KR20200047169A - Substrate inspection apparatus including scanning function - Google Patents

Abstract

According to an embodiment of the present invention, a substrate inspection device including a scanning function comprises: a carrier for seating a substrate; an inspection unit spaced apart from the carrier and restoring three-dimensional information on the surface of the substrate to inspect for abnormality of the substrate wherein the inspection unit scans and inspects the surface of the substrate along a predetermined scan path; and a light source unit which emits single wavelength light to provide the same to the inspection unit. The inspection unit includes a holographic restoration device. The holographic restoration device includes: a collimator for collimating the single wavelength light emitted from the light source unit; a light splitter for splitting the single wavelength light, which has passed through the collimator, into object light and reference light; an object light objective lens for passing the object light divided by the light splitter therethrough; an optical mirror for reflecting the reference light divided by the light splitter; an image sensor which records an interference fringe formed when the object light passing through the object light objective lens and reflected from the surface of the substrate and the reference light reflected by the optical mirror are transmitted to the light splitter, respectively; and a processor for receiving and storing an image including intensity information of an object hologram generated by converting the interference fringe in the image sensor, and generating three-dimensional shape information of the substrate surface. The substrate inspection device including a scanning function can accurately generate three-dimensional shape information of a substrate, which is an object to be measured, only by obtaining one hologram.

Description

Substrate inspection apparatus including scanning function

The present invention relates to a substrate inspection apparatus including a scanning function. More specifically, the present invention generates a substrate by generating three-dimensional shape information of a substrate from an image including intensity information of an object hologram generated by interference of the reference light reflected from the optical mirror and the object light reflected from the substrate or transmitted through the substrate. It relates to a device for inspecting the surface for defects.

A digital holography microscope refers to a microscope that acquires the shape of an object using digital holography technology.

If a general microscope is a device that acquires the shape of an object by acquiring reflected light reflected from the object, a digital holography microscope acquires interference light and / or diffracted light generated by the object, and acquires the shape of the object therefrom to be.

A digital holography microscope uses a laser that generates light of a single wavelength as a light source, and uses a light splitter to divide the light generated by the laser into two lights. At this time, one light (hereinafter referred to as reference light) is directed to the image sensor, and the other light (hereinafter referred to as object light) is reflected from the target object and directed to the above-described image sensor so that interference between the reference light and the object light occurs. do.

The image sensor may record the interference fringe according to the interference phenomenon as a digital image, and restore the 3D shape of the object to be measured from the recorded interference fringe. At this time, the interference fringe recorded by the image sensor is usually referred to as a hologram.

Conventional optical holography microscopy records the interference pattern according to the interference phenomenon between the reference light and the object light as a special film. At this time, when the reference light is irradiated to the special film on which the interference fringe is recorded, the shape of the virtual object to be measured is restored where the object to be measured is located.

Compared with the conventional optical holography microscope, the digital holography microscope digitizes (or digitizes) the interference pattern of light through an image sensor, and restores the shape of the object to be measured through electronic calculation rather than an optical method. There is a difference.

On the other hand, the conventional digital holography microscope using a single wavelength laser light source has a problem that the minimum unit length of the measurement of the object is limited to the wavelength length of the laser. In order to compensate for this, another conventional digital holography microscope using a laser light source having two or more wavelengths has a problem in that it is impossible to obtain a three-dimensional shape of an object in real time, as well as a high manufacturing cost of the microscope.

In addition, the above-mentioned conventional digital holography microscopes generate a computer generated hologram (CGH) with a computer to restore the shape of the object to be measured, and display it on a spatial light modulator (SLM), and display the A 3D hologram image of the object was obtained by illuminating the reference light. However, this method not only requires the use of an expensive spatial light modulator (SLM), but also has a clear technical limitation by simply digitizing a special film in the above-described optical holography microscope.

In order to solve the problems of the conventional digital holography microscopes, for example, Republic of Korea Patent Publication No. 10-2016-0029606 (hereinafter referred to as "published prior art") proposes a digital holography microscope and a digital hologram image generation method. Hereinafter, the disclosed prior art will be briefly described.

1 is a block diagram showing in detail a two-wavelength digital holography microscope device according to the prior art.

Referring to FIG. 1, the conventional two-wavelength digital holography microscope device includes a mixed light source unit 10, a wavelength division unit 20, an interference pattern acquisition unit 30, an objective unit 40, and an image sensor unit 50, It includes an image storage unit 60, a control unit 70, and an object shape restoration unit 80.

The mixed light source unit 10 includes a mixed light source emitting unit 11 and a light source unit lens 12. The mixed light source light emitting unit 11 emits mixed light having a wavelength band distributed in several non-uniform bands. The light source unit lens 12 optically adjusts the mixed light generated by the mixed light source emitting unit 11 and makes it incident on the wavelength division unit 20.

The wavelength division unit 20 includes a first light splitter 21 and a first filter plate 22 and a second filter plate 23 and a first reflector 24. The first light splitter 21 receives the mixed light incident from the mixed light source unit 10 and divides it into two lights. At this time, the first light splitter 21 serves to divide the incident mixed light in different directions and proceed. The first filter plate 22 receives one of the light split by the first light splitter 21 to obtain a first light beam having a predetermined single wavelength. Here, the light input to the first filter plate 22 is filtered while passing through the first filter plate 22, and a first ray having a single wavelength determined according to the characteristics of the first filter plate 22 is obtained. The second filter plate 23 receives the other one of the light split by the first light splitter 21 in the same manner as the first filter plate 22, and has a second wavelength different from that of the first filter Acquire rays. And the second ray is sent to the interference pattern acquisition unit 30. The first reflector 24 serves to receive the first light obtained from the first filter plate 22 and reflect it to the interference pattern acquisition unit 30.

The interference pattern acquisition unit 30 includes a second light splitter 31, a third light splitter 32, a second reflector 33, a third filter plate 34, and a third reflector 35. The second optical splitter 31 receives the first light input from the wavelength division unit 20 and divides the first light into a first object light and a first reference light. At this time, the second light splitter 31 serves to divide the incident first light beam in different directions and proceed. The third light splitter 32 receives the second light beam in the same manner as the second light splitter 31 and divides it into a second object light and a second reference light. The second reflector 33 receives the first reference light, and sends the reflected first light to the second light splitter 31. The third filter plate 34 may receive the first reference light divided by the second light splitter 31 and send it to the second reflector 33, and receive the reflected first reflection reference light and send it to the second light splitter. In addition, the third filter plate 34 prevents the second object light from reaching the second reflector 33 when the second light splitter 31 reaches the second light splitter 31 and the light is split into a second reflector 33. . To this end, the third filter plate 34 is a filter plate having the same characteristics as the first filter plate 22 in transmitting light. The third reflector 35 receives the second reference light, and sends the reflected second reference light to the third light splitter 32, wherein the second reflector 33 and the third reflector 35 are the control unit 70 ) Can be configured to adjust the angle under control, so that an off-axis hologram can be implemented.

On the other hand, the first object light and the second object light obtained as described above are converted to each of the first reflection object light and the second reflection object light through the following process and are sent to the image sensor unit 50. The second optical splitter 31 injects the first object light divided as described above into an object to be measured mounted on the objective unit 40, and further transmits the second object divided from the third optical splitter 32 Light is incident on the object to be measured. In this case, the reflected light reflecting the first object light incident from the object to be measured is referred to as the first reflected object light. In addition, the reflected light reflecting the second object light incident from the object to be measured is referred to as the second reflected object light. The second light splitter 31 receives the first reflected object light and the second reflected object light reflected as described above and sends them to the third light splitter 32. The third light splitter 32 sends the first reflected object light and the second reflected object light received as described above to the image sensor unit 50 again.

In addition, the first reflection reference light and the second reflection reference light obtained as described above are sent to the image sensor unit 50 through the following process. Specifically, the second light splitter 31 receives the first reflection reference light reflected from the second reflector 33 and sends it to the third light splitter 32. As described above, the third light splitter 32 receives the first reflection reference light sent from the second light splitter 31 and the second reflection reference light reflected from the third reflector 35, and then receives the image sensor unit 50 again. To send. Accordingly, the first light reflection object light, the first reflection reference light, the second reflection object light, and the second reflection reference light are both sent in the same direction to the image sensor unit 50 in the third light splitter 32, and then interfere with each other. Interference patterns are generated.

On the other hand, the second reflector 33 and the third reflector 35 are angled under the control of the control unit 70 in order to construct an off-axis system in which light rays of different wavelengths form different interference patterns. Characterized in that it can be adjusted in multiple directions. That is, as the angles of the second reflector 33 and the third reflector 35 are different from each other, the first reflecting reference light reflected from the second reflector 33 and the second reference reflecting from the third reflector 35 When a distance occurs in the direction of the light, the first reflection reference light and the second reflection reference light are combined with the first reflection object light and the second reflection object light reaching the image sensor unit 50 to form an interference pattern, each wavelength Very differently disjointed interference patterns are formed.

The objective part 40 includes an object holder 41 and an objective lens 42. The object holder 41 is fixed to the object to be measured to be measured, and the objective lens 42 optically adjusts the first object light and the second object light incident on the object to be measured.

