Pattern inspection apparatus and pattern inspection method

Abstract

There is provided a pattern inspection method including obtaining an image of a substrate on which a pattern is formed, extracting a contour based on the image, detecting a position of a target pattern based on the contour, generating pattern inspection data by performing curve fitting on the detected position of the target pattern, and analyzing the pattern based on the pattern inspection data, the curve fitting is performed by using at least one of a Sigmoid function, a hyperbolic tangent function, and a Fermi-Dirac function, and wherein the pattern inspection data includes a width in the first direction, a height in the second direction, and a pattern slope of the target pattern.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is based on Korean Patent Application No. 10-2023-0073740 filed in the Korean Intellectual Property Office on June 8, 2023, and claims priority under 35 U.S.C. §119, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] Various example embodiments of the present disclosure relate to a pattern inspecting apparatus and / or a pattern inspecting method. Background Art

[0004] Various example embodiments relate to a pattern inspecting apparatus and / or a pattern inspecting method.

[0005] In the manufacture of semiconductor devices, it is necessary or desirable to accurately measure fine patterns formed by photolithography processes, etching processes (such as wet etching and / or dry etching processes), etc. In order to identify whether fine patterns have been formed with precise dimensions before and after the pattern forming process, electrical characteristic inspection and / or critical dimensions (CD) measurement are performed. For example, a scanning electron microscope (SEM) is used as equipment for measuring CD. However, the pattern measurement accuracy of highly fine patterns formed by using extreme ultraviolet (EUV) patterning technology is low. Therefore, research on this issue has been conducted. Summary of the invention

[0006] Various example embodiments may provide a pattern inspecting apparatus and / or a pattern inspecting method for improving inspection accuracy of a pattern.

[0007] Problems to be solved or improved by the technical idea are not limited to those mentioned above, and other problems can be clearly understood by those of ordinary skill in the art from the following description.

[0008] According to some example embodiments, there is provided a pattern inspection method, comprising obtaining an image of a substrate on which a pattern is formed, extracting a contour based on the image, detecting a position of a target pattern based on the contour, generating pattern inspection data by performing curve fitting on the detected position of the target pattern, and analyzing the pattern based on the pattern inspection data. The curve fitting is performed by using at least one of a Sigmoid function, a hyperbolic tangent function, and a Fermi-Dirac function. The pattern inspection data includes a width in a first direction, a height in a second direction, and a pattern slope of the target pattern.

[0009] Alternatively or additionally, according to some example embodiments, there is provided a pattern inspection method, comprising: obtaining an image of a substrate having a pattern formed thereon, extracting a plurality of contours based on the image, obtaining a plurality of pattern coordinate values ​​of the plurality of contours, extracting a plurality of target coordinate values ​​of a target pattern from the plurality of pattern coordinate values ​​based on a profile of the plurality of pattern coordinate values, generating pattern inspection data by performing curve fitting on the detected plurality of target coordinate values, and analyzing consistency of an optical proximity correction (OPC) pattern based on the pattern inspection data. The pattern inspection data includes a width in a first direction, a height in a second direction, and a pattern slope of the target pattern.

[0010] Alternatively or additionally, according to some example embodiments, there is provided a pattern inspection method, comprising: obtaining an image of a substrate on which a pattern is formed, extracting a plurality of contours based on the image, obtaining a plurality of pattern coordinate values ​​of the plurality of contours, extracting a plurality of target coordinate values ​​of a target pattern in the plurality of pattern coordinate values ​​based on the contours of the plurality of pattern coordinate values, generating pattern inspection data by performing curve fitting on the detected plurality of target coordinate values, and analyzing the consistency of an optical proximity correction (OPC) pattern based on the pattern inspection data. The plurality of target coordinate values ​​of the target pattern represent a transition region of a nanosheet. The pattern inspection data comprises a width in a first direction, a height in a second direction, and a pattern slope of the target pattern. The generation of the pattern inspection data comprises: generating the pattern inspection data by using a curve fitting method for the plurality of target coordinate values, the curve fitting method using at least one of a sigmoid function, a hyperbolic tangent function, and a Fermi-Dirac function. The extraction of the plurality of target coordinate values ​​comprises extracting the pattern coordinate values ​​of a region where the slope changes in the contour as the target coordinate values. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Various example embodiments will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0012] Figure 1 is a block diagram of a pattern inspection apparatus according to an embodiment;

