (operation S131 and operation S231), and thus a description thereof will be omitted.

[0171] A method of calculating a statistical value in quantifying abnormal crystallization (operation S132 and operation S232 ) may be different from a method of calculating a statistical value in quantifying a degree of crystallization (operation S131 and operation S231 ).

[0172] For example, as referenced above Figure 20 As described above, in quantifying the degree of crystallinity (operation S131 and operation S231), the average value of the color table values ​​extracted from all pixels in the focus region FR may be calculated. In other embodiments, statistical values ​​such as the maximum value, minimum value, standard deviation, etc. of the color table values ​​extracted from all pixels may be calculated. That is, in quantifying the degree of crystallinity (operation S131 and operation S231), the statistical values ​​of the color table values ​​extracted from the entire area of ​​the focus region FR may be calculated. In quantifying the degree of crystallinity (operation S131 and operation S231), the statistical values ​​of the color table values ​​extracted from two dimensions (plane dimensions) may be calculated.

[0173] On the other hand, in quantifying abnormal crystallization (operation S132 and operation S232), line integral data of color table values ​​of pixels located on the same line in the focal region FR may be extracted, two or more trend lines of the line integral data may be extracted, and statistical values ​​of the two or more trend lines may be calculated. That is, in quantifying abnormal crystallization (operation S132 and operation S232), statistical values ​​of trend lines of line integral data extracted from the same line rather than from the entire area of ​​the focal region FR may be calculated. In quantifying abnormal crystallization (operation S132 and operation S232), statistical values ​​of trend lines of line integral data extracted from one dimension (line dimension) may be calculated.

[0174] First, in extracting line integral data for each angle in the focal region (operation S132b and operation S232b), the statistical value calculation unit 63 of the control unit 60 may extract the line integral data.

[0175] The line integral data may include data obtained by adding color table values ​​of pixels at the same position (e.g., the same row or the same column) in multiple extended lines, where the multiple extended lines extend at the same angle in the focal region FR and are arranged side by side with each other in directions different from the angle.

[0176] For example, Figure 21 1 is an example diagram illustrating focus areas FR each including 640 pixels in the longitudinal direction and 480 pixels in the transverse direction. The first focus area FR1 may include virtual extension lines g01, g02, ..., g0N tilted by 0 degrees relative to the longitudinal direction. The second focus area FR2 may include virtual extension lines g51, g52, ..., g5N tilted by 5 degrees relative to the longitudinal direction. The third focus area FR3 may include virtual extension lines g01, g02, ..., g0N tilted by 10 degrees relative to the longitudinal direction. 10 1. g 10 2. ..., g 10 N. The fourth focal region FR4 may include a virtual extension line g inclined by 110 degrees relative to the longitudinal direction. 110 1. g 110 2. ..., g 110 N, where N is a positive integer.

[0177] Although Figure 21 The first to fourth focus regions FR1 to FR4 show a plurality of virtual extension lines inclined at different angles, but they are merely captured images of the same focus region FR.

[0178] The first focus region FR1 may be composed of a plurality of virtual extension lines g01, g02, ..., g0N, and each of the plurality of virtual extension lines g01, g02, ..., g0N may overlap with a plurality of pixels. For example, the first focus region FR1 may be composed of approximately 480 virtual extension lines g01, g02, ..., g0N, and each of the plurality of virtual extension lines g01, g02, ..., g0N may overlap with approximately 640 pixels.

[0179] As in the second focus region FR2, the third focus region FR3, and the fourth focus region FR4, the number of extension lines included in the focus region FR and the number of pixels overlapping each extension line may vary according to the tilt angle relative to the longitudinal direction. For ease of description, the following description will focus on the first focus region FR1.

[0180] The statistic calculation unit 63 may perform line integration on color table values ​​included in virtual extension lines g01 , g02 , . . . , g0N extending at an angle of 0 degrees in the first focus region FR1 .