The image sensor unit 50 projects the interference pattern obtained from the interference pattern acquisition unit 30 onto a digital image sensor, measures the projected interference pattern using the digital image sensor, and disperses the measured value. Convert to Usually, the interference fringe is recorded as a hologram. As the digital image sensor, various image sensors such as a CCD may be used.

The image storage unit 60 stores the interference fringe information converted from the image sensor unit 50 into discrete signals in various storage media such as a memory or a disk device.

The control unit 70 implements the above-described off-axis system and adjusts the position and angle of the second reflector 33 and the third reflector 35 to obtain an interference pattern, such as the interference pattern acquisition unit 30 ) To control the object 40, such as adjusting the objective lens 42 to control the first object light and the second object light incident on the object to be measured, the interference fringes are measured and The image sensor unit 50 is controlled to allow information to be converted into a discrete signal, and the image storage unit 60 is controlled to store interference fringe information converted into a discrete signal.

The object shape restoration unit 80 includes a phase information acquisition unit 81, a thickness information acquisition unit 82, and a shape restoration unit 83. The phase information acquisition unit 81 acquires the phase information of the interference pattern for the first ray and the interference pattern for the second ray by using the interference pattern information, respectively, and the thickness information acquisition unit 82 Obtains the thickness information of the object to be measured using the phase information, and the shape restoration unit 83 restores the real-time three-dimensional shape of the object to be measured using the thickness information. At this time, the thickness information of the object to be measured includes difference information between the paths of the object light and the reference light. Due to the optical path difference between the object light and the reference light, the interference pattern is formed when the object light and the reference light overlap.

According to the disclosed prior art including the above, it is possible to improve measurement resolution and secure real-time image acquisition, but still has the following problems.

Since the conventionally disclosed prior art uses a mixed light source having a wavelength band distributed in several bands, the wavelength division unit 20 divides first and second light sources having different wavelengths to obtain at least two single wavelengths. To do so, the first filter plate 22, the second filter plate 23, and the first reflector 24 must be used.

In addition, the interference pattern acquisition unit 30, a third light splitter 32 for dividing the second light source, a third reflector 35 for reflecting the second light source, and a second light source for the second reflector 33 The third filter plate 34 to block the incident light to be additionally used.

Therefore, the structure of the microscope is complicated, and this entails various problems such as an increase in manufacturing cost and an increase in design complexity. Therefore, a new method is needed to solve the above-mentioned problems while using a single wavelength light source.

Republic of Korea Patent Publication No. 10-2016-0029606 Republic of Korea Patent Publication No. 10-2010-0095302 Republic of Korea Patent Publication No. 10-2012-0014355 Republic of Korea Patent No. 10-1139178 Republic of Korea Patent No. 10-1441245 U.S. Patent No. 7,649,160

The present invention is to solve the problems of the prior art as described above, it is intended to accurately generate the three-dimensional shape information of the surface of the substrate by the acquisition of only one hologram.

In particular, the present invention is to generate information about the reference light and the curvature aberration information of the object light objective lens from one hologram and to correct the object hologram obtained by taking this into consideration to generate three-dimensional shape information of the substrate with improved accuracy.

In addition, the present invention seeks to solve the complex optical device structure and thus significant high cost problems.

Furthermore, the present invention seeks to detect defects of these structures with a high probability by accurately acquiring three-dimensional shapes of ultra-fine structures such as TFTs and semiconductors.

According to an embodiment of the present invention, a carrier for mounting a substrate, spaced apart from the carrier by a predetermined distance, restores three-dimensional information on the surface of the substrate to check for abnormality of the substrate, and follows the preset scan path. And an inspection unit that scans and inspects the surface of the light source and emits a single wavelength light to the inspection unit, the inspection unit includes a holographic restoration device, and the holographic restoration device is emitted from the light source unit. A collimator for collimating single wavelength light, an optical splitter for dividing the single wavelength light that has passed through the collimator into object light and reference light, an object light objective lens for passing the object light divided by the light splitter, and the light splitter An optical mirror reflecting the reference light divided by the image, passing through the object light objective lens An image sensor for recording an interference fringe formed by transmitting the object light reflected from the surface of the substrate and the reference light reflected by the optical mirror to the optical splitter, and the object hologram generated by converting the interference fringe from the image sensor. Provided is a substrate inspection apparatus including a scanning function, including a processor for receiving and storing an image including intensity information and generating three-dimensional shape information of the substrate surface.

In one embodiment of the present invention, the inspection unit may include a plurality of holographic restoration devices arranged in a line along the first direction.

In one embodiment of the present invention, the light source unit may include a plurality of light sources corresponding to the number of holographic restoration devices.

In one embodiment of the present invention, the light source unit is composed of a single light source, and the inspection unit further includes a light distribution unit that uniformly distributes the single wavelength light provided from the light source unit to the plurality of holographic restoration devices. can do.

In one embodiment of the present invention, an optical fiber for transmitting the single wavelength light emitted from the light source unit to the inspection unit may be further provided.

In one embodiment of the present invention, the processor extracts a real image component corresponding to a real image among at least one frequency component included in the image, and is paired with the reference light based on the real image components ( conjugate) to generate a real image hologram including the corrected light in relation to the real image information on the surface of the substrate, and based on the corrected light, generate an intermediate hologram from which the information of the reference light is removed from the real image hologram, and the intermediate hologram After generating the curvature aberration correction information from the, based on the curvature aberration correction information, to generate a correction hologram with an improved error due to curvature aberration in the intermediate hologram, the three-dimensional shape information of the substrate surface from the correction hologram Can generate

Other aspects, features, and advantages other than those described above will become apparent from the following detailed description, claims and drawings for carrying out the invention.

According to one embodiment of the present invention made as described above, it is possible to accurately generate three-dimensional shape information of a substrate as an object to be measured by acquiring only one hologram.

In particular, by generating information about the reference light and curvature aberration information of the object light objective lens from one hologram and correcting the obtained object hologram in consideration of this, it is possible to generate three-dimensional shape information of an object to be measured with improved accuracy.

In addition, it is possible to solve the complicated optical device structure and thus a considerable high cost problem.

Furthermore, by accurately acquiring the three-dimensional shape of ultra-fine structures such as TFTs and semiconductors, defects in these structures can be detected with a high probability.

Of course, the scope of the present invention is not limited by these effects.

1 is a block diagram showing in detail a two-wavelength digital holography microscope device according to the disclosed prior art.
2 is a view schematically showing a substrate inspection apparatus according to an embodiment of the present invention.
3A is a block diagram showing a schematic configuration of a holographic reconstruction apparatus according to a first embodiment of the present invention.
3B is a block diagram showing a schematic configuration of a holographic reconstruction apparatus according to a second embodiment of the present invention.
4 is a view schematically showing a substrate inspection apparatus according to another embodiment of the present invention.
5 is a block diagram illustrating a light distribution unit of a substrate inspection apparatus according to another embodiment of the present invention.
6A and 6B are views for explaining the appearance of an exemplary measurement object.
7 is an example of an image of a portion of an object to be measured.
FIG. 8 is a diagram showing frequency components of an image of a part of the measurement target object illustrated in FIG. 7.
9 is a diagram for explaining a method of extracting frequency components corresponding to a real image from the frequency components illustrated in FIG. 8.
10A is a diagram showing the intensity of digital reference light.
10B is a diagram showing the phase of the reference light.
10C is a diagram showing the intensity of the correction light.
10D is a diagram showing the phase of the correction light.
11 is a diagram illustrating an exemplary actual hologram.
12 and 13 are diagrams for describing a method in which a processor according to an embodiment of the present invention determines a curvature aberration correction term from an intermediate hologram.
14 is a diagram showing an example of a three-dimensional shape of a measurement object generated from a hologram.
15 is a flowchart illustrating a method of generating 3D shape information of an object to be measured performed by a holographic reconstruction apparatus according to an embodiment of the present invention.
16 and 17 are block diagrams of a processor according to embodiments of the present invention.
18, 19, and 20 are flowcharts of a method of detecting a defect according to embodiments of the present invention.
21 is a diagram illustrating an embodiment of a machine learning unit and an image processing unit.
22 is a block diagram of a machine learning unit according to an embodiment.
23 is a diagram showing a connection relationship of a defect detection algorithm modeled through a machine learning unit.

Hereinafter, various embodiments of the present disclosure are described in connection with the accompanying drawings. Various embodiments of the present disclosure may have various modifications and various embodiments, and specific embodiments are illustrated in the drawings and related detailed descriptions are described. However, this is not intended to limit the various embodiments of the present disclosure to specific embodiments, and should be understood to include all modifications and / or equivalents or substitutes included in the spirit and scope of the various embodiments of the present disclosure. In connection with the description of the drawings, similar reference numerals have been used for similar elements.

Expressions such as “include” or “may include” that may be used in various embodiments of the present disclosure indicate the existence of a corresponding function, operation, or component disclosed, and additional one or more functions, operations, or The components and the like are not limited. Also, in various embodiments of the present disclosure, terms such as “include” or “have” are intended to indicate that there are features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, It should be understood that one or more other features or numbers, steps, actions, components, parts, or combinations thereof are not excluded in advance.