[0013] Figure 2A and 2B is a cross-sectional view of an optical proximity correction (OPC) pattern of a transition region of a nanosheet according to some example embodiments;

[0014] Figure 3 is a block diagram of a pattern inspection method according to some example embodiments;

[0015] Figure 4 is a cross-sectional view of an inspection object of a pattern inspecting apparatus and a pattern inspecting method according to some example embodiments;

[0016] Figure 5 is a cross-sectional view of a scanning electron microscope (SEM) image of a pattern in a transition region including nanosheets according to some example embodiments;

[0017] Figure 6 is a graph illustrating a plurality of profiles according to some example embodiments; and

[0018] Figure 7 is a graph illustrating a pattern inspecting method according to some example embodiments. DETAILED DESCRIPTION

[0019] Hereinafter, some example embodiments will be described in detail with reference to the accompanying drawings. In the accompanying drawings, the same reference numerals are used for the same constituent elements, and repeated descriptions thereof are omitted.

[0020] Figure 1 is a block diagram of a pattern inspecting apparatus 10 according to some example embodiments.

[0021] refer to Figure 1 , the pattern inspection device 10 may include a measuring device 100 , a data processing unit 200 , and a data analysis unit 300 .

[0022] The measuring device 100 may include a stage, an electron supply unit, and an electron detection unit. The semiconductor substrate may be placed on the stage. The measuring device 100 may inspect a pattern formed on the substrate. In some example embodiments, the measuring device 100 may include any one or more of an optical microscope (e.g., a confocal microscope), a SEM, a transmission electron microscope (TEM), a focused ion beam (FIB) inspection device, and an electron beam inspection device. The measuring device 100 may measure a semiconductor substrate in a clean room; however, example embodiments are not limited thereto.

[0023] In some example embodiments, the measuring device 100 may irradiate an electron beam onto the substrate via an electron supply unit. In addition, the measuring device 100 may detect secondary electrons emitted by the substrate by using an electron detection unit. The measuring device 100 may generate data of the pattern by scanning the intensity of secondary electrons emitted by the pattern on the substrate. The data about the pattern may include images such as SEM images.

[0024] The data processing unit 200 may include a contour extraction unit 210, a coordinate acquisition unit 220, a data interpolation unit 230, and a data extraction unit 240. The data processing unit 200 may be connected to an electronic supply unit and an electronic detection unit of the measuring device 100. The data processing unit 200 may receive data from the measuring device 100, and store and / or process the data. In some example embodiments, the data processing unit 200 may receive a SEM image of a pattern of a substrate from the measuring device 100.

[0025] The data processing unit 200 may include, for example, at least one processor. For example, the data processing unit 200 may control at least one other component (e.g., an electronic supply unit or an electronic detection unit) by executing software, and perform various data processing or data calculations. According to some example embodiments, as at least a part of the data processing or data calculation, the data processing unit 200 may store data received from other components (e.g., an electronic supply unit and / or an electronic detection unit) in a volatile memory, process commands or data stored in the volatile memory, and store the result data in a non-volatile memory.

[0026] According to some example embodiments, the data processing unit 200 may include a main processor (e.g., a central processing unit or an application processor) or an auxiliary processor (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or in conjunction with the main processor.

[0027] According to some example embodiments, when the data processing unit 200 includes a main processor and an auxiliary processor, the auxiliary processor may be configured to use lower power than the main processor, and / or may be set to be dedicated to a specified function. The auxiliary processor may be implemented separately from the main processor or as part of the main processor. Example embodiments are not limited thereto.

[0028] The auxiliary processor can, for example, replace the main processor when the main processor is in an inactive (e.g., sleep) state, or control at least a portion of the functions or states associated with at least one of the components of the measuring device 100 (e.g., the electronic supply unit and / or the electronic detection unit) together with the main processor when the main processor is in an active (e.g., application processing) state.