[0181] For example, the virtual extension line (i.e., the first extension line) g01 of the first focus region FR1 may include color table value data for each of the 640 pixels located in the first column, the virtual extension line (i.e., the second extension line) g02 of the first focus region FR1 may include color table value data for each of the 640 pixels located in the second column, and the virtual extension line (i.e., the Nth extension line) g0N of the first focus region FR1 may include color table value data for each of the 640 pixels located in the Nth column.

[0182] The statistical value calculation unit 63 may add all color table value data of pixels located in the same row of the first extension line g01 to the Nth extension line g0N of the first focus region FR1. Therefore, the first extension line g01 to the Nth extension line g0N may be converted into the first line integral data g0.

[0183] The first-line integrated data g0 may include first to M-th row data. The first-row data may be obtained by summing the color table values ​​of the 480 pixels in the first row, and the M-th row data may be obtained by summing the color table values ​​of the 480 pixels in the M-th row, where M is a positive integer. In other words, the first-line integrated data g0 may include approximately 640 color table value data. Here, row data is merely an example name, and column data may also be named according to the angle of the extended line.

[0184] In this way, the statistical value calculation unit 63 can extract line integral data for each angle. For example, the second line integral data g5 can be extracted by performing line integration on an extended line tilted by 5 degrees in the second focal region FR2, and the third line integral data g 10 The fourth line integral data g can be extracted by performing line integration on an extended line tilted up to 10 degrees in the third focal region FR3, and the fourth line integral data g 110 It can be extracted by performing line integration on an extended line tilted up to 110 degrees in the fourth focal region FR4.

[0185] In some embodiments, line integral data may be extracted for each component of the color table value. For example, when an RGB color table is used in extracting the color table value (operations S132a and S232a), line integral data may be extracted for each RGB component and for each angle.

[0186] like Figure 22As shown in the figure, when the statistical value calculation unit 63 extracts the second line integral data g5 by performing line integration on the extended line inclined by 5 degrees in the second focal region FR2, the statistical value calculation unit 63 can also extract line integral data from each of the extended lines Rg51, Rg52, ..., Rg5N for the R component image, the extended lines Gg51, Gg52, ..., Gg5N for the G component image, and the extended lines Bg51, Bg52, ..., Bg5N for the B component image.

[0187] Figure 23 and Figure 24 It is shown in the figure Figure 6 A schematic graph of a trend line of the line integration data in operation S132c is provided.

[0188] Apart from Figure 21 and Figure 22 In addition, refer to Figure 23 and Figure 24 In extracting two or more trend lines of the line integral data for each angle (operation S132c and operation S232c), the statistical value calculation unit 63 of the control unit 60 may extract two or more trend lines of the line integral data for each angle. For example, the statistical value calculation unit 63 may extract the first line integral data g0 extracted from the first focal region FR1, the second line integral data g5 extracted from the second focal region FR2, and the third line integral data g1 extracted from the third focal region FR3. 10 and the fourth line integral data g extracted from the fourth focal region FR4 110 For the convenience of description, the following description will be based on the first line integral data g0.

[0189] Figure 23 and Figure 24 The trend lines of the line integral data of the abnormal substrate and the trend lines of the line integral data of the normal substrate are shown respectively.

[0190] The statistical value calculation unit 63 can extract a trend line based on the row data (or column data) included in the first line integral data g0. Figure 21 The first line integrated data g0 may include first to M-th row data. The first row data may be obtained by adding the color table values ​​of 480 pixels in the first row, and the M-th row data may be obtained by adding the color table values ​​of 480 pixels in the M-th row.

[0191] Figure 23 and Figure 24 The horizontal axis of represents the serial numbers of the first to M-th line data, and the vertical axis represents the value obtained by normalizing the color table value of each of the first to M-th line data to a scale of 0 to 1. Figure 23 and Figure 24 The graph shows a trend line extracted using approximately 462 rows of data from among the first row of data to the Mth row of data.

