Interference observation device and interference observation method

Abstract

To enable discrimination of a foreign object attached to an observation object.SOLUTION: An interference observation device 1 comprises: an interference optical system 3 that has a light source 2 that outputs light and a reference mirror 15, and splits the light output from the light source 2 into first light L1 and second light L2, and outputs interference light L3 between the first light L1 reflected by an observation object 8 and the second light L2 reflected by the reference mirror 15; an image pickup device 4 that detects the interference light L3; a processing unit 5 that acquires an interference image based on the detection results of the image pickup device 4 and acquires the degree of disorder of the image of a foreign object region attached to the observation object 8 in the interference image; and a display 53 that displays the information on the degree of disorder acquired by the processing unit 5.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an interference observation apparatus and an interference observation method. [Background technology]

[0002] There is known an interference observation device that includes a light source that outputs light, an interference optical system that splits the light output from the light source into a first light and a second light, and outputs interference light between the first light reflected by an observation object and the second light reflected by a reference mirror, an image sensor that detects the interference light, and a processing unit that acquires an interference image based on the detection result of the image sensor. As an example of this type of technology, Patent Document 1 discloses a cell observation device that is intended to distinguish the type of cell by acquiring information such as the optical thickness of the cell. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] WO2011 / 132587 publication [Patent Document 2] Patent Publication No. 08-124982 [Patent Document 3] Patent Publication No. 11-176898 Summary of the Invention [Problem to be solved by the invention]

[0004] In the above-mentioned interference observation device, there are cases where a foreign object is attached to the observation target, and in such cases, it is desirable to distinguish the foreign object (i.e., to distinguish the type of the foreign object). Regarding this issue, for example, Patent Document 2 describes that minute foreign objects attached to the pattern surface of a semiconductor device are a major factor in reducing the yield of the semiconductor device. Furthermore, for example, Patent Document 2 describes that in order to prevent the attachment of such foreign objects, it is important to quickly identify the source of the foreign object and take measures to prevent contamination, and that for this purpose, analysis of the composition and state of the foreign object is essential.

[0005] Regarding the composition of foreign matter, for example, Patent Document 3 describes that when a gate oxide film is formed by thermal oxidation in a semiconductor manufacturing process, the presence of metal contamination and organic contamination can cause dielectric breakdown. Patent Document 3 also describes that organic contamination can cause degradation of insulation even when attached after the gate oxide film is formed, and describes a method for detecting and removing organic contamination.

[0006] SUMMARY OF THE PRESENT DISCLOSURE An object of the present invention is to provide an interference observation apparatus and an interference observation method capable of discriminating between foreign matter adhering to an observation object. [Means for solving the problem]

[0007] As a result of extensive research, the inventors have found that in an interference image of an object to be observed having foreign matter attached thereto, the degree of randomness of an image of a foreign matter region differs depending on the type of the foreign matter. Therefore, the inventors have found that if the degree of randomness of an image of a foreign matter region in an interference image can be obtained, it is possible to discriminate the foreign matter, and have completed the present invention.

[0008] That is, the interference observation device of the present invention is [1] "an interference observation device comprising: a light source that outputs light; an interference optical system having a reference mirror and splitting the light output from the light source into first light and second light, and outputting interference light between the first light reflected by an object to be observed and the second light reflected by the reference mirror; an imager that detects the interference light; a processing unit that acquires an interference image based on a detection result of the imager, and acquires a degree of disorder of an image of a foreign matter region adhering to the object to be observed in the interference image; and an output unit that outputs information regarding the degree of disorder acquired by the processing unit."

[0009] In this interference observation device, when a foreign object is attached to the observation object, information regarding the degree of disorder of the image of the foreign object region in the interference image can be output. Therefore, based on the output information regarding the degree of disorder, it becomes possible to discriminate the foreign object attached to the observation object using the above-mentioned knowledge.

[0010] The interference observation device of the present invention may be the interference observation device according to [1], further comprising: [2] a storage unit that stores a foreign object classification model relating to a relationship between the degree of disorder of a foreign object and the type of the foreign object, and the processing unit refers to the foreign object classification model stored in the storage unit to discriminate the type of the foreign object from the acquired degree of disorder. In this case, it becomes possible to discriminate foreign objects adhering to an observation target object by referring to the foreign object classification model.

[0011] The interference observation apparatus of the present invention may be the interference observation apparatus according to [2], which is provided with a learning unit that generates a foreign object classification model relating to the relationship between the degree of disorder of the foreign object and the type of the foreign object by machine learning using a plurality of interference images in a case where the foreign object is attached to the observation target as training data. In this case, it is possible to generate the foreign object classification model by machine learning.

