Optical metrology device

Abstract

An optical metrology apparatus includes an illumination unit configured to simultaneously irradiate a first illumination light and a second illumination light onto a substrate surface, the first illumination light irradiating at a first incident angle having a difference from a measurement angle greater than a critical angle, the second illumination light irradiating at a second incident angle having a difference from the measurement angle equal to or less than the critical angle, the wavelength of the second illumination light is different from that of the first illumination light; an optical system configured to collect reflected light from a surface of the substrate according to the first illumination light and the second illumination light; and a multi-channel camera configured to generate an original image in which a dark field image and a bright field image of the surface of the substrate are integrated based on the reflected light collected by the optical system.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the priority of Korean Patent Application No. 10-2023-0073065 filed in the Korean Intellectual Property Office on June 7, 2023, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0003] The inventive concept relates to an optical metrology apparatus for performing optical measurement on a substrate, a substrate processing apparatus including the optical metrology apparatus, and an optical inspection method for a substrate. Background Art

[0004] As the integration density of semiconductor devices increases, defects such as scratches, discoloration, etc. become key factors affecting the electrical characteristics and performance of semiconductor devices.

[0005] Optical inspection may be performed on a semiconductor substrate to detect defects in or on a semiconductor device. Optical inspection may include brightfield inspection using brightfield illumination and darkfield inspection using darkfield illumination. Brightfield inspection may effectively detect discoloration but may have difficulty detecting scratches, while darkfield inspection may effectively detect scratches but may have difficulty detecting discoloration.

[0006] When brightfield inspection and darkfield inspection are performed on semiconductor devices, various types of defects such as discoloration and scratches can be effectively detected. However, when optical inspection needs to be performed by sequentially performing brightfield illumination and darkfield illumination, the time period required to inspect the substrate may increase. Summary of the invention

[0007] An aspect of the inventive concept is to provide an optical metrology apparatus, a substrate processing apparatus, and an optical inspection method capable of effectively detecting various types of defects while reducing a time period required to inspect defects of a substrate.

[0008] According to one aspect of the inventive concept, an optical metrology device includes: an illumination unit configured to simultaneously irradiate a first illumination light and a second illumination light onto a surface of a substrate, the first illumination light being irradiated at a first incident angle having a difference from a measurement angle greater than a critical angle, the second illumination light being irradiated at a second incident angle having a difference from the measurement angle equal to or less than the critical angle, and a wavelength of the second illumination light being different from that of the first illumination light; an optical system configured to collect reflected light from a surface of the substrate according to the first illumination light and the second illumination light; and a multi-channel camera configured to generate an original image in which a dark field image and a bright field image of the surface of the substrate are integrated based on the reflected light collected by the optical system.

[0009] According to one aspect of the inventive concept, an optical metrology device includes: an illumination unit configured to irradiate first illumination light and second illumination light having different wavelengths onto a surface of a substrate moving along a conveying path at different incident angles; and a multi-channel camera configured to generate an original image in which a dark field image based on scattered light according to the first illumination light and a bright field image based on directly reflected light according to the second illumination light are integrated, wherein the optical metrology device is located on a conveying path of a substrate included in a substrate processing device.

[0010] According to one aspect of the inventive concept, an optical metrology device includes: an illumination unit configured to irradiate first illumination light and second illumination light having different wavelengths onto a surface of a substrate moving along a conveying path at different incident angles; a multi-channel camera configured to acquire scattered light of the first illumination light and directly reflected light of the second illumination light to generate an original image; and an inspection device configured to correct a shape of the original image to generate a corrected image, separate the corrected image according to color channels, generate a dark field image having illumination information of the scattered light and a bright field image having illumination information of the directly reflected light, and analyze the dark field image and the bright field image to inspect defects on the surface of the substrate.

[0011] According to one aspect of the inventive concept, a substrate processing device includes: a polisher configured to polish a surface of a substrate; a cleaner configured to clean the polished surface of the substrate; a conveyor configured to convey the substrate from the polisher to the cleaner; a loader configured to supply the substrate from a conveying container to the polisher before processing, and to convey the substrate from the cleaner to the conveying container after processing; and an optical metrology device located on a conveying path of the substrate between the cleaner and the conveying container, wherein the optical metrology device is configured to irradiate first illumination light and second illumination light having different wavelengths onto the surface of the substrate moving along the conveying path at different incident angles, and generate an original image in which a dark field image based on scattered light according to the first illumination light and a bright field image based on directly reflected light according to the second illumination light are integrated.

[0012] According to one aspect of the inventive concept, an optical inspection method includes: irradiating a first illumination light and a second illumination light with different wavelengths onto a surface of a substrate moving along a conveying path at different incident angles; acquiring scattered light of the first illumination light and directly reflected light of the second illumination light using a multi-channel camera to generate an original image; correcting a shape of the original image to generate a corrected image; separating the corrected image according to color channels to generate a dark field image having illumination information of the scattered light and a bright field image having illumination information of the directly reflected light; and analyzing the dark field image and the bright field image to inspect defects on the surface of the substrate. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other aspects, features and advantages of the present inventive concept will be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings, in which:

[0014] Figure 1 is a diagram illustrating a substrate processing apparatus according to some embodiments.

[0015] Figure 2 is a flow chart illustrating a method of processing a substrate according to some embodiments.

[0016] Figure 3A and Figure 3B is a diagram showing defect types of a substrate.

[0017] Figure 4 and Figure 5 is a diagram illustrating an optical metrology apparatus according to some embodiments.

[0018] Figure 6 is a flow chart illustrating a method of optical inspection according to some embodiments.

[0019] Figure 7 is a diagram illustrating an optical path of an optical metrology apparatus according to some embodiments.

[0020] Figure 8 is a diagram illustrating incident angles of illumination light for an optical metrology device according to some embodiments.

[0021] Fig. 9 is a graph showing illumination according to a measurement angle of an optical metrology device and an incident angle of illumination light, according to some embodiments.

[0022] FIG. 10A to FIG. 10F is a diagram illustrating a dark field image according to an incident angle of illumination light according to some embodiments.

[0023] Fig.11A and Fig. 11B is a diagram illustrating a method of generating a corrected image according to some embodiments.