[0192] The trend line of the line integral data is a curve of a function generated by connecting the points corresponding to the average values ​​of every specific number of data in the first row of data and the Mth row of data. For example, Figure 23 The first curve GRP1 is a trend line extracted by connecting points corresponding to the average value of every 15 data from the first row of data to the Mth row of data of the abnormal substrate, and Figure 23 The second graph GRP2 is a trend line extracted by connecting points corresponding to the average value of every 50 data from the first row of data to the Mth row of data of the abnormal substrate. Figure 24 The third curve GRP3 is a trend line extracted by connecting points corresponding to the average value of every 15 data from the first row of data to the Mth row of data of the normal substrate, and Figure 24 The fourth graph GRP4 is a trend line extracted by connecting points corresponding to the average value of every 50 data from the first row of data to the Mth row of data of the normal substrate.

[0193] The number of rows of data selected for calculating the average value can be varied in various ways. Figure 27 Describe this.

[0194] Figure 25 It is shown in the figure Figure 6 A schematic curve diagram of the statistical values ​​of the trend line in operation S132d.

[0195] Figure 26 It is shown in the figure Figure 6 A schematic curve graph and a schematic table of statistical values ​​of the trend line in operation S132d are provided.

[0196] Apart from Figures 21 to 24 In addition, refer to Figure 25 and Figure 26 In calculating the statistical values ​​of the two or more trend lines (operation S132d and operation S232d), the statistical value calculation unit 63 of the control unit 60 may calculate the statistical values ​​using the two or more trend lines extracted from the line integral data for each angle.

[0197] Figure 25 The fifth curve GRP5 is shown as Figure 23 A curve of the square value of the deviation between the first curve GRP1 and the second curve GRP2 in FIG. 1 , and Figure 25 The sixth curve GRP6 is shown as Figure 24 A curve showing the square value of the deviation between the third curve GRP3 and the fourth curve GRP4 in FIG.

[0198] Figure 26 The seventh graph GRP7 is a graph showing the dispersion of the data of the fifth graph GRP5 , and the eighth graph GRP8 is a graph showing the dispersion of the data of the sixth graph GRP6 .

[0199] Various statistical values ​​can be used as the statistical values ​​calculated by the statistical value calculation unit 63 using the two or more trend lines extracted in extracting the two or more trend lines of the line integral data for each angle (operation 132c and operation 232c).

[0200] For example, Figure 25 and Figure 26 As shown in the figure, the statistical value calculation unit 63 may obtain square values ​​of deviations of two trend lines and use the average value or standard deviation of these values ​​as a statistical value.

[0201] As shown in the seventh curve GRP7, the average value of the squared deviations between the two trend lines for the abnormal substrate is 0.000146. On the other hand, for the normal substrate, the average value of the squared deviations between the two trend lines is 0.000053. Furthermore, the standard deviation of the squared deviations between the two trend lines for the abnormal substrate is 0.000160. On the other hand, for the normal substrate, the standard deviation of the squared deviations between the two trend lines is 0.000060.

[0202] The statistical value calculation unit 63 may also use various statistical values ​​such as a maximum value and a minimum value in addition to the average value or the standard deviation.

[0203] After selecting OPED and OACD (operation S140) (see Figure 2 ), the optimal inspection value selection unit 65 of the control unit 60 can select the OACD by comparing the statistical values ​​of the abnormal substrates with the statistical values ​​of the normal substrates. For example, by comparing the statistical values ​​of the abnormal substrates with the statistical values ​​of the normal substrates, the OACD can be selected within a range that includes the statistical values ​​of the normal substrates but does not include the statistical values ​​of the abnormal substrates.

[0204] In determining the degree of crystallization and abnormal crystallization (operation S240) in determining the degree of crystallization and abnormal crystallization of the target substrate (operation S200), the crystal quality determination unit 67 of the control unit 60 can determine the abnormal crystallization of the target substrate by comparing the statistical value of the target substrate and the statistical value corresponding to the OACD.

[0205] For example, if the statistical value of the target substrate exceeds the OACD or is less than the OACD, the target substrate may be determined to be an abnormal substrate.

[0206] Figure 27 It is shown in the figure Figure 6 Schematic table of a method of selecting a trend line of line integral data in operation S132c.