In various embodiments of the present disclosure, expressions such as “or” include any and all combinations of words listed together. For example, "A or B" may include A, may include B, or may include both A and B.

Expressions such as “first”, “second”, “first”, or “second” used in various embodiments of the present disclosure may modify various elements of various embodiments, but do not limit the elements. Does not. For example, the above expressions do not limit the order and / or importance of the components. The above expressions can be used to distinguish one component from another component. For example, the first user device and the second user device are both user devices and represent different user devices. For example, a first component may be referred to as a second component without departing from the scope of rights of various embodiments of the present disclosure, and similarly, the second component may also be referred to as the first component.

When a component is said to be "connected" to or "connected" to another component, some of the components may or may not be directly connected to the other components, It will be understood that other new components may exist between the other components. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it will be understood that no new component exists between the component and the other components. You should be able to.

Terms used in various embodiments of the present disclosure are only used to describe specific specific embodiments, and are not intended to limit various embodiments of the present disclosure. Singular expressions include plural expressions unless the context clearly indicates otherwise.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person skilled in the art to which various embodiments of the present disclosure belong.

Terms, such as those defined in a commonly used dictionary, should be interpreted as having meanings consistent with meanings in the context of related technologies, and are ideally or excessively formal unless explicitly defined in various embodiments of the present disclosure. It is not interpreted as meaning.

2 is a view schematically showing a substrate inspection apparatus 1 according to an embodiment of the present invention.

Referring to FIG. 2, the substrate inspection apparatus 1 according to an embodiment of the present invention may include a carrier 100, an inspection unit 1000, and a light source unit 310 (see FIG. 3A).

The carrier 100 may seat the substrate M on one surface. The carrier 100 performs a function of bringing in / out the deposition chamber for depositing material on the substrate M or moving the substrate M between the deposition chambers while the substrate M is fixed.

The inspection unit 1000 is spaced a predetermined distance from the carrier 100 to restore the three-dimensional information on the surface of the substrate M to inspect the substrate M for anomalies, but the substrate M along a predetermined scan path. Can be inspected by scanning the surface.

In this case, the inspection unit 1000 may include a holographic restoration device 300 to be described later. The inspection unit 1000 according to an embodiment may include one holographic restoration apparatus 300. Since there is a limit to the size of an image that can be obtained through the holographic restoration apparatus 300, specifically, the image sensor 380, there is a limit to inspect the large area substrate M. Therefore, the substrate inspection apparatus 1 according to an embodiment of the present invention includes a scanning function, so that the substrate M of a large area can be inspected.

The holographic reconstruction apparatus 300 may inspect the surface of the substrate M corresponding to the unit area UA with one measurement. Here, the unit area UA is formed in a rectangular shape, and the size of the unit area UA may be x mm by y mm. For example, the size of the unit area UA may be 15 mm by 15 mm. However, this is only one embodiment, and the technical spirit of the present invention is not limited thereto. The unit area UA can accurately divide the surface of the substrate M having a constant area in the x-y plane direction into n by m pieces (n, m is an integer). However, since the unit area UA corresponds to a performance capable of being photographed at a time by the holographic restoration apparatus 300, it may not correspond to an area multiple of the area of the substrate M.

In this case, the holographic reconstruction apparatus 300 may inspect the entire surface of the substrate M by scanning a part of the unit area UA by overlapping and scanning. The image obtained by the holographic reconstruction apparatus 300 may generate speckle noise in an area adjacent to the edge due to coherence of the laser. Therefore, even if an image is acquired for the unit area UA, an effective area for obtaining 3D shape information may be smaller than the unit area UA. The inspection unit 1000 may set a scan path to accurately generate 3D shape information on the entire substrate M surface using the area of the effective area and the area of the substrate M.

The inspection unit 1000 may further include a first driver 1010 that moves the holographic restoration apparatus 300 along a preset scan path. At this time, the carrier 100 may be fixed to a certain position after transferring the substrate M to face the inspection unit 1000. Although the inspection unit 1000 is not illustrated, the holographic restoration apparatus 300 may be positioned in the initial measurement area on the substrate M, including a detection unit such as a vision camera that checks the position of the substrate M. Thereafter, the inspection unit 1000 sets a scan path using information including the dimensions of the substrate M, which is already known, and moves the holographic restoration apparatus 300 along the preset scan path to perform the inspection. You can.

As another embodiment, the holographic restoration apparatus 300 described above may be fixed in a state spaced apart from the carrier 100 or the surface of the substrate M by a predetermined distance. At this time, the second driver 110 connected to the carrier 100 may move the carrier 100 so that the carrier 100 can be measured along a preset scan path.

3A is a block diagram showing a schematic configuration of a holographic reconstruction apparatus 300A according to a first embodiment of the present invention.

In the present invention, the 'holographic reconstruction device' may mean a device that acquires a hologram (hereinafter, referred to as 'object hologram') for an object to be measured and analyzes and / or displays the obtained object hologram.

For example, the holographic restoring apparatus 300A may be a device that is disposed on a semiconductor manufacturing line, obtains an object hologram of the produced semiconductor, and determines whether the semiconductor is integrity from the obtained object hologram. However, this is exemplary and the spirit of the present invention is not limited thereto. The object to be measured can be any object having a three-dimensional shape, but in this specification, the object to be measured is a substrate. In addition, in this specification, an object to be measured and a substrate may be used in combination.

On the other hand, in the present invention, 'object hologram (Hologram)' is a hologram that can be generated from an image obtained by the holographic reconstruction apparatus (300A), may mean a hologram before various processing by the holographic reconstruction apparatus (300A) is made. have. Detailed description thereof will be described later.

Referring to FIG. 3A, the holographic reconstruction apparatus 300A according to the first embodiment of the present invention includes a collimator 320 for collimating single wavelength light emitted from the light source unit 310 and a single wavelength passing through the collimator 320 An optical splitter 330 for dividing light into object light O and reference light R, an object light objective lens 340 for passing object light O divided by light splitter 330, and an optical splitter 330 A reference light objective lens 360 passing the reference light R divided by), an optical mirror 370 reflecting the reference light R passing through the reference light objective lens 360, and an object light objective lens 340 Thus, the object light O reflected from the surface of the substrate M and the reference light R reflected by the optical mirror 370 pass through the object light objective lens 340 and the reference light objective lens 360, respectively. The image acquired by the image sensor 380 and the image sensor 380 which is a recording medium for recording the image formed by being transferred to the 330 It may include a processor 390 to process. Here, the light source unit 310 may emit a single wavelength light and provide it to the holographic reconstruction apparatus 300A.

Meanwhile, the holographic reconstruction apparatus 300A according to the first embodiment of the present invention may further include a reference light objective lens 360 through which the reference light R divided by the light splitter 330 passes.

At this time, the processor 390 may generate 3D information on the surface of the substrate M from the image acquired by the image sensor 380. The detailed description of the operation of the processor 390 will be described later.

3B is a block diagram showing a schematic configuration of a holographic reconstruction apparatus 300B according to a second embodiment of the present invention.

Referring to FIG. 3B, the holographic reconstruction apparatus 300B according to the second embodiment of the present invention includes a collimator 320 for collimating single wavelength light emitted from the light source unit 310 and a single wavelength passing through the collimator 320 The surface of the substrate M after the light splitter 330 dividing the light into the object light O and the reference light R, and the object light O divided by the light splitter 330 passes through the substrate M The object light objective lens 340 passing the object transmitted light T including information, the second optical mirror 372 reflecting the object transmitted light T passing through the object light objective lens 340, and the light splitter 330 To the first optical mirror 370 and the first optical mirror 370 reflecting the reference light R passing through the reference light objective lens 360 and the reference light objective lens 360 passing through the reference light R divided by The second light splitter 332 and the second light splitter 332 through which the reference light R reflected by the object and the object transmitted light T reflected by the second optical mirror 372 are transmitted, respectively. It may include an image sensor 380 for recording the image formed by the reference light (R) and the object light transmitted light (T) transmitted to the processor 390 to process the image acquired by the image sensor 380. .

Of course, even in the second embodiment, the processor 390 may generate 3D information on the surface of the substrate M from the image acquired by the image sensor 380. The detailed description of the operation of the processor 390 will be described later.

3A and 3B, the holographic reconstruction apparatus 300A according to the first embodiment of the present invention and the holographic reconstruction apparatus 300B according to the second embodiment of the present invention respectively measure the object light O The fact that the object M is reflected (the embodiment of FIG. 3A) or the object light O transmits the object M to be measured (the embodiment of FIG. 3B) and some components thereof (e.g., It has substantially the same configuration except for the additional use of the second optical mirror 372 and the second light splitter 332 of the embodiment of FIG. 3B and the placement of some components accordingly).

In particular, it should be noted that the image is acquired by the image sensor 380, and the processor 390 has the same characteristic in that it generates the reference light R from the acquired image.

Hereinafter, the holographic reconstruction apparatuses 300A and 300B according to the first and second embodiments of the present invention will be collectively described as the holographic reconstruction apparatus 300.