[0029] According to some example embodiments, an auxiliary processor (e.g., an image signal processor or a communication processor) may be implemented as part of other components (e.g., a communication module) that are functionally related thereto. According to some example embodiments, an auxiliary processor (e.g., a neural network processor) may include a hardware structure dedicated to processing an artificial intelligence model. The artificial intelligence model may be generated by using machine learning. Machine learning may be performed in the data processing unit 200 itself that processes the artificial intelligence model, and may also be performed by using a separate server.

[0030] The contour extraction unit 210 may receive an image, such as a SEM image of a pattern, from the measurement apparatus 100. The contour extraction unit 210 may extract a contour from the image of the pattern.

[0031] The coordinate acquisition unit 220 can detect the position of the target pattern. The target pattern may correspond to a transition region of a nanosheet in a semiconductor device to be described below. The coordinate acquisition unit 220 can obtain a plurality of pattern coordinate values ​​of a plurality of contours. In addition, the coordinate acquisition unit 220 can extract a plurality of pattern coordinate values ​​of the target pattern based on the contour or shape of the plurality of pattern coordinate values. In some embodiments, the coordinate acquisition unit 220 can extract a pattern coordinate value in an area where the contour or shape slope of the plurality of pattern coordinate values ​​changes as a target coordinate value. In this case, each of the pattern coordinate value and the target coordinate value may include an X-axis coordinate value and a Y-axis coordinate value.

[0032] The data interpolation unit 230 may generate interpolation data based on the target coordinate values. In some embodiments, the first data may be generated by curve fitting or interpolating multiple target coordinate values ​​detected by the coordinate acquisition unit 220. In addition, the data interpolation unit 230 may generate the second data by differentiating the first data.

[0033] In some embodiments, the data interpolation unit 230 can extract the third data by filtering the second data. The data interpolation unit 230 can match the second data with the sigma value of the Gaussian filter, and extract the Gaussian distribution with the matching sigma value as the third data. For example, the data interpolation unit 230 can extract the Gaussian distribution with three sigma values ​​as the third data. In this way, outliers can be removed, and the degree of matching of the pattern can be predicted by using the third data with higher reliability. In addition, the data interpolation unit 230 can normalize multiple target coordinate values. In some embodiments, the data interpolation unit 230 can normalize multiple target coordinate values ​​based on the coordinates of (0.0).

[0034] The data extraction unit 240 may extract and / or generate pattern inspection data based on the first to third pieces of data. The pattern inspection data may include a width of the target pattern in a horizontal direction or a first direction, a height in a second direction or a vertical direction perpendicular to the first direction, and a pattern slope. In some example embodiments, the data extraction unit 240 may calculate the height of the pattern inspection data in the vertical direction based on the difference between the maximum value and the minimum value of the Y-axis coordinate values ​​of the plurality of target coordinate values.

[0035] In some embodiments, the data extraction unit 240 can calculate and / or obtain the width of the pattern inspection data in the horizontal direction based on the third data. In addition, the data extraction unit 240 can calculate and / or obtain the pattern slope of the pattern inspection data by dividing the height in the vertical direction by the width in the horizontal direction.

[0036] In addition, the pattern inspection device may include a database (not shown). The database may transmit data to and / or receive data from the measuring device 100, the data processing unit 200, and the data analysis unit 300. The database may have a general data structure implemented in a storage space (hard disk or memory) of a computer system by using a management program.

[0037] The database may have a data storage type that can freely search, extract, delete, edit and / or add data. The database may be implemented using a relational database management system (RDBMS) (such as one or more of Oracle, Infomix, Sybase, and DB2), an object-oriented database management system (OODBMS) (such as Gemston, Orion, and O2), an XML local database (such as Excelon, Tamino, and Sekaiju), and may include appropriate fields and / or elements to implement its own functions. Example embodiments are not limited thereto.

[0038] In some example embodiments, the database may receive data from the measurement apparatus 100 and store the received data. The database may store a pattern layout on a substrate received from the measurement apparatus 100, an SEM image, a first piece of data, a second piece of data, a third piece of data, and pattern inspection data to be described.

[0039] The data analysis unit 300 may analyze the consistency of an optical proximity correction (OPC) pattern with respect to the pattern based on the pattern inspection data. In some example embodiments, the data analysis unit 300 may determine the consistency of the OPC pattern of the pattern based on the width in the horizontal direction, the height in the vertical direction, and the pattern slope of the pattern inspection data. The OPC pattern may represent a recessed pattern of a transition region of a nanosheet.