[0024] Fig.12 is a diagram schematically illustrating a method of inspecting defects from a corrected image according to some embodiments.

[0025] Fig.13 is a diagram illustrating a method of generating a feature map from an input image using a neural network operation.

[0026] FIG. 14A to FIG. 14D is a diagram illustrating an input image and a feature map generated from the input image.

[0027] Fig.15 is a flow chart illustrating a method of learning a statistical model according to some embodiments.

[0028] Fig.16 is a diagram illustrating a method of generating a statistical model according to some embodiments.

[0029] Fig.17 is a flow chart illustrating a method of inspecting defects according to some embodiments.

[0030] Fig.18 is a diagram showing the Mahalanobis distance.

[0031] 19A to 19D are diagrams showing various input images and inspection images and binary images extracted based on the input images. DETAILED DESCRIPTION

[0032] Hereinafter, example embodiments of the inventive concept will be described with reference to the accompanying drawings.

[0033] Figure 1 is a diagram illustrating a substrate processing apparatus according to some embodiments.

[0034] Reference Figure 1 , the substrate processing apparatus 100 may include a loader 110, a conveyor 120, a polisher 130, a cleaner 140, and a power supply 150 disposed in a housing H. The substrate processing apparatus 100 may be used for a chemical mechanical polishing (CMP) process to planarize a surface of a substrate S during a process of manufacturing a semiconductor device. For example, the substrate S may be a wafer.

[0035] Figure 2 is a flow chart illustrating a substrate processing method according to some embodiments. Figure 2 , the CMP process of the substrate processing apparatus 100 may include operations S11 to S15 . Figure 1 A transfer path TP of the substrate S according to the CMP process for operations S11 to S15 may be illustrated in FIG.

[0036] The loader 110 may provide the substrate S to the polisher 130 before performing the CMP process, and may receive the substrate S from the cleaner 140 after the CMP process is completed. The loader 110 may include a transfer robot 111. In operation S11, when the substrate S is loaded into a transfer container (e.g., a front opening universal box (FOUP)) outside the housing H, the transfer robot 111 may obtain the substrate S from the transfer container and may load the substrate S into the polisher 130.

[0037] The conveyor 120 may convey the substrate S along a predetermined conveying path so that the substrate S is polished and cleaned in a predetermined order. The conveyor 120 may include linear conveyors 121 and 122 and a swing conveyor 123 .

[0038] The polisher 130 may perform polishing of the substrate S. The polisher 130 may include a loading unit 131 and first to fourth polishing units 130a to 130d. The loading unit 131 may load the substrate S obtained from the transfer robot 111. The first to fourth polishing units 130a to 130d may have the same or different configurations. For example, the first polishing unit 130a may have a polishing pad 132a for fixing the substrate S, a polishing head 133a for polishing the substrate S, and a polishing arm 134a for controlling and maintaining the position of the polishing head 133a.

[0039] In operation S12, the substrate S loaded in the polisher 130 may be sequentially polished in the first to fourth polishing units 130a to 130d while moving along the linear conveyors 121 and 122. In addition, the substrate S on which the polishing process is completed may be loaded into the cleaner 140 through the swing conveyor 123.

[0040] The cleaner 140 may clean and dry the substrate S polished by the polisher 130. For example, the cleaner 140 may include a loading unit 141, a transfer position 142, brush units 143 and 144, a two-fluid cleaning unit 145, and a drying unit 146.

[0041] The loading unit 141 may load the substrate S polished by the polisher 130. The substrate S may be sequentially transferred to the brush units 143 and 144, the dual-fluid cleaning unit 145, and the drying unit 146 along the transfer position 142. The brush units 143 and 144 may brush the surface of the polished substrate S to remove contaminants, the dual-fluid cleaning unit 145 may spray a fluid on the surface of the substrate S to remove residual materials, and the drying unit 146 may dry the surface of the substrate S.

[0042] In operation S13 , the substrate S loaded in the loading unit 141 may be sequentially moved along the transfer position 142 through the brush units 143 and 144 , the two-fluid cleaning unit 145 , and the drying unit 146 , and may be cleaned and dried.

[0043] In operation S14, the substrate S on which cleaning is completed may be received by the transfer robot 111, and may be moved along a predetermined transfer path of the loader 110 to be loaded into a transfer container. Then, in operation S15, the substrate S may be unloaded from the transfer container.

[0044] Various types of defects such as scratches or discoloration may occur on the substrate S due to polishing or cleaning of the substrate S. Defects occurring in the substrate S may reduce electrical characteristics and performance of a semiconductor device formed on the substrate S, and as a result, the yield of the semiconductor device may be reduced.

[0045] Figure 3A and Figure 3B is a diagram showing defect types of a substrate.

[0046] Figure 3A is a diagram showing a discoloration defect occurring in a substrate S. Chemical substances used in an abrasive for polishing may discolor the surface of the substrate S by reflecting with the surface material of the substrate S. Figure 3A , a color changing area DC is shown on the surface of the substrate S.

[0047] Figure 3B FIG. 2 shows a scratch defect occurring on a substrate S. Abrasive particles used in an abrasive for a CMP process may generate scratches on the surface of the substrate S, and thus a scratch defect may occur. Figure 3B , shows scratches SC generated on the surface of a substrate S.

[0048] Preferably, after the CMP process, the substrate S is inspected Figure 3A Discoloration defects and Figure 3B Compare the scratch defects of the two. Figure 3A and Figure 3B , the discolored region DC may have a relatively larger area than the scratch SC. Therefore, by bright field inspection of detecting light directly reflected from the substrate S, the discolored defect of the substrate S can be detected.

[0049] Since the width of the scratch SC may be smaller than the resolution of the camera used for optical measurement, the scratch defect may be difficult to detect by bright field inspection. Since Rayleigh scattering or Mie scattering may occur in the scratch SC having a size similar to the wavelength of the light irradiated onto the substrate S, the scratch defect may be detected by dark field inspection that detects light scattered from the substrate S.

[0050] In short, in order to inspect both the discoloration defect and the scratch defect of the substrate S, both the bright field inspection and the dark field inspection may be performed.