Meanwhile, the processor 390 of the holographic restoring apparatus 300 according to an embodiment of the present invention may include all kinds of apparatus capable of processing data. For example, the processor 390 may refer to a data processing device embedded in hardware having physically structured circuits to perform functions represented by codes or instructions included in a program.

As an example of such a data processing device embedded in hardware, a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, and an application-specific integrated (ASIC) Circuit), FPGA (Field Programmable Gate Array), and the like, but the scope of the present invention is not limited thereto.

In addition, the image sensor 380 according to an embodiment of the present invention may be implemented with at least one image sensor, such as a Charge Coupled Device (CCD), a Complimentary Metal-Oxide Semiconductor (CMOS), for example.

Meanwhile, the light source unit 310 may be disposed at a position physically spaced from the holography restoration apparatus 300. For example, the inspection unit 1000 is disposed inside a deposition chamber or a separately configured inspection chamber, and the light source unit 310 provides a single wavelength light to the inspection unit 1000 while being disposed outside the above-described chambers. can do. In this case, the inspection unit 1000 may further include an optical transmitter 311 for transmitting light of a single wavelength provided from the light source unit 310 to the holographic restoration apparatus 300.

The holographic restoring apparatus 300 is an optical system that forms paths of the object light O and the reference light R for generating an object hologram of the substrate M, which is an object to be measured, to generate accurate 3D shape information. The system is constructed with the distance between each component set in advance.

The light transmitter 311 is a configuration for increasing the positional freedom of the inspection unit 1000, and the light of a single wavelength provided from the light source unit 310 is separated from the light source unit 310 by the inspection unit 1000. It can perform the transfer function. The light transmitting device 311 is a flexible light transmitting medium, and may be, for example, optical fiber. When the optical fiber is used as the optical transmitter 311, the optical fiber 311 performs a function of transmitting light only for a specific wavelength, so that noise of input light transmitted from the light source unit 310 can be removed. When exiting, the beam size may be enlarged and provided to the inspection unit 1000. In other words, the optical fiber 311 can replace the function of the collimator 320, so that the holography restoration apparatus 300 may omit the collimator 320 as necessary.

4 is a diagram schematically showing a substrate inspection apparatus 1 'according to another embodiment of the present invention.

Referring to FIG. 4, the substrate inspection apparatus 1 ′ according to another embodiment of the present invention may include a carrier 100, an inspection unit 1000, and a light source unit 310. The substrate inspection apparatus 1 ′ of FIG. 4 is identical to the substrate inspection apparatus 1 of FIG. 2 except for the inspection unit 1000, and other components are the same, and thus duplicate description will be omitted.

The inspection unit 1000 according to another embodiment may include a plurality of holographic restoration devices 300 arranged in a line along the first direction (x direction). The inspection unit 1000 moves the plurality of holographic reconstruction devices 300 arranged in the first direction (y direction) in a second direction (x direction) perpendicular to the first direction (x direction), thereby providing a large area substrate ( The surface of M) can be inspected more quickly. At this time, the surface of the substrate M in the unit area UA measurable using one holographic restoration apparatus 300 in the first direction (y direction) and n in the second direction (x direction) When dividing into m (n, m is an integer), the inspection unit 1000 may include n holographic restoration devices 300. Through this, the inspection unit 1000 can quickly inspect the entire substrate M by inspecting while moving along the second direction (x direction). However, the present invention is not limited to this, and as another embodiment, the inspection unit 1000 may be made of a different number of holographic restoration devices 300 other than n divided substrates M.

On the other hand, when the inspection unit 1000 is provided with a plurality of holographic reconstruction apparatus 300, the light source unit 310 includes a plurality of light sources corresponding to each of the holographic reconstruction apparatus 300, each of which has a single wavelength of light Can provide. As another embodiment, the substrate inspection apparatus 1 ′ includes a light source unit 310 having one light source, and uniformly converts a single wavelength of light emitted from the light source unit 310 into a plurality of holographic restoration devices 300. It can be distributed.

5 is a block diagram illustrating the light distribution unit 1100 of the substrate inspection apparatus 1 ′ according to another embodiment of the present invention.

Referring to FIG. 5, when the light source unit 310 is composed of one light source, the inspection unit 1000 is configured to uniformly distribute the single wavelength light provided from the light source unit 310 to the plurality of holographic restoration devices 300. A distribution 1100 may be further included. As an embodiment, the light distribution unit 1100 may be an optical splitter having one input port and a plurality of output ports, and distributing and outputting light provided through the input port into a plurality of lights. When the inspection unit 1000 receives light using an optical fiber, the light distribution unit 1100 may be integrally formed with the above-described optical fiber to simultaneously perform a light transmission function and a light distribution function. have.

As another embodiment, as illustrated in FIG. 5, the light distribution unit 1100 consists of an optical system including a plurality of light splitters 1030 and 1031 and an optical mirror 1070 to receive light provided from the light source unit 310. Can be distributed evenly.

6A and 6B are diagrams for explaining the appearance of an exemplary object M to be measured.

6A and 6B, the object M to be measured may include rectangular parallelepiped structures 51A to 51I arranged on one surface at predetermined intervals. In other words, the object M to be measured may include rectangular parallelepiped structures 51A to 51I protruding in the Z direction on a plane parallel to the X-Y plane.

Hereinafter, the holography restoration apparatus 300 irradiates the object light O in a direction perpendicular to the surface on which the structures 51A to 51I of the rectangular parallelepiped structure of the object M to be measured are arranged to image the object M to be measured It will be described on the premise of obtaining.

First, the image sensor 380 according to an embodiment of the present invention may acquire an image of an object M to be measured.

In the present invention, the 'image' of the object to be measured 50 is the intensity information at each position of the object hologram U0 (x, y, 0) with respect to the object to be measured 50 (ie | (U0 (x, y, 0) | ² ), and may be expressed as Equation 1 below.

[Equation 1]

Here, the object hologram Uo (x, y, 0) represents phase information at each x, y point of the object to be measured, and x, y are coordinates in the space where the object to be measured is placed and perpendicular to the object light O O (x, y) and R (x, y) denote object light O and reference light R, respectively, and O * (x, y) and R * (x, y) represents the complex conjugate of the object light O and the reference light R, respectively.

For example, the image sensor 380 may acquire an image as shown in FIG. 7 for a portion of the measurement target object M shown in FIGS. 6A and 6B (for example, a portion including 51A and 51B). You can.

Since the image obtained by the image sensor 380 includes intensity information at each position of the object hologram U0 (x, y, 0) as described above, the image sensor 380 acquires the general It may be different from the image of the object M to be measured (that is, photographed only with the object light O).

Referring to Equation 1, the object hologram U0 (x, y, 0) does not include the object light 0 including the phase information of the measurement object M at each point and the phase information of the measurement object. It may be generated by the interference of the reference light (R).

In addition, the object hologram (U0 (x, y, 0)) is in addition to the phase information (that is, the height information of the object) at each point (ie, each x, y point) of the object M to be measured, the object light objective lens 340 ) May further include errors and noises caused by aberration (eg, speckle noise due to use of a photon of a laser).

Accordingly, the processor 390 according to an embodiment of the present invention may perform various calculation processes as described below to remove the above-described error and noise from the image acquired by the image sensor 380.

The processor 390 according to an embodiment of the present invention may check frequency components of an image acquired by the image sensor 380. For example, the processor 390 may perform a 2D Fourier Transform on the image to check frequency components of the image.

In other words, the processor 390 includes frequency components included in an image including intensity information (ie | (U0 (x, y, 0) | ² ) for each position of the object hologram U0 (x, y, 0)). In this case, the image may include a frequency component corresponding to a real image, a frequency component corresponding to an Imaginary Image, and a DC component.

Of course, the image may further include various components in addition to the three components described above (the frequency component corresponding to the real image, the frequency component corresponding to the virtual image, and the DC component). For example, the image may further include frequency components due to noise. However, this is exemplary and the spirit of the present invention is not limited thereto.

The processor 390 according to an embodiment of the present invention may extract only components corresponding to a real condition among the identified frequency components. At this time, the processor 390 may extract components corresponding to the actual image in various ways.

For example, the processor 390 extracts components having a peak value (hereinafter referred to as peak components) among the frequency components included in the image, and a peak component corresponding to a real image among the extracted peak components. Components within the frequency difference of firing can be extracted as components corresponding to the actual image.

At this time, the processor 390 may determine components corresponding to the real image in various ways based on the peak component corresponding to the real image. For example, the processor 390 may determine frequency components in a cross region centered on a peak component corresponding to the real image as components corresponding to the real image. However, this is exemplary and the spirit of the present invention is not limited thereto.

In an optional embodiment, the processor 390 may extract only components corresponding to the real image among frequency components included in the hologram using an automatic real image spot-position extraction algorithm.

In the present invention, 'extracting' a specific frequency component may mean extracting a frequency and a magnitude (or intensity) of the frequency component.

FIG. 8 is a diagram showing frequency components of an image for a portion of the object M to be measured shown in FIG. 7.

As described above, the processor 390 may check frequency components of an image acquired by the image sensor 380, and accordingly, the processor 390 may include a frequency component 911 corresponding to the real image and a frequency component corresponding to the virtual image. Various frequency components including 912 and DC component 913 may be identified.