[0040] refer to Figure 1 Any or all of the elements described may be used with reference to Figure 1 any or all other elements described; for example, any element may communicate with Figure 1 Any or all other elements in the system perform unidirectional and / or bidirectional and / or broadcast communications to transmit or exchange information, such as but not limited to data and / or commands, in a serial and / or parallel manner via a wireless and / or wired bus (not shown).

[0041] Figure 2A and 2B 1 is a cross-sectional view of an OPC pattern of a transition region of a nanosheet 17 according to various example embodiments.

[0042] refer to Figure 2A and 2B , the pattern on the substrate under inspection may include a plurality of gate lines 11, nanosheets 17, and source / drain regions (not shown). Figure 2A and 2B A plan view of a semiconductor device is shown; example embodiments are not limited thereto.

[0043] The substrate may include a semiconductor, such as Si and / or Ge, and / or a compound semiconductor, such as one or more of SiGe, SiC, GaAs, InAs, InGaAs, and InP. The terms "SiGe", "SiC", "GaAs", "InAs", "InGaAs", and "InP" used may refer to materials including the elements included in each term, but may not be referred to as chemical formulas representing stoichiometric relationships. The substrate may include a conductive region (not shown), such as a well doped with an impurity and / or a structure doped with an impurity.

[0044] Each of the plurality of gate lines 11 may include one or more of a metal, a metal nitride, a metal carbide, or a combination thereof. The metal may include one or more of Ti, W, Ru, Nb, Mo, Hf, Ni, Co, Pt, Yb, Tb, Dy, Er, and Pd. The metal nitride may include one or more of TiN and TaN. The metal carbide may include TiAlC.

[0045] The nanosheet 17 may include a nanosheet stack including a plurality of nanosheets. A gate dielectric layer (not shown) may be disposed between the nanosheet 17 and the plurality of gate lines 11. The gate dielectric layer may include a portion covering surfaces of the plurality of nanosheets and a portion covering side surfaces thereof.

[0046] The nanosheet 17 may include a semiconductor layer including the same element. In some example embodiments, the nanosheet 17 may include a Si layer. The nanosheet 17 may be doped with a dopant of the same conductivity type as the source / drain region. In some embodiments, the nanosheet 17 may include a Si layer doped with an n-type dopant in a first region. In addition, the nanosheet 17 may include a Si layer doped with a p-type dopant in a second region different from the first region.

[0047] In some example embodiments, the first region may include an N-channel (N) metal oxide semiconductor (MOS) (NMOS) transistor region, and the second region may include a P-channel (P) MOS (PMOS) transistor region; however, example embodiments are not limited thereto. In this case, the source / drain region in the first region may include a Si layer doped with an n-type dopant or a SiC layer doped with an n-type dopant. In addition, the source / drain region in the second region may include a SiGe layer doped with a p-type dopant. The n-type dopant may include one or more of phosphorus (P), arsenic (As), or antimony (Sb). The p-type dopant may include boron (B) and / or gallium (Ga). In some example embodiments, either or both of the first region and the second region may be lightly counter-doped, for example, doped with a dopant of a conductivity type having a concentration much lower than that of the main dopant; example embodiments are not limited thereto.

[0048] refer to Figure 2A , the OPC pattern 13a for forming the nanosheet 17 may include a recessed region 15 for forming a transition region of the nanosheet 17. Figure 2B , the OPC pattern 13b for forming the nanosheet 17 may include a region offset from the target point by a first distance SX, a transition region for forming the nanosheet 17. The pattern inspection apparatus 10 and / or pattern inspection method of example embodiments may inspect the SEM image to determine the consistency of the OPC patterns 13a and 13b.

[0049] Figure 3 is a block diagram of a pattern inspecting method according to various example embodiments. Figure 4 is a cross-sectional view of an inspection object of a pattern inspecting apparatus and a pattern inspecting method according to some example embodiments. Figure 5 is a cross-sectional view of a SEM image of a pattern in a transition region including nanosheets, according to some example embodiments.