[0051] When the substrate S received from the substrate processing apparatus 100 is moved to a separate optical metrology apparatus, and the optical metrology apparatus needs to sequentially perform bright field inspection and dark field inspection, it may take a lot of time to inspect the substrate S. When the inspection time period of the substrate S is greatly increased, the production time period of the substrate S may be increased, and productivity may be reduced.

[0052] According to some embodiments, the optical metrology apparatus 200 may be included in the substrate processing apparatus 100. Figure 1The optical metrology device 200 may be disposed on a transfer path TP of the substrate S in the loader 110. Specifically, the optical metrology device 200 may be disposed on a transfer path through which the substrate S is transferred from the cleaner 140 to the transfer container.

[0053] The optical metrology device 200 can simultaneously illuminate the surface of the substrate S being transported along the transport path TP with first and second illumination lights having different wavelengths at different incident angles. For example, the first illumination light can be illumination light for dark field inspection, and the second illumination light can be illumination light for bright field inspection.

[0054] The optical metrology device 200 may use an optical system to collect reflected light reflected from the surface of the substrate S by the first illumination light and the second illumination light, and may use a multi-channel camera such as an RGB channel camera to generate an original image based on the reflected light. The color channels of the original image may be separated to simultaneously acquire a dark field image by the first illumination light and a bright field image by the second illumination light.

[0055] According to some embodiments, since the optical system and the camera for acquiring the bright field image and the dark field image are integrated, the size of the optical metrology device 200 can be miniaturized, and the optical metrology device 200 can also be mounted on the loader 110 having a limited space. In addition, during the transfer of the substrate S, the bright field image and the dark field image can be acquired at the same time. Therefore, except for the time required to process the substrate S, the bright field inspection and the dark field inspection can be performed without additional time.

[0056] The inventive concept is not limited to the case where the optical metrology apparatus 200 is included in the substrate processing apparatus 100 for CMP processing. For example, the optical metrology apparatus 200 may be included in a substrate processing apparatus for etching, and may inspect defects generated on the surface of the substrate S after etching.

[0057] In the following, reference will be made to Figures 4 to 19D An optical metrology apparatus according to some embodiments is described in detail.

[0058] Figure 4 and Figure 5 is a diagram illustrating an optical metrology apparatus according to some embodiments. Figure 4 is a perspective view showing the optical metrology apparatus 200 in an XYZ coordinate system, and Figure 5 is a plan view showing the optical metrology device 200 in the YZ plane.

[0059] Refer to Figure 4 and 5The optical metrology device 200 may include an illumination unit or illumination system 210, an optical system 220, a camera or camera unit or camera system 230, and a mounting member or mounting unit or mounting system 240. The optical metrology device 200 may be disposed on a transport path TP of the substrate S.

[0060] The illumination unit 210 may illuminate the substrate S being transported along the transport path TP with the first illumination light and the second illumination light having different wavelengths. For example, the first illumination light may be red light (R), and the second illumination light may be green light (G) and blue light (B). The first illumination light may be illumination light for dark field inspection, and the second illumination light may be illumination light for bright field inspection, and may be irradiated on the substrate S at different incident angles.

[0061] The optical system 220 may collect light RL reflected from the surface of the substrate S by the first illumination light L1 and the second illumination light L2, and transmit the collected light to the camera unit 230. The optical system 220 may include a mirror or a mirror unit or a mirror system 221 and a lens or a lens unit or a lens system 222 for improving the efficiency of the path of the reflected light and miniaturizing the optical system 220.

[0062] The reflected light may include directly reflected light and scattered light. In the reflected light of the first illumination light L1, the scattered light may be more dominant than the directly reflected light, and in the reflected light of the second illumination light L2, the directly reflected light may be more dominant than the scattered light. The scattered light of the first illumination light L1 may include a red light (R) component, and the directly reflected light of the second illumination light L2 may include a green light (G) component and a blue light (B) component.

[0063] The camera unit 230 may capture the reflected light transmitted from the optical system 220 to generate an original image. The camera unit 230 may include a multi-channel camera, such as an RGB channel camera. The RGB channel camera may include a red pixel configured to detect red light, a green pixel configured to detect green light, and a blue pixel configured to detect blue light. The red pixel may sense the scattered light of the first illumination light L1, and the green pixel and the blue pixel may sense the directly reflected light of the second illumination light L2.

[0064] The camera unit 230 may be implemented as a line scan camera to photograph a moving substrate. In the line scan camera, red pixels, green pixels, and blue pixels may be arranged linearly, respectively. The line scan camera may photograph a fixed scanning area SR toward the moving substrate S multiple times to obtain a partial image of the substrate S, and may perform a scanning operation for reconstructing the partial image into a two-dimensional original image.

[0065] The scanning region SR may be a linear region extending along the first direction X, the linear region may intersect with the second direction Y, and the second direction Y may be a conveying direction of the substrate S.

[0066] The original image generated by the camera unit 230 may be an image in which a dark field image of scattered light of the first illumination light L1 and a bright field image of directly reflected light of the second illumination light L2 are integrated. The dark field image and the bright field image may be acquired by separating color channels of the original image.

[0067] Specifically, the correction image can be divided into a red channel image, a green channel image, and a blue channel image. The red channel image can be determined as a dark field image, and a bright field image can be generated by combining the green channel image and the blue channel image. For example, the dark field image and the bright field image can be simultaneously acquired by the scanning operation of the camera unit 230.

[0068] According to some embodiments, the original image may be provided to an inspection device outside the optical metrology device 200, and the inspection device may divide the original image into a dark field image and a bright field image, may perform a dark field inspection based on the dark field image, and may perform a bright field inspection based on the bright field image. However, the present inventive concept is not limited thereto, and the inspection device may be included in the optical metrology device 200.

[0069] The mounting unit 240 may fix the lighting unit 210, the optical system 220, and the camera unit 230 to the substrate processing apparatus 100. For example, the mounting unit 240 may be mounted on a wall above an exit of the substrate processing apparatus 100 from which the substrate S is unloaded into a transfer container. The mounting unit 240 may include a passage 241 through which the substrate S enters and exits.

[0070] Hereinafter, an optical inspection method using the optical metrology apparatus 200 will be described in detail.