In addition, the processor 390 may extract only the frequency component 911 corresponding to the actual condition among the identified components. In this case, the processor 390 may determine frequency components 911B in the cross region centered on the peak component 911A corresponding to the real image as components corresponding to the real image, as illustrated in FIG. 9.

The processor 390 according to an embodiment of the present invention may generate digital reference light from frequency components corresponding to a real image extracted by the above-described process. Looking at this in more detail, the processor 390 may calculate the propagation direction and wave number of the digital reference light based on the frequency components corresponding to the actual image. In other words, the processor 390 may calculate the wavenumber vector of the digital reference light.

In addition, the processor 390 generates a digital reference light based on the propagation direction and wave number (or wave vector) of the digital reference light, and the conjugate term of the digital reference light (R (x, y)) generated as in Equation 2 below By obtaining, the correction light Rc (x, y) can be generated.

[Equation 2]

Rc (x, y) = conj [R (x, y)]

At this time, R (x, y) represents digital reference light generated based on frequency components corresponding to the real image, and Rc (x, y) represents correction light.

Since the digital reference light R (x, y) and the correction light Rc (x, y) have a conjugate relationship, as shown in FIGS. 10A and 10C, the intensity is the same, as shown in FIGS. 10B and 10D. Likewise, the phases can be reversed. Here, FIG. 10A is a diagram showing the intensity of the digital reference light R (x, y), FIG. 10B is a diagram showing the phase of the reference light, and FIG. 10C is the intensity of the correction light Rc (x, y). Fig. 10D is a diagram showing the phase of the correction light.

The generated correction light Rc (x, y) may be used for correction of a real hologram Um (x, y, 0) described later.

Meanwhile, the 'digital reference light' is light having the same properties as the reference light R generated by the above-described optical splitter 330 from light of a single wavelength, and the processor 390 restores the image obtained by the image sensor 380. It can be a virtual light.

The processor 390 according to an embodiment of the present invention may generate a real image hologram based on frequency components corresponding to the real image extracted by the above-described process. For example, the processor 390 may generate an actual hologram as illustrated in FIG. 11 by performing an inverse 2D Fourier transform on frequency components corresponding to the actual image.

In this case, the hologram may be represented by Equation 3 below.

[Equation 3]

Um (x, y, 0) = O (x, y) R * (x, y)

Here, Um (x, y, 0) denotes a hologram, O (x, y) denotes object light O, and R * (x, y) denotes a complex conjugate of reference light R.

On the other hand, such a real hologram (Um (x, y, 0)), in addition to information about the height of the object M to be measured, information about the reference light R and the error due to the aberration of the object light objective lens 340 It can contain.

Therefore, the processor 390 according to an embodiment of the present invention considers an error caused by the aberration of the object light 340 and the influence of the reference light R and the actual hologram Um (x, y, 0). A correction hologram Uc (x, y, 0) can be generated.

For example, the processor 390 has a term (Rc (x, y)) for correction light and a term for correction of curvature aberration (Rca (x) in the actual hologram (Um (x, y, 0)) as shown in Equation 4 below. , y)) to generate a correction hologram Uc (x, y, 0).

[Equation 4]

Uc (x, y, 0) = Um (x, y, 0) Rc (x, y) Rca (x, y)

Here, Uc (x, y, 0) represents a correction hologram in which information about the reference light R and aberration information of the object light objective lens 340 are removed, and Um (x, y, 0) represents a real hologram, Rc (x, y) represents a term for correction light, and Rca (x, y) represents a term for curvature aberration correction.

Meanwhile, the processor 390 according to an embodiment of the present invention may generate the term Rca (x, y) for curvature aberration correction described above in various ways.

For example, the processor 390 is actually a hologram (Um (x, y, 0)) multiplied by only the term (Rc (x, y)) for the corrected light from the hologram (hereinafter, the intermediate hologram) 3 of the object to be measured (M) A dimensional shape may be generated, and a term Rca (x, y) for curvature aberration correction may be generated from the generated 3D shape.

Looking at this in more detail, the processor 390 may determine at least one parameter for determining a curvature aberration correction term from the three-dimensional shape of the measurement target object M generated from the intermediate hologram. At this time, the parameters may include, for example, coordinates and radii of a center point defining a hemispherical curved surface.

12 and 13 are diagrams for explaining a method of determining a curvature aberration correction term from the intermediate hologram by the processor 390 according to an embodiment of the present invention.

For convenience of description, it is assumed that the image sensor 380 acquires an image of the cuboid-shaped structure 51D of FIG. 6B, and the processor 390 generates an intermediate hologram for the structure 51D according to the above-described process. do. It is also assumed that the three-dimensional shape 920 of the structure 51D generated from the intermediate hologram for the structure 51D is as shown in FIG. 12.

Under the assumptions described above, the processor 390 according to an embodiment of the present invention may determine at least one parameter for determining a curvature aberration correction term from the three-dimensional shape 920. For example, the processor 390 may determine, as parameters, the coordinates (Cx, Cy) of the center point of the hemispherical surface and the radius (r) of the surface from the curve on the II section of the three-dimensional shape 920 as shown in FIG. 13. . At this time, the processor 390 according to an embodiment of the present invention may determine the position and / or direction of the cutting surface such that the cutting surface such as the I-I cross section includes the center point of the three-dimensional shape 920 (ie, the center point of the hemispherical shape). Also, the processor 390 may determine that a cutting surface such as an I-I cross section is parallel to the traveling direction of the object light 0.

The processor 390 according to an embodiment of the present invention may generate (or determine) a curvature aberration correction term based on at least one parameter determined by the above-described process. For example, the processor 390 refers to the coordinates (Cx, Cy) of the center point of the curved surface and the radius (r) of the curved surface to generate a curved surface in three-dimensional space, and is reflected in the phase correction of each x, y point from the generated curved surface. A curvature aberration correction term may be generated (or determined) by generating information.

In an alternative embodiment, the processor 390 may determine a correction term from an intermediate hologram of an object to be measured (eg, an object having the same z value in all x and y coordinates) whose shape is known in advance.

In the case of an object to be measured in advance of the shape, since the z value at each x and y point is known in advance, the processor 390 provides a three-dimensional shape of the object to be measured generated from the intermediate hologram and the shape of the object to be measured. The correction term can also be determined by checking the difference in z values at each x and y point. However, this is exemplary and the spirit of the present invention is not limited thereto.

The processor 390 according to an embodiment of the present invention may generate a three-dimensional shape of the object M to be measured based on the correction hologram Uc (x, y, 0). In other words, the processor 390 may calculate the height in the z direction of the object at each x and y point.

For example, the processor 390 may convert the corrected hologram Uc (x, y, 0) into information on a reconstructed image plane. In this case, the reconstructed image plane refers to a virtual image display plane corresponding to a distance between the object to be measured and the image sensor by the processor, and may be a virtual plane calculated and simulated by the processor 390.

The processor 390 may calculate the height in the z direction of the object at x and y points from the reconstructed information in consideration of the reconstructed image plane. 14, three-dimensional shapes of two rectangular parallelepiped structures 51A and 51B disposed on the object M to be measured are exemplarily illustrated.

15 is a flowchart illustrating a method of generating three-dimensional shape information of a measurement target object M performed by the holographic restoration apparatus 300 according to an embodiment of the present invention. Hereinafter, descriptions of contents overlapping with those described in FIGS. 1 to 14 will be omitted, but will be described with reference to FIGS. 1 to 14 together.

The holographic reconstruction apparatus 300 according to an embodiment of the present invention may acquire an image of the object M to be measured (S1201).

In the present invention, the 'image' of the object M to be measured is intensity information at each position of the object hologram U0 (x, y, 0) with respect to the object M to be measured (ie | (U0 (x, y, 0) | ² ), and may be represented by Equation 1 above.

For example, the holographic reconstruction apparatus 300 acquires an image as shown in FIG. 7 for a portion of the object M to be measured shown in FIGS. 6A and 6B (for example, a portion including 51A and 51B). can do.

Since the image obtained by the holographic reconstruction apparatus 300 includes intensity information at each position of the object hologram U0 (x, y, 0) as described above, the holographic reconstruction apparatus 300 is obtained It may be different from the image of the object M to be measured (that is, taken only with the object light O).

Referring to Equation 1, the object hologram U0 (x, y, 0) does not include the object light 0 including the phase information of the measurement object M at each point and the phase information of the measurement object. It may be generated by the interference of the reference light (R).

In addition, the object hologram (U0 (x, y, 0)) is in addition to the phase information (that is, the height information of the object) at each point (ie, each x, y point) of the object M to be measured, the object light objective lens 340 ) May further include errors and noise due to aberration, such as speckle noise due to the use of a laser's photon.

Therefore, the holographic reconstruction apparatus 300 according to an embodiment of the present invention may perform the calculation process of steps S1202 to S1207 to remove the above-described error and noise from the image acquired by the holographic reconstruction apparatus 300. .

The holographic reconstruction apparatus 300 according to an embodiment of the present invention may check frequency components of an image acquired by the holographic reconstruction apparatus 300. (S1202) For example, the holographic reconstruction apparatus 300 is a two-dimensional image. By performing a Fourier Transform (2D Fourier Transform), it is possible to check the frequency components of the image.