[0012] The interference observation apparatus of the present invention may be the interference observation apparatus according to [2] or [3], wherein the foreign matter includes a reflective foreign matter and a transmissive foreign matter. In this case, it is possible to distinguish whether a foreign matter attached to an observation object is a reflective foreign matter or a transmissive foreign matter.

[0013] The interference observation apparatus of the present invention may be [5] "the interference observation apparatus according to [4], wherein the reflective foreign matter includes a semiconductor, and the transmissive foreign matter includes an organic matter." In this case, it is possible to distinguish whether the foreign matter attached to the observation target is a semiconductor having reflectivity or an organic matter having transmissivity.

[0014] The interference observation apparatus of the present invention may be [6] "the interference observation apparatus according to any one of [1] to [5], wherein the output unit includes a display unit that displays information about the degree of disorder." In this case, information about the degree of disorder of the image of the foreign object region in the interference image can be easily grasped.

[0015] The interference observation device of the present invention may be [7] "the interference observation device according to any one of [1] to [6], wherein the interference optical system is configured so that the optical path length of the optical path of the second light can be changed." In this case, by changing the optical path length of the optical path of the second light, it is possible to make foreign matter stand out in the obtained interference image. This makes it possible to easily distinguish foreign matter.

[0016] The interference observation method of the present invention is [8] "an interference observation method comprising: a light output step of outputting light from a light source; an interference light output step of splitting the light output from the light source into first light and second light, and outputting interference light between the first light reflected by an object to be observed and the second light reflected by a reference mirror; an interference light detection step of detecting the interference light with an image sensor; a processing step of acquiring an interference image based on a detection result of the image sensor and acquiring a degree of disorder of an image of the foreign matter region adhering to the object to be observed in the interference image; and an output step of outputting information regarding the degree of disorder acquired in the processing step."

[0017] In this interference observation method, when a foreign substance is attached to the observation object, information on the degree of disorder of the image of the foreign substance region in the interference image can be output. Therefore, based on the output information on the degree of disorder, it becomes possible to discriminate the foreign substance attached to the observation object using the above-mentioned knowledge. Effect of the Invention

[0018] According to the present invention, it is possible to provide an interference observation apparatus and an interference observation method capable of discriminating between foreign matter adhering to an observation object. [Brief description of the drawings]

[0019] [Figure 1] FIG. 1 is a configuration diagram showing an interference observation apparatus according to an embodiment. [Diagram 2] FIG. 2 is an enlarged view of the observation object of FIG. [Diagram 3] FIG. 3 is a flowchart showing an example of a method for calculating the degree of disorder. [Figure 4] FIG. 4 is a diagram illustrating the relationship between the phase image and the degree of disorder. [Diagram 5] Figure 5(a) shows a phase image, Figure 5(b) shows the real part of a complex image corresponding to the phase image of Figure 5(a), and Figure 5(c) shows the imaginary part of a complex image corresponding to the phase image of Figure 5(a). [Figure 6] Fig. 6(a) is a diagram showing the real part of the first smoothed complex image, and Fig. 6(b) is a diagram showing the imaginary part of the first smoothed complex image. [Figure 7] Fig. 7(a) is a diagram showing the real part of the second smoothed complex image, and Fig. 7(b) is a diagram showing the imaginary part of the second smoothed complex image. [Figure 8] Fig. 8(a) is a diagram showing the real part of the third smoothed complex image, and Fig. 8(b) is a diagram showing the imaginary part of the third smoothed complex image. [Figure 9] Fig. 9(a) is a diagram showing a first phase difference image, Fig. 9(b) is a diagram showing a second phase difference image, and Fig. 9(c) is a diagram showing a third phase difference image. [Figure 10] Fig. 10(a) is an enlarged view of a portion of a phase image including a reflective foreign object, and Fig. 10(b) is an enlarged view of a portion of a phase difference image corresponding to the phase image of Fig. 10(a). [Figure 11] Fig. 11(a) is an enlarged view of a portion of a phase image including a transparent foreign object, and Fig. 11(b) is an enlarged view of a portion of a phase difference image corresponding to the phase image of Fig. 11(a). [Figure 12] FIG. 12 is an image of local phase variations in the second phase difference image. [Figure 13] FIG. 13 is a diagram for explaining a foreign object classification model. [Figure 14] FIG. 14 is a diagram showing a reflectance foreign matter degree table. [Figure 15] FIG. 15 is a flowchart showing an example of a method for learning a foreign object classification model. [Figure 16] FIG. 16 is a flowchart showing an example of an interference observation method using the interference observation device. [Figure 17] FIG. 17 is a diagram for explaining a foreign object classification model according to a modified example. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0020] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the following description, the same or corresponding elements are designated by the same reference numerals, and duplicated descriptions will be omitted.