[0071] Figure 6 is a flow chart illustrating a method of optical inspection according to some embodiments.

[0072] In operation S21 , the first illumination light L1 and the second illumination light L2 may be irradiated on the substrate S passing through the transfer path.

[0073] In operation S22, the multi-channel camera may acquire scattered light of the first illumination light L1 and reflected light of the second illumination light L2 to generate an original image. When the original image is generated in a state where the substrate S moves on the conveying path at an uneven speed, the original image may have a deformed shape different from that of the substrate S.

[0074] In operation S23 , the inspection apparatus may correct the shape of the original image to generate a corrected image having the shape of the substrate S.

[0075] In operation S24 , the inspection apparatus may separate and correct the image according to the channels to obtain a dark field image having information of scattered light and a bright field image having information of reflected light.

[0076] In operation S25 , the inspection device may analyze the dark field image to inspect a scratch defect, and may analyze the bright field image to inspect a discoloration defect.

[0077] Figure 7 is a diagram illustrating an optical path of an optical metrology device according to some embodiments. Specifically, Figure 7 An optical path through which illumination light irradiated from the illumination unit 210 passes to reach the camera unit 230 via the substrate S and the optical system 220 is shown.

[0078] Reference Figure 7 , the lighting unit 210 may include a first light unit or a first lighting unit 211 that irradiates a first lighting light L1 and a second light unit or a second lighting unit 212 that irradiates a second lighting light L2. The wavelength of the first lighting light L1 may be different from the wavelength of the second lighting light L2. For example, the first lighting light L1 may be red light, and the second lighting light L2 may include green light and blue light. Specifically, the first lighting light L1 may be light having a wavelength of 620nm to 630nm, and the second lighting light L2 may be light having a wavelength of 580nm or less.

[0079] According to some embodiments, the first lighting unit 211 may generate red light using red light emitting diode (LED) lighting, and the second lighting unit 212 may apply a short-pass filter that transmits green light and blue light to white LED lighting to generate green light and blue light. However, the method of generating the first lighting light L1 and the second lighting light L2 by the first lighting unit 211 and the second lighting unit 212 is not limited thereto.

[0080] The incident angle of the first illumination light L1 may also be different from the incident angle of the second illumination light L2. For example, the second illumination light L2 may illuminate at a second incident angle IA2 having a critical angle or a difference less than the critical angle from the measurement angle MA for bright field inspection. Preferably, the second illumination light L2 may illuminate at a second incident angle IA2 equal to the measurement angle MA. In this case, the measurement angle MA may refer to an angle of reflected light that can be observed by the camera unit 230, and the measurement angle MA may be determined according to the arrangement of the camera unit 230 and the arrangement of the optical system 220. When the second illumination light L2 illuminates at the second incident angle IA2 equal to the measurement angle MA, the reflected light may be directly acquired from the camera unit 230, so that the bright field inspection may be effectively performed.

[0081] The first illumination light L1 may be illuminated at a first incident angle IA1, and the first incident angle IA1 has a difference greater than a critical angle from a measurement angle MA for dark field inspection. Figure 8 to Figure 1 0 The critical angle and the first incident angle IA1 are described in detail.

[0082] Figure 8 is a diagram illustrating incident angles of illumination light for an optical metrology device according to some embodiments.

[0083] Figure 8 1 shows the first illumination light L1 and the scattered light SL on the surface of the substrate S having the scratch SC. The first illumination light L1 incident on the surface of the substrate S may be scattered in various directions by defects such as the scratch SC. Among the light scattered in various directions, the light scattered in the direction having an angle equal to the measurement angle MA may be incident on the camera unit 230.

[0084] According to the measurement angle MA and the first incident angle IA1 of the first illumination light L1, the illuminance of the scattered light incident to the camera unit 230 may be changed. The measurement angle MA, the first incident angle IA1 of the first illumination light L1, and the second incident angle IA2 of the second illumination light L2 may be measured relative to a central axis or an optical axis AOI (e.g., a vertical axis).

[0085] Fig. 9 is a graph showing illumination according to a measurement angle of an optical metrology device and an incident angle of illumination light, according to some embodiments.

[0086] exist Fig. 9 In the graph of , the horizontal axis represents the measurement angle, and the vertical axis represents the incident angle of the illumination light. Fig. 9 The illuminance of scattered light incident on the camera unit 230 according to the measurement angle and the incident angle is shown as a contour line. Fig. 9 , as the difference between the measurement angle and the incident angle decreases, the illuminance of the scattered light may increase, and as the difference between the measurement angle and the incident angle increases, the illuminance of the scattered light may decrease.

[0087] The light incident from the substrate S to the camera unit 230 by the first illumination light L1 may include not only scattered light but also light directly reflected by the first illumination light L1. The critical angle may be defined as an angle at which the illuminance of the directly reflected light and the illuminance of the scattered light collected in the direction of the measurement angle are equal to each other. When the difference between the measurement angle and the incident angle is less than or equal to the critical angle, the illuminance of the directly reflected light may be significantly increased, and when the difference between the measurement angle and the incident angle is greater than the critical angle, the illuminance of the directly reflected light may be rapidly reduced. Fig. 9 An area CA is shown where the difference between the measured angle and the incident angle is less than or equal to the critical angle. Fig. 9 In the example of , the critical angle may be 4 degrees.

[0088] When the light incident to the camera unit 230 includes scattered light and directly reflected light, the directly reflected light may become noise when performing dark field inspection. When the difference between the measurement angle and the incident angle is less than or equal to the critical angle, it may be difficult to obtain a normal dark field image because the illumination of the directly reflected light is greater than the illumination of the scattered light.

[0089] FIG. 10A to FIG. 10F is a diagram illustrating a dark field image according to an incident angle of illumination light according to some embodiments.

[0090] The measuring angle may be fixed according to the arrangement of the optical system 220 and the camera unit 230. FIG. 10A to FIG. 10F In the example shown in FIG. 1 , the measurement angle may be fixed at 10 degrees.