In other words, the holographic restoring apparatus 300 includes the intensity information for each position of the object hologram U0 (x, y, 0) (that is, the frequency included in the image including | (U0 (x, y, 0) | ² )) Components may be identified, wherein the image may include a frequency component corresponding to a real image, a frequency component corresponding to an imaginary image, and a DC component.

Of course, the image may further include various components in addition to the three components described above (the frequency component corresponding to the real image, the frequency component corresponding to the virtual image, and the DC component). For example, the image may further include frequency components due to noise. However, this is exemplary and the spirit of the present invention is not limited thereto.

The holographic reconstruction apparatus 300 according to an embodiment of the present invention may extract only components corresponding to the real image from the identified frequency components. (S1203) At this time, the holographic reconstruction apparatus 300 may correspond to the real image in various ways. Ingredients can be extracted.

For example, the holographic reconstruction apparatus 300 extracts components having a peak value (hereinafter, peak components) among frequency components included in an image, and a peak corresponding to a real image among the extracted peak components Components within the frequency difference between the component and the firing can be extracted as components corresponding to the actual condition.

At this time, the holographic reconstruction apparatus 300 may determine components corresponding to the actual image in various ways based on a peak component corresponding to the actual image. For example, the holography reconstruction apparatus 300 may determine frequency components in a cross region centered on a peak component corresponding to the real image as components corresponding to the real image. However, this is exemplary and the spirit of the present invention is not limited thereto.

In an optional embodiment, the holographic reconstruction apparatus 300 may extract only components corresponding to the real image from among the frequency components included in the hologram using an automatic real image spot-position extraction algorithm.

In the present invention, 'extracting' a specific frequency component may mean extracting a frequency and a magnitude (or intensity) of the frequency component.

Referring back to FIG. 8, the holography reconstruction apparatus 300 may check frequency components of an image acquired by the holography reconstruction apparatus 300, and accordingly, the holography reconstruction apparatus 300 may correspond to a frequency component 911 corresponding to a real image. ), Various frequency components including the frequency component 912 and the DC component 913 corresponding to the virtual image may be identified.

Also, the holography reconstruction apparatus 300 may extract only the frequency component 911 corresponding to the real image from the identified components. At this time, the holographic reconstruction apparatus 300 may determine, as shown in FIG. 9, frequency components 911B in the cross region centered on the peak component 911A corresponding to the real image as components corresponding to the real image.

The holographic reconstruction apparatus 300 according to an embodiment of the present invention may generate digital reference light from frequency components corresponding to a real image extracted by the above-described process. (S1204) Looking at this in more detail, the holographic reconstruction apparatus ( 300) may calculate the propagation direction and wave number of the digital reference light based on the frequency components corresponding to the actual image. In other words, the holography reconstruction apparatus 300 may calculate the wavenumber vector of the digital reference light.

In addition, the holographic reconstruction apparatus 300 generates digital reference light based on the propagation direction and wave number (or wave vector) of the digital reference light, and the digital reference light R (x, y) generated as in Equation 2 described above. The correction light Rc (x, y) can be generated by obtaining the conjugate term.

Since the digital reference light R (x, y) and the correction light Rc (x, y) have a conjugate relationship, as shown in FIGS. 10A and 10C, the intensity is the same, as shown in FIGS. 10B and 10D. Likewise, the phases can be reversed. Here, FIG. 10A is a diagram showing the intensity of the digital reference light R (x, y), FIG. 10B is a diagram showing the phase of the reference light, and FIG. 10C is the intensity of the correction light Rc (x, y). Fig. 10D is a diagram showing the phase of the correction light.

The generated correction light Rc (x, y) may be used for correction of a real hologram Um (x, y, 0) described later.

Meanwhile, the 'digital reference light' is light having the same properties as the reference light R generated by the above-described light splitter 330 from light of a single wavelength, and the holographic restoring device 300 is obtained by the holographic restoring device 300. It may be a virtual light reconstructed from an image.

The holographic reconstruction apparatus 300 according to an embodiment of the present invention may also generate a real image hologram based on frequency components corresponding to the real image extracted by the above-described process. (S1204) For example, the holographic reconstruction apparatus 300 Can generate a real hologram as shown in FIG. 11 by performing an inverse 2D Fourier transform on frequency components corresponding to the real image. In this case, the hologram may be represented by Equation 3 described above.

The holographic reconstruction apparatus 300 according to an embodiment of the present invention may generate an intermediate hologram to generate a term (Rca (x, y)) for curvature aberration correction. (S1205) For example, the holographic reconstruction apparatus 300 ) Can actually generate an intermediate hologram by multiplying the hologram Um (x, y, 0) by the term Rc (x, y) for the correction light. The generated intermediate hologram can be used to generate curvature aberration correction information in step S1206.

The holographic restoring apparatus 300 according to an embodiment of the present invention generates a three-dimensional shape of the object M to be measured from the intermediate hologram generated in step S1205, and the terms for curvature aberration correction from the generated three-dimensional shape ( Rca (x, y)). (S1206) Looking at this in more detail, the holographic reconstruction apparatus 300 determines a curvature aberration correction term from the three-dimensional shape of the measurement target object M generated from the intermediate hologram. At least one parameter. At this time, the parameters may include, for example, coordinates and radii of a center point defining a hemispherical curved surface.

Referring back to FIGS. 12 and 13, a method for determining a correction term for curvature aberration from an intermediate hologram by the holographic reconstruction apparatus 300 according to an embodiment of the present invention will be described. For convenience of description, the holographic reconstruction apparatus 300 acquires an image of the rectangular parallelepiped structure 51D of FIG. 6B, and the holographic reconstruction apparatus 300 acquires an intermediate hologram for the structure 51D according to the above-described process. It is assumed to have been created. It is also assumed that the three-dimensional shape 920 of the structure 51D generated from the intermediate hologram for the structure 51D is as shown in FIG. 12.

Under the assumptions described above, the holographic reconstruction apparatus 300 according to an embodiment of the present invention may determine at least one parameter for determining a curvature aberration correction term from the three-dimensional shape 920. For example, the holographic reconstruction apparatus 300 determines the coordinates (Cx, Cy) of the center point of the hemispherical curved surface and the radius (r) of the curved surface as parameters from a curve on the II section of the three-dimensional shape 920 as shown in FIG. 13. You can. At this time, the holographic restoration apparatus 300 according to an embodiment of the present invention may determine the position and / or direction of the cutting surface such that the cutting surface such as the II cross-section includes the center point of the three-dimensional shape 920 (that is, the center point of the hemispherical shape). . Also, the holography restoring apparatus 300 may determine that a cutting surface such as an I-I cross section is parallel to the traveling direction of the object light 0.

The holographic restoration apparatus 300 according to an embodiment of the present invention may generate (or determine) a curvature aberration correction term based on at least one parameter determined by the above-described process. For example, the holography restoration apparatus 300 generates a curved surface in three-dimensional space with reference to the coordinates (Cx, Cy) of the center point of the curved surface and the radius (r) of the curved surface, and corrects the phase of each x, y point from the generated curved surface. A method of correcting a curvature aberration may be generated (or determined) in a manner of generating information to be reflected.

In an optional embodiment, the holographic reconstruction apparatus 300 may determine a correction term from an intermediate hologram of an object to be measured (eg, an object having the same z value in all x and y coordinates) that knows the shape in advance.

In the case of an object to be measured that knows the shape in advance, since the z value at each of the x and y points is known in advance, the holography restoring apparatus 300 can measure the three-dimensional shape of the object to be measured and the object to be measured from the intermediate hologram. The correction term can also be determined by checking the difference in z values at each x and y point of the shape. However, this is exemplary and the spirit of the present invention is not limited thereto.

The holographic restoring apparatus 300 according to an embodiment of the present invention considers an error caused by the aberration of the object light 40 and the influence by the reference light R and the actual hologram (Um (x, y, 0)) It is possible to generate a corrected hologram Uc (x, y, 0). (S1207) For example, the holographic reconstruction apparatus 300 may be configured to generate a real hologram Um (x, y, 0) as in Equation 4 above. A correction hologram Uc (x, y, 0) can be generated by multiplying the term Rc (x, y) for the correction light and the term Rca (x, y) for curvature aberration correction. In this case, the term Rc (x, y) for the correction light may be generated in step S1204, and the term Rca (x, y) for curvature aberration correction may be generated in step S1206.

The holographic restoring apparatus 300 according to an embodiment of the present invention may generate 3D shape information of the object M to be measured based on the corrected hologram Uc (x, y, 0). (S1208) In other words, the holography restoring apparatus 300 may calculate the height of the object at each x and y point in the z direction.

For example, the holographic reconstruction apparatus 300 may convert the corrected hologram Uc (x, y, 0) into information on the reconstructed image surface. In this case, the reconstructed image plane refers to a virtual image display plane corresponding to a distance between the object to be measured and the image sensor by the processor, and may be a virtual plane calculated and simulated by the holographic reconstruction apparatus 300. .