[0050] refer to Figure 3 , Figure 4 and Figure 5 , the pattern inspection method of example embodiments may first obtain a SEM image of a substrate in which a pattern has been formed.

[0051] In this case, the measuring device 100 may obtain a plurality of SEM images of the pattern of the substrate W (S110). The data processing unit 200 may receive the SEM image from the measuring device 100. The measuring device 100 may obtain a plurality of SEM images of a plurality of inspection regions A of the pattern including the transition region of the nanosheet in the pattern C on the substrate W. The plurality of inspection regions A may include a plurality of exposure (shot) regions different from each other, and the measuring device 100 may obtain a plurality of SEM images of the pattern of the transition region of the nanosheet in the different exposure regions. By quantitatively analyzing and processing the data of the pattern in the transition region of the nanosheet in the different exposure regions, the pattern inspection method and the pattern inspection device of the example embodiment may analyze the consistency of the OPC and simultaneously identify the trend of the pattern defect, and thus the reliability of the semiconductor device may be improved.

[0052] Figure 4 A wafer W having a plurality of patterns C corresponding to chips or exposures is shown, which also has inspection areas A. The number and / or arrangement of the inspection areas A and the number, arrangement and size of the patterns C are not limited to Figure 4 In addition, the diameter of the wafer W may be 200 mm, or 300 mm, or 450 mm; example embodiments are not limited thereto.

[0053] refer to Figure 4 and Figure 5 , when viewed in a plan view, the nanosheet 401 in the inspection area A may have a height corresponding to the vertical level heights of the first level LV1 and the second level LV2. In addition, the nanosheet 401 in the inspection area A may include a transition region CR in which the height (e.g., horizontal height) changes from the first level LV1 to the second level LV2. In this case, the transition region CR may represent a circular area from a first point SP, where the height starts to change from the first level LV1, to a second point EP, where the height becomes the same as the second level LV2. Analysis of the transition region CR may be important in determining the consistency of the OPC pattern.

[0054] Figure 6 is a graph showing multiple profile MCSs according to some example embodiments. Figures 3 to 5 Give a description and briefly give or omit references Figures 3 to 5 The repeated description given. Figure 6 The X-axis can indicate Figure 5The horizontal distance of the nanosheet 401 in FIG. Figure 5 In addition, the vertical distance of the nanosheet 401 in FIG. Figure 5 The profile of the nanosheet 401 in the horizontal direction is reversed left and right.

[0055] refer to Figure 1 and 3 6, the pattern inspection method of the example embodiment may obtain a SEM image, and then extract a plurality of contour MCS based on the SEM image (S120). The extraction of the plurality of contour MCS with respect to the SEM image may be performed by the contour extraction unit 210 of the data processing unit 200. The plurality of contour MCS may include the contour of each of the plurality of nanosheets 401 of the substrate W.

[0056] Figure 7 is a graph illustrating a pattern inspection method according to some example embodiments. Figure 7 The X-axis can indicate Figure 5 The horizontal distance of the nanosheet 401 in FIG. Figure 5 In addition, the vertical distance of the nanosheet 401 in FIG. Figure 5 The profile of the nanosheet 401 in the horizontal direction is reversed left and right.

[0057] refer to Figure 1 , Figure 3 and Figure 7 , the pattern inspection method of various exemplary embodiments, after extracting multiple contour MCSs, detects the positions of multiple target patterns based on the multiple contour MCSs (S130). The detection of the positions of the multiple target patterns may be performed by the coordinate acquisition unit 220. The positions of the multiple target patterns and their pattern coordinate values ​​to be described below may include X-axis coordinate values ​​and Y-axis coordinate values. The coordinate acquisition unit 220 may first obtain multiple pattern coordinate values ​​(stepping points) of the multiple contour MCSs to detect the positions of the target patterns based on the multiple contour MCSs. The multiple pattern coordinate values ​​(stepping points) may be represented at uniform intervals along the multiple contour MCSs. The multiple pattern coordinate values ​​(stepping points) may be obtained at an interval in the range of 1nm to 10nm.