[0091] Reference FIG. 10A to FIG. 10F , when the incident angle of the illumination light is 0 degrees, 5 degrees, 15 degrees, and 20 degrees (for example, the difference between the measurement angle and each incident angle is greater than the critical angle), the dark field image can be clearly seen. When the incident angle of the illumination light is 6 degrees and 10 degrees (for example, the difference between the measurement angle and the incident angle is equal to or less than the critical angle), the dark field image may appear blurry or almost invisible.

[0092] According to some embodiments, the incident angle of the first illumination light L1 may be determined as an angle having a difference from the measurement angle greater than the critical angle. Specifically, the incident angle of the first illumination light L1 may be determined as an angle at which the difference between the illuminance of the scattered light and the illuminance of the directly reflected light is maximum in an angle range having a difference from the measurement angle greater than the critical angle.

[0093] Hereinafter, a method of correcting the shape of an original image generated by acquiring scattered light from the first illumination light L1 and reflected light from the second illumination light L2 to generate a corrected image having the shape of the substrate S will be described.

[0094] Fig.11A and Fig. 11B is a diagram illustrating a method of generating a corrected image according to some embodiments. Fig.11A shows the original image before correction, Fig. 11B The corrected image is shown.

[0095] Reference Fig.11A , the original image may have a shape different from the shape of the substrate S. Specifically, the camera unit 230 may be a line scan camera and may continuously photograph a fixed scanning area and may scan the substrate S in a two-dimensional shape while the substrate S passes through the scanning area. Fig.11A In the present invention, an original image scanned in a two-dimensional shape and a region where the original image is scanned at several viewpoints with a constant time interval may be displayed as lines.

[0096] The substrate S may not always move at a constant speed in the conveying path, and the original image may have a deformed shape in a portion where the substrate S does not move at a constant speed. For example, when the substrate S having a circular shape is accelerated, the original image may not have a circular shape but may have a shape elongated in the conveying direction.

[0097] Fig.11A The original image can be Fig. 11B The correction image shown in FIG. 1 is corrected to have a shape equal to the shape of the substrate S in a manner similar to that of the correction image shown in FIG. For example, a correction image having a circular shape can be generated by detecting the degree of distortion of the original image based on the shape of the original image and adjusting the coordinate value corresponding to the conveying direction of the original image. Fig. 11B On the corrected image, you can use lines to mark Fig.11A The area in the original image corresponds to the scanned area.

[0098] According to some embodiments, among the pixel values ​​of red pixels, green pixels, and blue pixels constituting the original image, at least the pixel values ​​of green pixels and the pixel values ​​of blue pixels can be used to detect the shape of the original image. Since the green pixels and the blue pixels can collect direct reflected light for bright field inspection, the pixel values ​​of the green pixels and the pixel values ​​of the blue pixels can be respectively greater than the pixel values ​​of the red pixels. Therefore, when the pixel values ​​of the green pixels and the pixel values ​​of the blue pixels are used, the shape of the original image can be more easily detected.

[0099] It may be difficult to detect defects, particularly scratches, using the correction image itself. For example, even when the correction image is corrected to have the shape of the substrate S, the correction image may have a slightly distorted pattern compared to an image obtained by photographing a stationary substrate S.

[0100] According to some embodiments, the characteristics of the corrected image can be extracted by performing a neural network operation on the corrected image, and defects can be checked based on the characteristics. Therefore, even when the corrected image has a slightly distorted pattern, defects can be effectively checked.

[0101] In the following, reference will be made to Figures 12 to 19D A method of inspecting defects using a neural network operation by an inspection device is described in detail.

[0102] Fig.12 is a diagram schematically illustrating a method of inspecting defects from a corrected image according to some embodiments.

[0103] According to some embodiments, in order to detect scratches from the corrected image representing the surface of the substrate S, a dark field image may be extracted. In addition, in order to apply a neural network operation to the dark field image, the corrected image may be divided into a plurality of input images. When the neural network operation is applied by dividing the dark field image into a plurality of input images, the complexity of the neural network operation may be reduced.

[0104] A plurality of regions of interest ROIs may be set on the surface of the substrate S. For example, the substrate S may include a plurality of chip regions in which semiconductor chips are formed, and each of the plurality of chip regions may be set as a region of interest ROI. However, the inventive concept is not limited thereto, and the region of interest ROI may be set on a plurality of chip regions, or may be set in a region smaller than the chip region. The dark field image may be divided into a plurality of input images based on the region of interest ROI.

[0105] The input image may include a plurality of pixels. The position of each of the pixels may be represented as a coordinate value (X, Y), and each of the coordinate values ​​may correspond to a different portion of the region of interest ROI. The value of each of the pixels may represent the illumination of reflected light from different portions of the region of interest ROI. The input image may have information about a pattern formed in the region of interest ROI and information about a defect.

[0106] Each of the input images can be used as an input of a neural network operation, and a feature map corresponding to the input image can be output as an output of the neural network operation. In the feature map output according to the neural network operation, information about defects can be highlighted compared to the original input image.

[0107] According to some embodiments, the feature map may be three-dimensional data having a width W, a height H, and a channel C. The coordinate values ​​of the width W and the height H of the feature map may correspond to each portion of the region of interest ROI. Hereinafter, the position specified by the coordinate values ​​of the width W and the height H of the feature map may be referred to as the plane position of the feature map.

[0108] Typically, a feature map may have multiple channels C that highlight different information in an input image. Each of the plane positions of the feature map may have a feature vector including multiple features.

[0109] Typically, the size of the plane specified by the width W and height H of the feature map may be smaller than the size of the input image. For example, both the feature map and the input image may correspond to a region of interest ROI, but the resolution of the feature map may be lower than that of the input image.

[0110] The feature map can be applied to the statistical model to generate an inspection image representing the statistical distance of each part of the region of interest ROI. The inspection image can be two-dimensional data with width W and height H, and the coordinate values ​​of the width W and height H can correspond to each part of the region of interest ROI.

[0111] The statistical distance may be a numerical value indicating how unlikely a certain value is statistically. According to some embodiments, a plane position in the inspection image having a statistical distance greater than or equal to a threshold may be determined as a position where a defect exists.

[0112] In the following, reference will be made to Figures 13 to 18 A method of inspecting defects from a corrected image according to some embodiments is described in detail.