[0207] Apart from Figures 23 to 26 In addition, refer to Figure 27 , in the case of extracting a trend line from line integral data, the statistical value calculation unit 63 may adjust the number of row data selected for calculating the average value, thereby improving the OACD determination sensitivity.

[0208] Figure 27 The table illustrated in FIG. 1 shows a change in the ratio between the abnormal crystallization determination value ① and the normal crystallization determination value ② according to the point intervals of the first trend line and the second trend line. Figure 27 The abnormal crystallization determination value ① shown in the figure is obtained by multiplying the average of the squares of the deviations of the two trend lines extracted from the abnormal substrate by 10 6 The value obtained, and Figure 27 The normal crystallization determination value ② shown in the figure is obtained by multiplying the average of the squares of the deviations of the two trend lines extracted from the normal substrate by 10 6 The value obtained.

[0209] For example, if the average value of the row data is calculated at intervals of one point in the case of the first trend line, and the average value of the row data is calculated at intervals of 10 points in the case of the second trend line, the ratio of the normal crystallization determination value ② to the abnormal crystallization determination value ① may be approximately 27.9%. As another example, if the average value of the row data is calculated at intervals of one point in the case of the first trend line, and the average value of the row data is calculated at intervals of 30 points in the case of the second trend line, the ratio of the normal crystallization determination value ② to the abnormal crystallization determination value ① may be approximately 27.8%.

[0210] In this manner, the ratio of the normal crystallization determination value ② to the abnormal crystallization determination value ① can be changed by adjusting the number of points used to extract the first trend line and the number of points used to extract the second trend line.

[0211] The smaller the ratio of the normal crystallization determination value ② to the abnormal crystallization determination value ①, the better the difference between the abnormal crystallization determination value ① and the normal crystallization determination value ② appears. Therefore, the first and second trend lines can be extracted using the point interval where the ratio of the normal crystallization determination value ② to the abnormal crystallization determination value ① is minimized.

[0212] Figure 28 It is an icon Figure 3 FIG. 1 is a schematic perspective view of operation S210 . Figure 29 It is an icon Figure 28 FIG. 1 is a schematic plan view of a focal region FR of a polycrystalline silicon target substrate 30 ′.

[0213] Apart from Figure 3 and Figure 7 In addition, refer to Figure 28 and Figure 29 In manufacturing a polycrystalline silicon target substrate (operation S210), the polycrystalline silicon target substrate 30' may be manufactured using the OPED selected in the previous operation. The polycrystalline silicon target substrate 30' may be manufactured with Figure 7 The polysilicon test substrate 30 is different from the polysilicon test substrate 30 in at least one aspect: the polysilicon target substrate 30' is manufactured using OPED. Figure 28 The other components of the polysilicon target substrate 30' are Figure 7 The components of the polysilicon test substrate 30 are the same as those of the polysilicon test substrate 30, and thus redundant descriptions thereof will be omitted.

[0214] like Figure 29 As shown in FIG, the focal region FR may also be defined in the polysilicon 36' of the polysilicon target substrate 30'.

[0215] As in selecting the optimal inspection value using the test substrate (operation S100), in determining the crystallinity and abnormal crystallization of the target substrate (operation S200), capturing the focus region (operation S220) and quantifying the crystallinity and abnormal crystallization (operation S230) may be performed simultaneously. Capturing the focus region (operation S220) and quantifying the crystallinity and abnormal crystallization (operation S230) in determining the crystallinity and abnormal crystallization of the target substrate (operation S200) are the same as capturing the focus region (operation S120) and quantifying the crystallinity and abnormal crystallization (operation S130) in selecting the optimal inspection value using the test substrate (operation S100), and therefore, redundant description thereof will be omitted.

[0216] In determining the crystallization degree and abnormal crystallization (operation S240), the crystal quality determination unit 67 of the control unit 60 may determine the crystal quality of the polycrystalline silicon target substrate 30' such as the crystallization degree and abnormal crystallization by comparing the statistical values ​​of the polycrystalline silicon target substrate 30' with the previously selected OPED and OACD.