[0058] In some example embodiments, the coordinate acquisition unit 220 may extract a plurality of pattern coordinate values ​​of the target pattern based on the outer shape or shape of the plurality of pattern coordinate values. In some example embodiments, the coordinate acquisition unit 220 may extract pattern coordinate values ​​in an area where the outer shape or shape slope of the plurality of pattern coordinate values ​​(step points) changes as the plurality of target coordinate values. The central area separated by the dotted line in the vertical axis on the graph may correspond to an area where the outer shape or shape slope of the plurality of pattern coordinate values ​​(step points) changes.

[0059] Next, pattern inspection data may be generated by curve fitting the position of the detected target pattern (S140). In this case, the pattern inspection data may include the width of the plurality of target patterns in the horizontal direction, the height thereof in the vertical direction, and the pattern slope thereof. Curve fitting may be performed by the data interpolation unit 230. The data interpolation unit 230 may generate interpolation data by performing curve fitting on the target coordinate values.

[0060] In some example embodiments, the data interpolation unit 230 may normalize the plurality of target coordinate values ​​before performing the curve fitting. In some example embodiments, the data interpolation unit 230 may normalize the plurality of target coordinate values ​​based on a coordinate of (0.0).

[0061] In this case, the interpolation data may represent data values ​​for which curve fitting has been performed with respect to the target coordinate value. The interpolation data may correspond to a thick solid line represented as a linear fit on a curve graph. The data interpolation unit 230 may perform curve fitting by using at least one of a Sigmoid function, a hyperbolic tangent function, and a Fermi-Dirac function.

[0062] In some example embodiments, the data interpolation unit 230 may generate the first piece of data by curve fitting or interpolating the plurality of target coordinate values ​​detected by the coordinate acquisition unit 220. The first piece of data may be represented as a thick solid line (linear fitting) on ​​a curve graph.

[0063] Alternatively or additionally, the data interpolation unit 230 can generate the second data by differentiating the first data. The second data can represent the slope of the first data. The data interpolation unit 230 can extract the third data by filtering the second data. The data interpolation unit 230 can match the second data with the sigma value of the Gaussian filter. Next, the data interpolation unit 230 can extract the Gaussian distribution with the matching sigma value as the third data. In this case, the third data can be represented as a thin solid line (Gaussian fit) in the curve graph.

[0064] For example, the data interpolation unit 230 can extract a Gaussian distribution with a three-sigma value as the third piece of data. In other words, normal distribution data within 99.7% of the second piece of data can be extracted as the third piece of data. In this way, by removing outliers, accurate pattern data (e.g., pattern inspection data) can be obtained.

[0065] In some example embodiments, after extracting the third piece of data, pattern inspection data may be extracted and / or generated based on the first to third pieces of data. In some example embodiments, the data extraction unit 240 may calculate the height of the pattern inspection data in the vertical direction based on the difference between the maximum and minimum values ​​of the Y-axis coordinate values ​​of the plurality of target coordinate values.

[0066] In some example embodiments, the data extraction unit 240 may calculate and / or obtain the width of the pattern inspection data in the horizontal direction based on the third piece of data. In addition, the data extraction unit 240 may calculate and / or obtain the pattern slope of the pattern inspection data by dividing the height in the vertical direction by the width in the horizontal direction. In addition, the data extraction unit 240 may calculate the angle of the transition area of ​​the target pattern by using the width in the horizontal direction and the height in the vertical direction.

[0067] After the pattern inspection data is generated, the pattern may be analyzed based on the pattern inspection data (S150). The analysis of the pattern may be performed by the data analysis unit 300. The data analysis unit 300 may determine the consistency of the OPC pattern based on the horizontal width, vertical height, and pattern slope of the pattern inspection data.

[0068] The data analysis unit 300 can determine the consistency of the OPC pattern based on whether the pattern slope exceeds a preset threshold slope. In this way, the pattern inspection device and pattern inspection method of the example embodiments can quantitatively analyze data, such as one or more of the width, height, and slope of the transition area relative to the nanosheet, by using SEM images. Alternatively or additionally, the pattern inspection device and / or pattern inspection method according to various example embodiments can obtain and analyze data with improved accuracy by using curve fitting, Gaussian filters, etc. on the data. Alternatively or additionally, by analyzing the data of the pattern on the substrate formed by the OPC pattern, the consistency of the OPC pattern can be determined, and defects such as leakage current occurring in the substrate can be predicted or more likely to be predicted and adapted to or resolved.