The holographic reconstruction apparatus 300 may calculate the height in the z direction of the object at the x and y points from the reconstructed information in consideration of the reconstructed image plane. 14, three-dimensional shapes of two rectangular parallelepiped structures 51A and 51B disposed on the object M to be measured are exemplarily illustrated.

16 is a block diagram of a processor 390 according to embodiments of the present invention. 16 and 17 show the structure of a processor 390 that performs a function of detecting a defect using a hologram. As illustrated in FIG. 16, the processor 390 of the holographic restoration apparatus 300 may further include a defect detection unit 97 for defect detection.

The processor 390 includes an image acquisition unit 91, a real-world information extraction unit 92, a real-world hologram generation unit 93, an intermediate hologram generation unit 94, a correction hologram generation unit 95, and a three-dimensional shape information generation unit (96), a defect detection unit 97 may be included.

The defect detection unit 97 uses the at least one of an image obtained through light irradiated to the target object, real information obtained from the image, real hologram, intermediate hologram, correction hologram, and three-dimensional shape information. Can be judged.

The defect detection unit 97 detects the presence or absence of a defect included in the 3D shape information of the target object, the location of the defect, the defect area, etc. by comparing the 3D shape information and the reference shape information for the target object. The reference shape information is set for each target object, and can be obtained through light irradiated to the target object in a defect-free state. The reference shape information may be a 3D image reconstructed through the irradiated light, or a 2D image itself, or a set of parameters from the image.

The defect detection unit 97 first determines the presence or absence of a defect by comparing the 3D shape information and the reference shape information for the target object, and based on the 3D shape information of the defective target object, the defect location and the defect area Detailed defect information, such as, can be detected.

The defect detection unit 97 may infer a defect or a defect-producing process in the object to be measured using the location of the defect or the defect area of the object to be measured and information on a previously registered process. Information about the process may include functions for each process of manufacturing a target object, and location information generated or changed by each process. Here, the process refers to a general circuit board manufacturing process, and may include diffusion, photo, etching, deposition, ion implantation, polishing, back polishing, wafer cutting, chip adhesion, mold, printing, plating, solder ball attachment, testing, etc. .

Specifically, the defect detection unit 97 considers the location of the defect, determines that a defect has occurred in at least one process of mask fabrication, wafer processing, and chip assembly, and additional input data, for example, real information, real hologram, In consideration of at least one of the intermediate hologram, the corrected hologram, and the 3D shape information, among the detailed processes such as diffusion, photo, etching, deposition, ion implantation, and polishing, which are included in wafer processing, a process in which defects are generated is determined. You can. As described above, the defect detection unit 97 may determine a process in an upper range where a defect is generated, and determine a process in a range where a lower defect is generated by further considering input data. The defect detection unit 97 may determine a process in which a defect occurs in a process in a higher range by considering only a part of the input data, and may specifically determine a detailed process in which a defect occurs in consideration of all of the input data.

17 is a diagram illustrating the structure and operation of the processor 390 for detecting defects using the machine learning modeling apparatus 200 according to the embodiments of the present invention.

As shown in FIG. 17, the holographic reconstruction apparatus 300 for detecting defects may further include a machine learning modeling apparatus 200.

The processor 390 for detecting defects may transmit at least one of the real information, the real hologram, the intermediate hologram, the corrected hologram, and the three-dimensional shape information obtained from the image and the defect information to the modeling unit 200.

The modeling unit 200 may include a machine learning unit 210 that performs machine learning and an image processing unit 220 that processes images for machine learning.

 The machine learning unit 210 may generate an algorithm between the input data by repeatedly inputting the input data. In addition, the machine learning unit 210 may generate algorithms between real information and defect information, real hologram and defect information, intermediate hologram and defect information, correction hologram and defect information, three-dimensional shape information, and defect information, respectively. For example, by using an algorithm, a type of input data having high relevance to the presence or absence of a defect may be specified as one of an image, real information, real hologram, intermediate hologram, correction hologram, and three-dimensional shape information.

The image processing unit 220 may analyze an image using a preset filter. For example, one or more pixels included in the image may be divided into n feature maps using a 3X3 filter. The machine learning unit 210 may generate an algorithm for defect information based on the images analyzed through the image processing unit 220.

The defect detection algorithm may be updated through the modeling unit 200 including the machine learning unit 210 and the image processing unit 220. The modeling unit 200 may transmit the updated defect detection algorithm to the processor 390 so that defects can be detected more accurately and quickly.

18 is a flowchart of a defect detection method according to embodiments of the present invention.

18, in S1610, the holography restoration apparatus 300 irradiates light to a region of the target substrate. The n holographic reconstruction devices 300 may irradiate light to n areas (see FIG. 4). In S1620, the holography restoration apparatus 300 senses an image including intensity information of the object hologram generated by interference of the reference light reflected from the optical mirror and the object light affected by the target substrate. In S1630, the holographic reconstruction apparatus 300 analyzes the image to obtain real information, real hologram, intermediate hologram, correction hologram, and three-dimensional shape information. In S1640, the holographic restoration apparatus 300 detects defect information including a type of defect, a location of the defect, and a process related to the defect included in the target substrate based on the 3D shape information. The holographic reconstruction apparatus 300 may detect a defect location on the target substrate based on the 3D shape information, and use the defect location to determine the type of defect and a process related to the defect. The holography restoration apparatus 300 may store and manage an LUT further including detailed defect information for each location of the defect.

Through this, the holographic reconstruction apparatus 300 may detect a defect existing in the target object using a hologram of the target object that is manufactured through one or more automated processes. The holographic reconstruction apparatus 300 may detect defect information existing in the target object by comparing the 3D shape information of the completed target object with pre-registered 3D shape information.

As shown in FIG. 19, after the step S1640, the holographic reconstruction apparatus 300 transfers the real image information, the real image hologram, the middle hologram, the correction hologram, the three-dimensional shape information, and the defect information obtained from the image to the machine learning unit to be defective. The detection algorithm can be learned (S1650).

As shown in FIG. 20, in S1810, the holographic restoration apparatus 300 may receive an updated defect detection algorithm.

In S1820, the holography restoration apparatus 300 irradiates light to a region of the target substrate.

In S1830, the holographic reconstruction apparatus 300 may sense an image including intensity information of the object hologram generated by interference of the reference light reflected from the optical mirror and the object light affected by the target substrate.

In S1840, the holographic reconstruction apparatus 300 may analyze the image and selectively obtain input data selected as meaningful data by a defect detection algorithm.

In S1850, the holographic reconstruction apparatus 300 may detect defect information including a type of defect, a location of the defect, and a process related to the defect included in the target substrate based on the input data.

21 is a view for explaining an embodiment of the machine learning unit 210 and the image processing unit 220.

In S1910, the machine learning modeling apparatus 200 may acquire input data. The machine learning modeling apparatus 200 may perform convolution operation after clustering the input data. Through the clustering, the processing time of the input data can be shortened. In addition to clustering, processing time can be reduced by compressing input data. In S1920, the machine learning modeling apparatus 200 may determine a filter for performing a convolution operation on input data in one convolution layer among a plurality of convolution layers. In S1930, the machine learning modeling apparatus 200 may determine a plurality of sub-filters corresponding to different filtering regions in the filter. In S1940, the machine learning modeling apparatus 200 may generate a plurality of feature maps based on a plurality of sub-filters. In S1950, the machine learning modeling apparatus 200 may obtain output data based on a plurality of feature maps. The machine learning modeling apparatus 200 may cluster a plurality of feature maps or output data. Through the clustering, the processing time of the feature map or output data can be shortened. Through the clustering, the processing time of the feature map or output data can be shortened. In addition to clustering, processing time can be reduced by compressing the feature map or output data.

For example, clustering includes K-Means Clustering, Mean-Shift Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM), Agglomerative Hierarchical Clustering, etc. There is this. In addition to clustering, performance can be improved through various algorithms.

22 is a block diagram of a machine learning unit 210 according to an embodiment.

Referring to FIG. 22, the data learning unit 210 according to an embodiment includes a data acquisition unit 211, a pre-processing unit 212, a training data selection unit 213, a model learning unit 214, and a model evaluation unit ( 215).

The data acquisition unit 211 may acquire data necessary for acquiring the recognition result. The data acquisition unit 211 may acquire data necessary for learning to acquire a recognition result.

For example, the data acquisition unit 211 included in the data learning unit 210 for learning the input image, or the input hologram and the output data (defect information) determined according to the input image or the input hologram, the holographic restoration apparatus 300 The input image or the input hologram can be received from.

The pre-processing unit 212 may pre-process the acquired data so that the acquired data can be used for learning for defect determination. The pre-processing unit 212 may process the acquired data in a preset format so that the model learning unit 214 to be described later can use the acquired data for learning for defect determination.

The learning data selection unit 213 may select data necessary for learning from pre-processed data. The selected data may be provided to the model learning unit 214. The learning data selection unit 213 may select data necessary for learning from pre-processed data according to a preset criterion for defect determination. Further, the learning data selection unit 213 may select data according to a preset criterion by learning by the model learning unit 214 to be described later.

The model learning unit 214 may learn a criterion on how to determine a defect based on the training data. In addition, the model learning unit 214 may learn the criteria on which learning data should be used for defect determination.