[0113] Fig.13 is a diagram illustrating a method of generating a feature map from an input image using a neural network operation. Specifically, Fig.13 A method of generating a feature map from an input image using a convolutional neural network (CNN) is shown.

[0114] Reference Fig.13 , CNN may include multiple layers, including layer 1 and layer 2. An input image (1×X×Y) may be input to the first layer "layer 1", and the output of one layer may be the input to the next layer. Also, a feature map (C×W×H) may be output from the last layer "layer 2".

[0115] Each of the plurality of layers may include a convolution layer, and may optionally further include a pooling layer. In the convolution layer, a feature map may be generated by performing a convolution operation on an input image of the convolution layer and each of one or more filters.

[0116] Multiple filters can be used in a convolutional layer to extract various features from the input image. A filter can refer to a weight matrix used to highlight features in the input image.

[0117] For example, in the present inventive concept, in order to extract scratches, a Sobel filter, a Prewitt filter, etc. may be used to detect edges of an image, and a filter for Histogram of Oriented Gradients (HOG) feature extraction may be used.

[0118] The pooling layer can reduce the size of the feature map by reducing the size of the output feature map. For example, when performing max pooling in the pooling layer, the size of the feature map can be reduced by retaining only the maximum value in each pooling window of the feature map and removing the remaining values.

[0119] According to some embodiments, even when the input image has a distorted pattern, a feature can be extracted from the input image using a CNN, and defects can be checked based on the feature.

[0120] FIG. 14A to FIG. 14D is a diagram illustrating an input image and a feature map generated from the input image.

[0121] Fig.14A shows the input image, FIG. 14B to FIG. 14D A two-dimensional image constituting a feature map generated by applying a CNN to an input image is shown.

[0122] Reference Fig.14A In the input image, the pattern PT and the scratch SC in the region of interest ROI may have similar brightness, and the background portion without pattern or scratch may be displayed relatively darkly. The pattern PT and the scratch SC in the input image may have similar pixel values, and it may be difficult to identify the scratch SC in the input image.

[0123] FIG. 14B to FIG. 14D The two-dimensional image in FIG shows the feature values ​​in different channels corresponding to the region of interest ROI. FIG. 14B to FIG. 14D , in each two-dimensional image constituting the feature map, the portion with the scratch SC in the region of interest ROI can be displayed relatively brightly, and the pattern and background portion can be displayed relatively darkly. For example, in the feature map, the portion with the scratch can be highlighted. Therefore, by using the feature map, the portion with the scratch in the region of interest ROI can be easily identified.

[0124] According to some embodiments, a statistical model of characteristics of the surface of the substrate S may be learned by using a feature map generated using input images of various patterns for the substrate S. In addition, by applying the statistical model to the feature map of the input image of the substrate S to be inspected for defects, it may be inspected whether the substrate S has defects.

[0125] Fig.15 is a flow chart illustrating a method of learning a statistical model according to some embodiments.

[0126] In operation S31, a learning image may be acquired. For example, when the inspection to be performed using the neural network model is a dark field inspection, the learning image may include dark field images obtained from a plurality of substrates S having various patterns on their surfaces.

[0127] According to some embodiments, in order to acquire characteristics of the surface of a substrate S without defects, a learning image may be selected from images obtained from a substrate that does not include defects.

[0128] In operation S32, an input image may be extracted from the learning image. For example, at least a portion of a plurality of semiconductor chip regions included in the substrate S may be selected as a region of interest, and an input image corresponding to the region of interest may be extracted. The input image for statistical model learning may be referred to as an input image for learning to distinguish the input image for statistical model learning from the input image for optical inspection.

[0129] In operation S33, a feature map may be generated by performing a CNN operation on each of the input images for learning. Fig.13 Methods for generating feature maps are described. Feature maps used for statistical model learning may be referred to as feature maps for learning to distinguish feature maps used for statistical model learning from feature maps used for optical inspection.

[0130] In operation S34, a statistical model for the region of interest may be generated. For example, the statistical model for the region of interest may include the average value of each feature in the feature map used for learning and the covariance between two different features. Fig.16 Describe the method for generating statistical models for each feature.

[0131] In operation S35, the statistical model may be stored for use in defect inspection. For example, the statistical model may be stored in an inspection device.

[0132] Fig.16 is a diagram illustrating a method of generating a statistical model according to some embodiments.

[0133] Reference Fig.16 , shows the feature map generated from the input image. Fig.12 The feature map may include three-dimensional data, and the three-dimensional data includes a width W, a height H, and a number of channels C.

[0134] A plane including width W and height H in the feature map may correspond to the input image. The plane may have a resolution lower than that of the input image, and the characteristics of the input image may be highlighted. In addition, in the feature map, different characteristics of the input image may be highlighted for each channel.

[0135] In order to generate a statistical model, multiple feature maps can be generated for learning. For example, when a CNN is applied to N input images, N feature maps can be generated, such as Fig.16 shown.

[0136] According to some embodiments, a mean vector of each feature vector corresponding to each plane position and a covariance matrix of each feature vector may be determined based on a plurality of feature maps used for learning.

[0137] For example, the feature vector may include a feature value for each channel at a plane position specified by a horizontal position and a vertical position of the feature map. An average vector of the feature vector may be determined by averaging the feature values ​​for each channel in the feature map used for learning.

[0138] And, the covariance of the eigenvectors corresponding to a particular plane position can be determined as follows. Fig.16 In FIG. 1 , features F1 , F2 , and F3 for each channel corresponding to the plane position (1, 1) may be shaded. Features F1 , F2 , and F3 for each channel may constitute a feature vector.

[0139] Based on the features F1, F2, F3 corresponding to the plane position (1, 1), the covariance matrix Cov(1, 1) for the plane position (1, 1) can be determined as shown in the following equation 1:

[0140] [Equation 1]

[0141]

[0142] In Equation 1, variances Var(F1), Var(F2), and Var(F3) may be determined as variances of features F1, F2, and F3 in the plurality of feature maps, respectively. In addition, covariance may be determined based on a correlation between two features in each of the plurality of feature maps. For example, Cov(F1, F2) may be determined based on a correlation between features F1 and F2 in each of the plurality of feature maps.