[0069] A semiconductor device may be manufactured based on the analysis (S160). For example, if the leakage current is predicted to be large, a feed-forward and / or feedback mechanism may be implemented to manufacture the semiconductor device based on the predicted leakage. Example embodiments are not limited thereto.

[0070] Any of the elements and / or functional blocks disclosed above may include or be implemented in a processing circuit, such as hardware including a logic circuit; a hardware / software combination, such as a processor executing software; or a combination thereof. For example, the processing circuit may more specifically include, but is not limited to, a central processing unit (CPU), an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA), a system on a chip (SoC), a programmable logic unit, a microprocessor, an application specific integrated circuit (ASIC), etc. NAND gate, NOT gate, etc.

[0071] Although various example embodiments have been specifically shown and described with reference to some of the example embodiments of the various example embodiments, it should be understood that various changes in form and detail may be made therein without departing from the spirit and scope of the appended claims. Furthermore, the example embodiments are not necessarily mutually exclusive. For example, some example embodiments may include one or more features described with reference to one or more figures, and may also include one or more other features described with reference to one or more other figures.

Claims

1. A pattern inspection method, include: obtaining an image of a substrate having a pattern formed thereon; extracting a contour based on the image; detecting a position of a target pattern based on the contour; generating pattern inspection data by performing curve fitting on the detected position of the target pattern; as well as analyzing the pattern based on the pattern inspection data, wherein The curve fitting is performed by using at least one of a Sigmoid function, a hyperbolic tangent function, and a Fermi-Dirac function, and The pattern inspection data includes a width in a first direction, a height in a second direction, and a pattern slope of the target pattern.

2. The pattern inspection method according to claim 1, in, Detecting the position of the target pattern based on the contour includes: obtaining a plurality of pattern coordinate values ​​of the contour; and A plurality of target coordinate values ​​of the target pattern are extracted from the plurality of pattern coordinate values ​​based on the outer shapes of the plurality of pattern coordinate values.

3. The pattern inspection method according to claim 2, wherein The plurality of target coordinate values ​​of the target pattern represent transition regions of nanosheets, and Extracting the plurality of target coordinate values ​​includes extracting pattern coordinate values ​​of a region in which a slope changes in the outer shape as the target coordinate values.

4. The pattern inspection method according to claim 2, wherein the pattern inspection data is generated include: A height of the pattern check data in the second direction is calculated based on a difference between a first value corresponding to a maximum value of the Y-axis coordinate values ​​of the plurality of target coordinate values ​​and a second value corresponding to a minimum value of the Y-axis coordinate values ​​of the plurality of target coordinate values. 5 . The pattern inspection method according to claim 2 , wherein extracting the target coordinate value further comprises normalizing the target coordinate value.

6. The pattern inspection method according to claim 1, in, Generating the pattern inspection data by performing curve fitting on the position of the target pattern includes: generating a first piece of data by performing curve fitting on the position of the target pattern; and The second piece of data is generated by differentiating the first piece of data.

7. The pattern inspection method according to claim 6, wherein generating the pattern inspection data by performing curve fitting on the position of the target pattern further comprises extracting a third piece of data by filtering the second piece of data, and The extracting of the third piece of data includes matching the second piece of data with a sigma value of a Gaussian filter and extracting a Gaussian distribution having a matching sigma value. 8 . The pattern inspecting method according to claim 7 , wherein a width of the pattern inspecting data in the first direction is calculated based on the third piece of data.

9. The pattern inspection method according to claim 1, wherein the pattern slope represents a value obtained by dividing the height in the second direction by the width in the first direction, and The analysis of the pattern determines a consistency of an optical proximity correction (OPC) pattern based on at least one of a width in the first direction, a height in the second direction, and a slope of the pattern. 10 . The pattern inspecting method according to claim 9 , wherein the OPC pattern represents a concave pattern of a transition region of the nanosheet.