[0217] Figure 30 is Figure 3 FIG. 1 is a schematic graph of an image of the focus region FR captured in operation S220. Figure 31 is a schematic table showing crystallization energy, crystallization degree values, and crystallization pictures of various samples of the polycrystalline silicon target substrate 30 ′. Figure 32 Schematic diagram comparing a captured image of the focal region FR of an abnormally crystallized substrate and a captured image of the focal region FR of a normal substrate.

[0218] Apart from Figure 26 In addition, refer to Figures 30 to 32 , Figure 30 FIG. 4 shows a captured image of a focal region FR. Figure 30 As shown in , determination of the degree of crystallinity in the focal region FR is not easily accomplished with the naked eye.

[0219] On the other hand, Figure 31 As shown in the enlarged crystallization images of Samples #1 to #3, it can be seen that Sample #1 has a higher degree of crystallization than Sample #2, and Sample #2 has a higher degree of crystallization than Sample #3. Here, it can be seen that the ratio of the target substrate's statistical value to OPED is also higher for Sample #1 than for Sample #2, and higher for Sample #2 than for Sample #3. Therefore, it can be seen that Sample #1 has a lower crystallization energy than Sample #2, and that Sample #2 has a lower crystallization energy than Sample #3.

[0220] As described above, according to the substrate inspection method S1 according to the embodiment, the degree of crystallization and the crystallization energy for achieving the degree of crystallization may be objectively calculated by quantifying the degree of crystallization.

[0221] in addition, Figure 32 A captured image of a normal substrate OK in the first picture PCT1 on the left and a captured image of an abnormal substrate NG in the second picture PCT2 on the right are shown. Figure 32 As shown in , a normal substrate OK may not include the longitudinal moiré MUR, and an abnormal substrate NG may include the longitudinal moiré MUR. Figure 32 Normal substrate OK and abnormal substrate NG are used for Figure 26 The captured images of the normal substrate OK and the abnormal substrate NG are described.

[0222] like Figure 26 As shown in the figure, it can be seen that the statistical value (for example, the average value or the standard deviation) of the normal substrate OK is lower than the statistical value of the abnormal substrate NG.

[0223] As described above, according to the substrate inspection method S1 according to the embodiment, abnormal crystallization can be objectively calculated by quantifying the abnormal crystallization.

[0224] For example, since the annealing device 10 (see Figure 7 ) may cause abnormal crystallization such as longitudinal moire, transverse moire, and oblique moire. By using data obtained by objectively quantifying the misalignment of the optical system configuration using line integral data for each angle, it is possible to determine whether abnormal crystallization such as longitudinal moire, transverse moire, and oblique moire has occurred.

[0225] At the end of the detailed description, those skilled in the art will recognize that many changes and modifications can be made to the embodiments without departing substantially from the principles of the present disclosure. Therefore, the embodiments of the present disclosure disclosed are used only in a general and descriptive sense and not for the purpose of limitation.

Claims

1. A substrate inspection method, comprising: Use a test substrate to select the best inspection value; as well as determining at least one of a degree of crystallinity and abnormal crystallization of a target substrate using the optimal inspection value, Wherein, selecting the optimal check value includes: capturing a focal region located in at least a portion of the test substrate; quantifying at least one of the degree of crystallinity and the abnormal crystallization of the test substrate; and At least one of an optimum process energy density value and an optimum abnormal crystallization determination value is selected as the optimum inspection value.

2. The substrate inspection method according to claim 1, wherein: quantifying the at least one of the degree of crystallinity and the abnormal crystallization of the test substrate includes: quantifying the abnormal crystallization, and Quantifying the abnormal crystallization includes: extracting color table values; and Calculates statistics using the color table values.

3. The substrate inspection method according to claim 2, wherein: The color table used in extracting the color table value is at least one of an RGB color table, a grayscale color table, a YCbCr color table, and an HSV color table.