[0143] Similarly, the covariance matrix of the feature vector corresponding to a specific plane position of the feature map can be determined based on the covariance of the features corresponding to each plane position.

[0144] An inspection image may be generated by applying a statistical model including a mean value and a covariance matrix to a feature map generated based on a target image to be inspected for defects, and defect inspection may be performed based on the inspection image.

[0145] Fig.17 is a flow chart illustrating a method of inspecting defects according to some embodiments.

[0146] In operation S41, an image of a substrate on which defect inspection is to be performed, such as a target image, may be acquired. For example, when the inspection to be performed using the neural network model is a dark field inspection, the inspection image may be a dark field image generated by the first illumination light L1.

[0147] In operation S42, an input image may be extracted from the inspection image. For example, a plurality of regions of interest may be set on the target substrate, and input images respectively corresponding to the regions of interest may be extracted from the dark field image.

[0148] In operation S43, a feature map may be generated by performing a CNN operation on each of the input images. Feature maps may be generated for all input images so that defects may be checked for the entire area of ​​the target substrate. Fig.13 A method for generating feature maps by performing CNN operations on an input image is described.

[0149] In operation S44, a statistical model of the feature map may be used to determine the statistical distances of the feature vectors corresponding to the plane positions of the feature map, respectively. According to some embodiments, the statistical distance may be determined as the Mahalanobis distance. The Mahalanobis distance may be a value indicating how many times the value of the variable differs from the mean value and the normal distribution of the variable.

[0150] According to some embodiments, based on the average value of each feature of the feature vector at the plane position of the feature map and the covariance matrix of the feature vector at the plane position, the Mahalanobis distance of the feature vector at the plane position can be determined. The Mahalanobis distance of the plane position can numerically represent the difficulty of statistically generating the feature value of the feature vector at the plane position in the feature map.

[0151] In operation S45 , an inspection image may be constructed based on statistical distances of feature vectors corresponding to the plane positions, and defects in the region of interest may be detected based on the inspection image.

[0152] Fig.18 is a diagram showing the Mahalanobis distance.

[0153] As described above, the plane positions of the feature map may correspond to the feature vectors included for each channel. Fig.18 The graph of may have a first channel C1 axis and a second channel C2 axis among a plurality of channels included in the feature graph. In the above graph, the feature vector is shown according to the first channel C1 component and the second channel C2 component of the feature vector corresponding to the plane position.

[0154] The eigenvectors of the plane positions can form a constant distribution. Fig.18 In the curve graph, a first distribution DP1 of the feature vector corresponding to the first position, a second distribution DP2 of the feature vector corresponding to the second position, and a third distribution DP3 of the feature vector corresponding to the third position are shown.

[0155] As the eigenvector for a particular plane position moves further away from the distribution of eigenvectors, the eigenvalues ​​included in the eigenvector may be values ​​that are statistically less likely to occur.The Mahalanobis distance may be calculated to determine how statistically unlikely any eigenvector is.

[0156] In the feature map generated from the target image, the feature vector for a specific plane position can be determined according to the following equation 2 The Mahalanobis distance.

[0157] [Equation 2]

[0158]

[0159] In equation 2, It can indicate the feature vector The mean vector of , and Σ can indicate the eigenvector The covariance matrix of . Fig.16 In the example, the feature vector of the plane position (1, 1) Features F1, F2 and F3 may be included.

[0160] Based on the Mahalanobis distance of the feature vectors for the plane positions of the feature map, an inspection image may be generated. The inspection image may have a width W and a height H equal to the width and height of the feature map, and the plane positions of the inspection image may include the Mahalanobis distances of the feature vectors corresponding to the plane positions of the feature map, respectively.

[0161] The inspection image may correspond to a region of interest ROI. According to some embodiments, it may be determined that a defect having a value greater than or equal to a predetermined size exists in a plane position in the inspection image.

[0162] 19A to 19D are diagrams showing various input images and inspection images and binary images extracted based on the input images.

[0163] 19A to 19D An inspection image generated by applying a statistical model to an input image having various patterns and a binary image obtained by binarizing the inspection image based on a predetermined threshold value are shown.

[0164] Reference Fig.19A , the portion with the pattern and the portion with the scratch in the input image may have similar illumination, and the portion with the scratch may look less obvious compared to the portion with the pattern.

[0165] In the inspection image generated by applying the statistical model, scratches that are statistically less likely to appear can be highlighted compared to patterns that are statistically frequently appearing.

[0166] In a binary image generated based on an inspection image, the position of a plane having a feature vector whose Mahalanobis distance is greater than or equal to a threshold value and the position of a plane having a feature vector whose Mahalanobis distance is less than the threshold value in the inspection image can be displayed as different values. Fig.19A In the example above, white portions of the binary image may indicate scratches.

[0167] The learning images generated from substrates S with various patterns can be used to generate statistical models. Fig.19A The pattern of the input image is different from the pattern of FIG. 19B to FIG. 19D In the input image of , an inspection image in which scratches are highlighted can be generated. For example, the statistical model according to some embodiments can be applied to detect defects of a substrate S having various patterns.

[0168] The optical metrology apparatus according to some embodiments can generate an original image in which a dark field image and a bright field image are integrated during the transfer of the substrate. Therefore, bright field inspection and dark field inspection can be performed without requiring an additional period of time for generating the original image. In addition, a decrease in productivity due to the time required for bright field inspection and dark field inspection can be prevented.

[0169] A substrate processing apparatus according to some embodiments may include an optical metrology apparatus. The optical metrology apparatus for bright field inspection and the optical metrology apparatus for dark field inspection may be integrated to spatially efficiently arrange the optical metrology apparatus on a transport path of the substrate processing apparatus.

[0170] The optical inspection method according to some embodiments may correct the distortion of the shape of a multi-channel image generated during the transfer of a substrate, and thus may detect defects in the substrate using features extracted from the corrected image using a neural network operation.

[0171] Finally, since substrates processed by a substrate processing apparatus can be thoroughly inspected without reducing productivity and substrates having various defects can be removed in advance, electrical characteristics and yield of semiconductor devices manufactured from the substrates can also be improved.

[0172] Problems to be solved by the present inventive concept are not limited to the above-mentioned problems, and other problems not mentioned will be clearly understood from the above description by those skilled in the art.