11. A pattern inspection method, include: obtaining an image of a substrate having a pattern formed thereon; extracting a plurality of contours based on the image; obtaining a plurality of pattern coordinate values ​​of the plurality of contours; extracting a plurality of target coordinate values ​​of a target pattern from the plurality of pattern coordinate values ​​based on the contours of the plurality of pattern coordinate values; generating pattern inspection data by performing curve fitting on the detected plurality of target coordinate values; as well as analyzing a consistency of an optical proximity correction (OPC) pattern based on the pattern inspection data, The pattern inspection data includes at least one of a width in a first direction, a height in a second direction, and a pattern slope of the target pattern.

12. The pattern inspection method according to claim 11, wherein The plurality of target coordinate values ​​of the target pattern represent transition regions of the nanosheets, and Extracting the plurality of target coordinate values ​​includes extracting pattern coordinate values ​​of a region in which a slope changes in the outer shape as the target coordinate values.

13. The pattern inspection method according to claim 11, wherein generating the pattern inspection data further include: calculating a height of the pattern inspection data in the second direction based on a difference between a maximum value and a minimum value of Y-axis coordinate values ​​of the plurality of target coordinate values, and The step of extracting the target coordinate value further includes normalizing the target coordinate value.

14. The pattern inspection method according to claim 11, wherein the pattern inspection data is generated include: The pattern inspection data regarding the plurality of target coordinate values ​​is generated by using a curve fitting method using at least one of a Sigmoid function, a hyperbolic tangent function, and a Fermi-Dirac function.

15. The pattern inspection method according to claim 11, wherein the pattern inspection data is generated include: Generate a first piece of data by performing curve fitting on the detected plurality of target coordinate values; generating a second piece of data by differentiating the first piece of data; as well as The third piece of data is extracted by filtering the second piece of data.

16. The pattern inspection method according to claim 15, The third piece of data is extracted include: The second piece of data is matched with a sigma value of a Gaussian filter, and a Gaussian distribution having the matched sigma value is extracted as the third piece of data.

17. The pattern inspection method according to claim 16, wherein calculating a width of the pattern check data in the first direction based on the third piece of data, and calculating the pattern slope of the target pattern by dividing a height of the pattern check data in the second direction by its width in the first direction, and Analysis of the pattern determines a uniformity of the OPC pattern based on at least one of a width in the first direction, a height in the second direction, and a slope of the pattern.

18. A pattern inspection method, include: obtaining an image of a substrate having a pattern formed thereon; extracting a plurality of contours based on the image; obtaining a plurality of pattern coordinate values ​​of the plurality of contours; extracting a plurality of target coordinate values ​​of a target pattern from the plurality of pattern coordinate values ​​based on the contours of the plurality of pattern coordinate values; generating pattern inspection data by performing curve fitting on the detected plurality of target coordinate values; as well as The consistency of an optical proximity correction (OPC) pattern is analyzed based on the pattern inspection data, wherein The plurality of target coordinate values ​​of the target pattern represent transition regions of nanosheets, The pattern inspection data includes a width in a first direction, a height in a second direction, and a pattern slope of the target pattern, Generating the pattern inspection data includes generating the pattern inspection data using a curve fitting method for the plurality of target coordinate values ​​by using at least one of a sigmoid function, a hyperbolic tangent function, and a Fermi-Dirac function, and Extracting the plurality of target coordinate values ​​includes extracting pattern coordinate values ​​of a region in which a slope changes in the outer shape as the target coordinate values.

19. The pattern inspection method according to claim 18, wherein the pattern inspection data is generated include: The first data is generated by performing curve fitting on the detected plurality of target coordinate values; generating a second piece of data by differentiating the first piece of data; as well as extracting the third piece of data by filtering the second piece of data, and The extracting of the third piece of data includes: The second piece of data is matched with a sigma value of a Gaussian filter, and a Gaussian distribution having the matched sigma value is extracted as the third piece of data.

20. The pattern inspection method according to claim 19, wherein the pattern inspection data is generated include: calculating a height of the pattern inspection data in the second direction based on a difference between a maximum value and a minimum value of Y-axis coordinate values ​​of the plurality of target coordinate values, wherein the width of the pattern inspection data in the first direction is calculated based on the third piece of data, and the pattern slope of the target pattern is calculated by dividing the height of the pattern inspection data in the second direction by its width in the first direction, and The analysis of the pattern determines the consistency of the OPC pattern based on the width in the first direction, the height in the second direction, and the slope of the pattern.

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