[0021] [Interference observation device] As shown in FIG. 1, the interference observation device 1 includes a light source 2, an interference optical system 3, an image sensor 4, a processing unit 5, and a stage S. The interference observation device 1 is an interference microscope for observing an observation object 8 arranged on the stage S using the interference of light. The observation object 8 is, for example, a semiconductor device, but may also be other industrial samples formed of metal, glass, resin, liquid crystal, polymer compound, etc. The observation object 8 may also be a biological sample such as a cell. The following description will be given by setting an X direction, a Y direction perpendicular to the X direction, and a Z direction perpendicular to both the X direction and the Y direction, as shown in FIG. 1.

[0022] The interference observation apparatus 1 is configured to be able to observe the inside of an observation object 8. In internal observation, an observation surface R located within the observation object 8 is observed. In this example, the observation object 8 is a so-called film-attached wafer. As shown in Figs. 1 and 2, the observation object 8 includes a device portion 81 constituting a patterned chip, and a film portion 82 which is a passivation film provided on the surface of the device portion 81. The device portion 81 is formed by forming a pattern of functional elements 81b on a wafer 81a. The observation surface R is the surface of the device portion 81.

[0023] The light source 2 outputs incoherent light. The light source 2 is, for example, a lamp-based light source such as a halogen lamp, an LED (Light emitting diode) light source, an SLD (Superluminescent diode) light source, an ASE (Amplified spontaneous emission) light source, or the like. In this example, the interference optical system 3 is configured as a Linnik interference type. The interference optical system 3 has a lens 11, a beam splitter 12, an objective lens 13, a reference objective lens 14, and a reference mirror 15. The interference optical system 3 is disposed in a housing H together with the light source 2, and constitutes an optical module M. The optical module M is movable along the Z direction by a predetermined actuator 16. The Z direction is a direction parallel to the optical axis of the objective lens 13, and is a direction parallel to the direction in which a first light L1, which will be described later, is incident on the observation object 8.

[0024] The lens 11 collimates the light output from the light source 2. The beam splitter 12 is, for example, a prism having an optical surface 12a, and splits the light collimated by the lens 11 into a first light L1 and a second light L2 at the optical surface 12a. The beam splitter 12 outputs the first light L1 to the objective lens 13, and outputs the second light L2 to the reference objective lens 14. In addition, the first light L1 reflected by the observation surface R of the observation object 8 enters the optical surface 12a via the objective lens 13, and the second light L2 reflected by the reference mirror 15 enters the optical surface 12a via the reference objective lens 14. The first light L1 and the second light L2 are combined at the optical surface 12a to become an interference light L3. The interference optical system 3 outputs the interference light L3 to the image sensor 4.

[0025] The objective lens 13 focuses the first light L1 output from the beam splitter 12 on the observation object 8 arranged on the stage S. The objective lens 13 also receives the first light L1 reflected from the observation surface R of the observation object 8. The objective lens 13 outputs the incident first light L1 to the beam splitter 12. The reference objective lens 14 guides the second light L2 output from the beam splitter 12 to the reference mirror 15 and focuses the light on the reference mirror 15. The reference objective lens 14 also outputs the second light L2 reflected by the reference mirror 15 to the beam splitter 12. The reference mirror 15 reflects the second light L2 output from the reference objective lens 14 back to the reference objective lens 14.

[0026] Further disposed within the housing H of the optical module M are a stepping motor 17 for moving the reference objective lens 14, and a stepping motor 18 and a piezoelectric element 19 for moving the reference mirror 15. The stepping motor 17 moves the reference objective lens 14 along the optical axis direction of the second light L2 perpendicular to the Z direction (e.g., the X direction). The stepping motor 18 and the piezoelectric element 19 move the reference mirror 15 along the optical axis direction of the second light L2. In order to obtain interference fringes with a good S / N ratio, it is preferable that the optical path length from the beam splitter 12 to the reference mirror 15 be adjusted so as to be approximately equal to the optical path length from the beam splitter 12 to the observation surface (reflection surface) R.