4. The substrate inspection method according to claim 2, wherein: Calculating the statistical value includes: extracting line integral data for each angle in the focal region; extracting two or more trend lines of the line integral data for each angle; and Statistical values ​​of the two or more trend lines are calculated.

5. The substrate inspection method according to claim 4, wherein: The line integral data includes data obtained by adding a plurality of color table values ​​of a plurality of pixels located at the same position in a plurality of extended lines, and The plurality of extension lines extend at the same angle in the focal region and are arranged side by side with each other in a direction different from the angle.

6. The substrate inspection method according to claim 5, wherein: The line integral data is extracted for each component of the color table value.

7. The substrate inspection method according to claim 5, wherein: Each of the two or more trend lines is a curved line of a function generated by connecting points corresponding to average values ​​of every n data included in the line integral data, where n is a natural number.

8. The substrate inspection method according to claim 7, wherein: The two or more trend lines include a first trend line and a second trend line, The first trend line is a curve of a function generated by connecting points corresponding to the average value of each x data. The second trend line is a curve of a function generated by connecting points corresponding to the average value of each y data, and x and y are natural numbers equal to or smaller than n, and are derived from a case where a ratio of a statistical value of normal substrates among test substrates to a statistical value of abnormal substrates among the test substrates is minimized.

9. The substrate inspection method according to claim 4, wherein: The statistical value is calculated using the square of the deviation between the two or more trend lines.

10. The substrate inspection method according to claim 9, wherein: The statistical value is one of an average value, a standard deviation, a maximum value, and a minimum value of the squares of the deviations between the two or more trend lines.

11. The substrate inspection method according to claim 1, wherein: Selecting the optimal check value further comprises: manufacturing the test substrate, Determining the at least one of the degree of crystallinity and the abnormal crystallization of the target substrate includes: manufacturing the target substrate, and The test substrate and the target substrate include polysilicon formed by a laser annealer.

12. The substrate inspection method according to claim 11, wherein: In manufacturing the target substrate, the crystallization energy of the laser annealer uses the optimal process energy density value.

13. The substrate inspection method according to claim 1, wherein: Determining the at least one of the degree of crystallinity and the abnormal crystallization of the target substrate includes: capturing a focal region located on at least a portion of the target substrate; and quantifying the at least one of the degree of crystallinity and the abnormal crystallization of the target substrate, Capturing the focal region of the target substrate is performed in the same manner as capturing the focal region of the test substrate, and Quantifying the at least one of the degree of crystallinity and the abnormal crystallization of the target substrate is performed in the same manner as quantifying the at least one of the degree of crystallinity and the abnormal crystallization of the test substrate.

14. A substrate inspection device comprising: an annealer, wherein the annealer crystallizes the amorphous silicon on the substrate into polycrystalline silicon; an imaging assembly that captures a focal region located in at least a portion of the polysilicon; as well as a controller that analyzes images provided by the imaging assembly, Wherein, the imaging component includes a dark field microscope and a differential interference contrast microscope.

15. The substrate inspection apparatus according to claim 14, wherein The dark field microscope includes a first light source, a first reflector, a guide, a first objective lens, a first tube lens and a first camera.

16. The substrate inspection apparatus according to claim 14, wherein The differential interference contrast microscope includes a second light source, a second reflector, a prism, a second objective lens, a second tube lens and a second camera.

17. The substrate inspection apparatus according to claim 16, wherein The prism includes a refractive prism, and the refractive prism separates light incident from the second light source into at least two beams.

18. The substrate inspection apparatus according to claim 17, wherein The at least two beams separated by the prism are reflected at different points in the focal region.

19. The substrate inspection apparatus according to claim 16, wherein The dark field microscope includes a first light source, a first reflector, a guide, a first objective lens, a first tube lens, and a first camera, and the first camera and the second camera are configured as one camera.

20. The substrate inspection apparatus according to claim 19, wherein The first and second light sources, the first and second reflectors, the first and second objective lenses, and the first and second tubular lenses are respectively configured as a light source, a reflector, an objective lens, and a tubular lens.

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