In addition, the model learning unit 214 may train the data recognition model used for defect determination using the training data. In this case, the data recognition model may be a pre-built model. For example, the data recognition model may be a pre-built model receiving basic training data (eg, sample images, sample holograms, etc.).

The data recognition model may be constructed in consideration of the application field of the recognition model, the purpose of learning, or the computer performance of the device. The data recognition model may be, for example, a model based on a neural network. For example, a model such as a deep neural network (DNN), a recurrent neural network (RNN), a bidirectional recurrent deep neural network (BRDNN), or a convolutional neural network (CNN) may be used as a data recognition model, but is not limited thereto.

According to various embodiments of the present disclosure, when a plurality of pre-built data recognition models exist, the model learning unit 214 may determine a data recognition model to train a data recognition model having a high relationship between input learning data and basic learning data. have. In this case, the basic learning data may be pre-classified for each type of data, and the data recognition model may be pre-built for each type of data.

Further, the model learning unit 214 may train a data recognition model using, for example, a learning algorithm including an error back-propagation or a gradient descent method.

Further, the model learning unit 214 may train the data recognition model, for example, through supervised learning using learning data as an input value. In addition, the model learning unit 214 recognizes data through unsupervised learning that discovers a criterion for defect determination by, for example, self-learning the type of data required for defect determination without much guidance. You can train the model. Also, the model learning unit 214 may train the data recognition model, for example, through reinforcement learning using feedback on whether a result of defect determination according to learning is correct.

In addition, when the data recognition model is trained, the model learning unit 214 may store the trained data recognition model. In this case, the model learning unit 214 may store the trained data recognition model in the memory of the holographic reconstruction apparatus 300. Alternatively, the model learning unit 214 may store the trained data recognition model in the memory of the holographic reconstruction apparatus 300 to be described later. Alternatively, the model learning unit 214 may store the learned data recognition model in a memory of a server connected to a wired or wireless network with the electronic device.

In this case, the memory in which the learned data recognition model is stored may store, for example, instructions or data related to at least one other component of the electronic device. Also, the memory may store software and / or programs. The program may include, for example, a kernel, middleware, application programming interface (API) and / or application program (or "application"), and the like.

The model evaluation unit 215 may input the evaluation data into the data recognition model, and if the recognition result output from the evaluation data does not satisfy a predetermined criterion, the model learning unit 214 may cause the model learning unit 214 to learn again. In this case, the evaluation data may be preset data for evaluating the data recognition model.

For example, the model evaluation unit 215 does not satisfy a predetermined criterion when the number or ratio of the evaluation data in which the recognition result is not accurate among the recognition results of the learned data recognition model for the evaluation data exceeds a preset threshold. It can be evaluated as failed. For example, when a predetermined criterion is defined as a ratio of 2%, the model evaluation unit 215 learns when the learned data recognition model outputs an incorrect recognition result for evaluation data exceeding 20 out of a total of 1000 evaluation data. It can be evaluated that the data recognition model is not suitable.

On the other hand, when there are a plurality of learned data recognition models, the model evaluator 215 evaluates whether or not a predetermined criterion is satisfied for each trained data recognition model, and a model that satisfies a predetermined criterion as a final data recognition model. Can decide. In this case, when there are a plurality of models satisfying a predetermined criterion, the model evaluator 215 may determine, as a final data recognition model, any one or a predetermined number of models preset in order of highest evaluation score.

Meanwhile, at least one of the data acquisition unit 211, the pre-processing unit 212, the learning data selection unit 213, the model learning unit 214, and the model evaluation unit 215 in the machine learning unit 210 is at least one. It can be manufactured in the form of a hardware chip and mounted on an electronic device. For example, at least one of the data acquisition unit 211, the pre-processing unit 212, the training data selection unit 213, the model learning unit 214, and the model evaluation unit 215 is artificial intelligence (AI). It may be manufactured in the form of a dedicated hardware chip for, or it may be manufactured as part of an existing general-purpose processor (for example, a CPU or application processor) or a graphics-only processor (for example, a GPU) and mounted on various electronic devices described above.

In addition, the data acquisition unit 211, the pre-processing unit 212, the learning data selection unit 213, the model learning unit 214 and the model evaluation unit 215 may be mounted on one electronic device, or separately Each may be mounted on electronic devices. For example, some of the data acquisition unit 211, the pre-processing unit 212, the training data selection unit 213, the model learning unit 214, and the model evaluation unit 215 are included in the electronic device, and the others are It can be included in the server.

In addition, at least one of the data acquisition unit 211, the pre-processing unit 212, the learning data selection unit 213, the model learning unit 214, and the model evaluation unit 215 may be implemented as a software module. At least one of the data acquisition unit 211, the pre-processing unit 212, the learning data selection unit 213, the model learning unit 214, and the model evaluation unit 215 includes a software module (or an instruction). Program module), the software module may be stored in a computer-readable readable non-transitory computer readable media. Also, in this case, at least one software module may be provided by an operating system (OS) or may be provided by a predetermined application. Alternatively, some of the at least one software module may be provided by an operating system (OS), and the other may be provided by a predetermined application.

22 is a diagram showing a connection relationship of a defect detection algorithm modeled through the machine learning unit 210.

Defect location information D2 may be determined by using attribute information D1 obtained from the input data image and holograms, and the type of defect D3 may be determined based on the location information of the defect.

The embodiment according to the present invention described above may be implemented in the form of a computer program that can be executed through various components on a computer, and such a computer program can be recorded on a computer-readable medium. At this time, the medium may be to store a program executable by a computer. Examples of the medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical image sensors such as CD-ROMs and DVDs, and magneto-optical mediums such as floptical disks, And program instructions including ROM, RAM, flash memory, and the like.

Meanwhile, the computer program may be specially designed and configured for the present invention or may be known and available to those skilled in the computer software field. Examples of computer programs may include machine language codes such as those produced by a compiler, as well as high-level language codes that can be executed by a computer using an interpreter or the like.

The specific implementations described in the present invention are examples, and do not limit the scope of the present invention in any way. For brevity of the specification, descriptions of conventional electronic configurations, control systems, software, and other functional aspects of the systems may be omitted. In addition, the connection or connection members of the lines between the components shown in the drawings are illustrative examples of functional connections and / or physical or circuit connections. In the actual device, alternative or additional various functional connections, physical It can be represented as a connection, or circuit connections. In addition, unless specifically mentioned, such as "essential", "important", etc., it may not be a necessary component for the application of the present invention.

Accordingly, the spirit of the present invention is not limited to the above-described embodiments, and should not be determined, and the scope of the spirit of the present invention as well as the claims to be described later, as well as all ranges that are equivalent to or equivalently changed from the claims Would belong to

300, 300A, 300B: Holographic restoration device
310: light source unit
320: collimator
330,332: Optical splitter
340: object light objective lens
350: object to be measured
360: Reference light objective lens
370,372: Optical mirror
380: image sensor
390: processor

Claims (6)

A carrier for seating the substrate;
An inspection unit spaced apart from the carrier to restore three-dimensional information on the surface of the substrate to inspect for abnormality of the substrate, and scanning and inspecting the surface of the substrate along a preset scan path; And
Includes; a light source unit for emitting a single wavelength light to provide to the inspection unit;
The inspection unit includes a holographic restoration device,
The holographic restoration device
A collimator for collimating single wavelength light emitted from the light source unit;
An optical splitter for dividing the single wavelength light passing through the collimator into object light and reference light;
An object light objective lens for passing the object light divided by the light splitter;
An optical mirror reflecting the reference light divided by the light splitter;
An image sensor that records an interference fringe formed by passing the object light reflected from the surface of the substrate through the object light objective lens and the reference light reflected by the optical mirror to the optical splitter; And
A processor that receives and stores an image including intensity information of an object hologram generated by converting the interference fringe from the image sensor, and generates three-dimensional shape information of the substrate surface. A substrate inspection device comprising. According to claim 1,
The inspection unit includes a plurality of holographic restoration devices arranged in a line along the first direction, the substrate inspection device including a scanning function. According to claim 2,
The light source unit is provided with a plurality of light sources corresponding to the number of the holographic reconstruction device, the substrate inspection apparatus including a scanning function. According to claim 2,
The light source unit is made of one light source,
The inspection unit further includes a light distribution unit for equally distributing the single wavelength light provided from the light source unit to the plurality of holographic restoration devices. The substrate inspection apparatus including a scanning function. According to claim 1,
And an optical fiber for transmitting the single wavelength light emitted from the light source unit to the inspection unit. According to claim 1,
The processor extracts a real image component corresponding to a real image among at least one frequency component included in the image, and corrects the correction light and the conjugate light in a conjugate relationship with the reference light based on the real image components. After generating a real image hologram including real image information on the substrate surface, based on the correction light, generating an intermediate hologram from which the information of the reference light is removed from the real image hologram, and generating curvature aberration correction information from the intermediate hologram , Based on the curvature aberration correction information, generating a correction hologram with improved error due to curvature aberration in the intermediate hologram, and generating the three-dimensional shape information of the substrate surface from the correction hologram, including a scanning function Substrate inspection device.

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