[0027] The response time of the stepping motors 17, 18 is greater than 10 msec, and the response time of the piezoelectric element 19 is less than 1 msec. That is, the response time of the piezoelectric element 19 is shorter than the response time of the stepping motors 17, 18. The stroke (minimum movement distance) of the stepping motors 17, 18 is several mm, and the stroke of the piezoelectric element 19 is about 10 μm. That is, the stroke of the piezoelectric element 19 is smaller than the stroke of the stepping motors 17, 18. The number of times that the stepping motors 17, 18 are driven in their lifetime is less than one million times, and the number of times that the piezoelectric element 19 is driven in their lifetime is more than ten billion times. That is, the number of times that the piezoelectric element 19 is driven in their lifetime is greater than the number of times that the stepping motors 17, 18 are driven in their lifetime.

[0028] The imaging element 4 is, for example, an image sensor (camera) such as a CCD area image sensor or a CMOS area image sensor. The imaging element 4 detects (images) the interference light L3 output from the interference optical system 3 (beam splitter 12). A lens 41 and a lens barrel 42 are arranged between the imaging element 4 and the interference optical system 3. The lens 41 forms an image of the interference light L3 output from the interference optical system 3 on the imaging surface of the imaging element 4. The lens 41 is housed in the lens barrel 42. The lens barrel 42 is formed, for example, in a cylindrical shape, and is fixed to the imaging element 4 so as to surround the imaging surface.

[0029] The processing unit 5 is communicably connected to each unit of the interference observation device 1 including the light source 2, the interference optical system 3, the image sensor 4, and the stage S, and acquires an interference image based on the detection result of the interference light L3 at the image sensor 4. The interference image includes, for example, a phase image, an amplitude image, a complex image, various images described later, and images equivalent to any of these.

[0030] The processing unit 5 is configured by, for example, a computer C including a processor (CPU), and RAM and ROM as storage media. The computer C has a storage area (storage unit) 51 that stores various information, an input unit 52 that accepts input of various information, a display unit 53 that displays various information, and a learning unit 54 that generates a foreign object classification model (described later) by machine learning. The input unit 52 is, for example, a device that accepts user operation input, such as a mouse or a keyboard. The display unit 53 is, for example, a display that displays an image. The input unit 52 and the display unit 53 may be commonly configured by, for example, a touch panel, and in this case, may be configured by a GUI (Graphical User Interface). In this example, the processing unit 5, the storage area 51, the input unit 52, the display unit 53, and the learning unit 54 are configured by one device, but at least one of them may be configured by another device, such as a mobile terminal.

[0031] The stage S is a stage for placing the observation object 8, and is movable along an XY plane perpendicular to the Z direction in which the first light L1 is incident on the observation object 8. This makes it possible in the interference observation apparatus 1 to observe the observation surface R of the observation object 8 (to obtain an interference image) while moving the stage S along the XY plane, that is, while changing the observation position of the observation object 8.

[0032] In such an interference observation device 1, first, incoherent light is output from the light source 2 (light output step). The light is collimated by the lens 11 and split into a first light L1 and a second light L2 by the beam splitter 12. The first light L1 is focused on the observation object 8 by the objective lens 13, reflected at the interface of the observation object 8, and input to the beam splitter 12 via the objective lens 13. The second light L2 is focused on the reference mirror 15 by the reference objective lens 14, reflected by the reference mirror 15, and input to the beam splitter 12 via the reference objective lens 14. The beam splitter 12 combines the input first light L1 and second light L2 to output an interference light L3 (interference light output step). The output interference light L3 is detected by the image sensor 4 (interference light detection step). Based on the detection result of the image sensor 4, the processing unit 5 acquires an interference image (processing step).

[0033] In the interference observation device 1, the imaging element 4 captures four interference images, and the processing unit 5 constructs (acquires) one phase image based on the four captured interference images. The piezoelectric element 19 moves the reference mirror 15 minutely in accordance with the timing of capturing the four interference images. This causes the optical path length difference between the first light L1 and the second light L2 to differ between the four interference images. The amount of movement of the reference mirror 15 to capture the four interference images is smaller than the wavelength λ of the light output from the light source 2. In this example, the optical path length difference (phase shift interval) of the second light L2 between the four interference images is λ / 4. When the reference mirror 15 is moved minutely so that the change width of the optical path length is λ / 4, it is noted that the reference light (second light L2) reciprocates through the reference optical path (optical path length of the optical path of the second light L2), and the actual movement amount step of the reference mirror 15 is λ / 8, and this value is 66.5 nm when λ=532 nm. The method of acquiring the interference image is not limited to the above-mentioned example, and may be any of various known methods.