[0173] While exemplary embodiments have been illustrated and described above, it will be apparent to those skilled in the art that modifications and variations may be made without departing from the scope of the inventive concept as defined by the appended claims.

Claims

1. An optical metrology device, include: an illumination unit configured to simultaneously irradiate a first illumination light and a second illumination light onto a surface of the substrate, the first illumination light being irradiated at a first incident angle whose difference from a measurement angle is greater than a critical angle, the second illumination light being irradiated at a second incident angle whose difference from the measurement angle is equal to or less than the critical angle, the wavelength of the second illumination light being different from the wavelength of the first illumination light; an optical system configured to collect reflected light from the surface of the substrate according to the first illumination light and the second illumination light; as well as A multi-channel camera is configured to generate a raw image in which a dark field image and a bright field image of the surface of the substrate are integrated based on the reflected light collected by the optical system.

2. The optical metrology device according to claim 1, in, The first illumination light is red light, and the second illumination light includes green light and blue light, and The multi-channel camera includes red pixels, green pixels, and blue pixels, and is configured to generate a dark field image having a scattered light component according to the first illumination light using the red pixels, and to generate a bright field image having a directly reflected light component according to the second illumination light using the green pixels and the blue pixels.

3. The optical metrology device according to claim 1, in, The wavelength of the first illumination light is 620 nm to 630 nm, and the wavelength of the second illumination light is 580 nm or less.

4. The optical metrology device according to claim 1, in, The lighting unit comprises: a first lighting unit configured to generate red light using red light emitting diode lighting, and A second lighting unit is configured to apply a short pass filter to the white light emitting diode lighting to generate green light and blue light.

5. The optical metrology device according to claim 1, in, The reflected light includes scattered light and directly reflected light, and The critical angle is determined by an angle at which the illuminance of the scattered light collected in the direction of the measurement angle is equal to the illuminance of the directly reflected light.

6. The optical metrology device of claim 1, wherein the critical angle is 4 degrees.

7. The optical metrology device according to claim 1, in, The second incident angle is equal to the measurement angle.

8. An optical metrology device, include: an illumination unit configured to irradiate first illumination light and second illumination light having different wavelengths onto a surface of the substrate moving along the transport path at different incident angles; as well as a multi-channel camera configured to generate a raw image in which a dark field image based on scattered light according to the first illumination light and a bright field image based on directly reflected light according to the second illumination light are integrated, Wherein, the optical metrology device is located on the conveying path of the substrate included in the substrate processing device.

9. The optical metrology device according to claim 8, in, The multi-channel camera includes a line scan camera, and The line scan camera is configured to photograph a fixed area facing a moving substrate multiple times to acquire a local image of the surface of the substrate, and perform a scanning operation to reconstruct the local image into a two-dimensional original image.

10. The optical metrology device according to claim 9, in, The fixing region extends in a direction perpendicular to a conveying direction of the substrate.

11. The optical metrology device according to claim 10, in, The first illumination light is red light, and the second illumination light includes green light and blue light, and The line scan camera includes red pixels, green pixels, and blue pixels respectively arranged in a linear manner, wherein the red pixels generate the dark field image, and the green pixels and the blue pixels generate the bright field image.

12. The optical metrology device according to claim 8, in, The optical metrology device is mounted on a surface or a wall above an exit of the substrate processing device from which the substrate is carried to a transfer container of the substrate processing device.

13. An optical metrology device, include: an illumination unit configured to irradiate first illumination light and second illumination light having different wavelengths onto a surface of the substrate moving along the transport path at different incident angles; a multi-channel camera configured to acquire scattered light of the first illumination light and directly reflected light of the second illumination light to generate an original image; as well as An inspection device is configured to correct the shape of the original image to generate a corrected image, separate the corrected image according to color channels to generate a dark field image having illumination information of the scattered light and a bright field image having illumination information of the directly reflected light, and analyze the dark field image and the bright field image to inspect defects on the surface of the substrate.

14. The optical metrology device according to claim 13, in, The inspection device corrects the shape of the original image to generate the corrected image by: sensing a degree of distortion of the original image based on a shape of the original image, and The coordinate values ​​corresponding to the conveying direction of the original image are adjusted to generate the corrected image having the shape of the substrate.

15. The optical metrology device according to claim 13, in, The inspection device separates the corrected image according to the color channel to generate a dark field image having the illumination information of the scattered light and a bright field image having the illumination information of the directly reflected light by the following operation: Separating the corrected image into a red channel image, a green channel image and a blue channel image, determining the red channel image as the dark field image, and The green channel image and the blue channel image are merged to generate the bright field image.

16. The optical metrology device according to claim 13, in, The inspection device analyzes the dark field image and the bright field image to inspect defects on the surface of the substrate by: detecting a color change from the bright field image, and Scratches are detected from the dark field image.

17. The optical metrology device according to claim 16, in, The inspection device detects scratches from the dark field image by: dividing the dark field image into a plurality of input images, generating a feature map for each of the plurality of input images using a convolutional neural network operation, By applying the feature map to a statistical model, a statistical distance of a feature vector including features of each channel respectively corresponding to a plane position of the feature map is calculated, generating an inspection image having information about the statistical distance of the feature vectors at each of the planar positions, and A plane position where the statistical distance of the feature vector in the inspection image is equal to or greater than a threshold is detected as a position where a scratch exists.

18. The optical metrology device according to claim 17, in, The inspection device calculates the statistical distance of the feature vector by the following operations: The Mahalanobis distance is calculated based on the feature vector corresponding to the plane position, the mean vector corresponding to the plane position in the statistical model, and the covariance matrix.

19. The optical metrology device according to claim 17, in, The inspection device is also configured to: Acquire dark field images obtained from a plurality of substrates having different patterns as learning images, extracting an input image for learning from the learning images, performing a convolutional neural network operation on each of the input images for learning to generate a feature map for learning, and An average vector of feature vectors corresponding to plane positions in the feature map for learning and a covariance matrix of the feature vectors are generated as a statistical model.

20. The optical metrology device according to claim 19, in, The learning image includes an image obtained from a substrate that does not contain defects.

Images