[0034] In this embodiment, the processing unit 5 calculates and acquires the degree of disorder of a region of a foreign matter 9 attached to the observation target 8 in the interference image, and discriminates the foreign matter 9 from the acquired degree of disorder. The foreign matter 9 to be discriminated includes at least a reflective foreign matter 91 having reflectivity to the first light L1, and a transmissive foreign matter 92 having transmissivity to the first light L1. The reflective foreign matter 91 includes, for example, silicon chips (silicon pieces) which are semiconductors. The transmissive foreign matter 92 is particularly an organic matter, and includes, for example, a dried stain of ethanol. The type of the foreign matter 9 to be discriminated is not particularly limited, and may be various types of elements.

[0035] Non-patent document 1: P. Burt and E. Adelson, “The Laplacian Pyramid as a Compact Image Code”, in IEEE Transactions on Communications, vol. 31, no. 4, pp. 532-540, April 1983

[0036] The above-mentioned Non-Patent Document 1 discloses a method for extracting image features by creating multiple smoothed images using different smoothing filter sizes for an original image and using a difference image between images with different smoothing filter sizes. However, since the phase is folded back from -π to π in a phase image, there is a problem in directly applying a smoothing filter to the phase. An example of a method for smoothing filtering and generating a difference image when phase folding occurs is shown below.

[0037] An example of a method for calculating the degree of randomness of an interference image by the processing unit 5 will be described in detail below with reference to the flowchart of FIG. 3. Since the phase image is folded back from -π to π as shown in FIG. 4, it is difficult to compare the acquired phase images when evaluating the phase variation. For example, since all four phase images in FIG. 4 contain phases from -π to π to the same extent, it is difficult to compare the degrees of randomness of the interference images. In addition, when evaluating local phase variations, the evaluation value changes significantly between a region that straddles the phase folding back and a region that does not. Therefore, the processing unit 5 does not directly calculate the standard deviation of the phase image I (see FIG. 5(a)), but calculates and acquires a complex image G0 (see FIG. 5(b), 5(c)) corresponding to the phase image I (step S11). Here, the complex image G0 is an image represented by a two-dimensional array of complex numbers whose real part is image G0Re (see FIG. 5(b)) and whose imaginary part is image G0Im (see FIG. 5(c)), and the deflection angle of the complex image G0 corresponds to the phase image I.

[0038] Next, the processing unit 5 smoothes the complex image G0 at a plurality of smoothing distances (i.e., smoothing window sizes) to obtain a plurality of smoothed complex images (step S12). Specifically, as shown in Fig. 6(a) and Fig. 6(b), the processing unit 5 smoothes (convolution operation) the complex image G0 at a window size of 2x2 to obtain a first smoothed complex image G1. As shown in Fig. 7(a) and Fig. 7(b), the processing unit 5 smoothes the complex image G0 at a window size of 4x4 to obtain a second smoothed complex image G2. As shown in Fig. 8(a) and Fig. 8(b), the processing unit 5 smoothes the complex image G0 at a window size of 8x8 to obtain a third smoothed complex image G3.

[0039] In addition, when smoothing the complex image, a smoothing filter process is performed on each of the real part and the imaginary part. The first to third smoothed complex images G1 to G3 are defined as follows, similar to the case of the complex image G0. The first smoothed complex image G1 is an image represented by a two-dimensional array of complex numbers whose real part is image G1Re (see FIG. 6(a)) and whose imaginary part is image G1Im (see FIG. 6(b)). The second smoothed complex image G2 is an image represented by a two-dimensional array of complex numbers whose real part is image G2Re (see FIG. 7(a)) and whose imaginary part is image G2Im (see FIG. 7(b)). The third smoothed complex image G3 is an image represented by a two-dimensional array of complex numbers whose real part is image G3Re (see FIG. 8(a)) and whose imaginary part is image G3Im (see FIG. 8(b)).

[0040] Next, the processing unit 5 performs division among the complex image G0, the first smoothed complex image G1, the second smoothed complex image G2, and the third smoothed complex image G3, and then calculates the argument to obtain a plurality of phase difference images that index the degree of phase fluctuation with respect to a specific spatial frequency (step S13). Specifically, the processing unit 5 divides the complex image G0 by the first smoothed complex image G1 to obtain the argument, thereby calculating the phase difference of the first smoothed complex image G1 with respect to the complex image G0 (i.e., Arg(complex image G0 / first smoothed complex image G1)). Here, Arg(C) is a function that calculates the argument with respect to the complex number C. As a result, a first phase difference image Z1 (see FIG. 9(a)) is obtained that indicates the phase fluctuation (ΔL=2) between pixels that are separated by a smoothing distance of 2.

[0041] The processing unit 5 divides the first smoothed complex image G1 by the second smoothed complex image G2 to obtain a deflection angle, thereby calculating a phase difference of the second smoothed complex image G2 with respect to the first smoothed complex image G1 (i.e., Arg(first smoothed complex image G1 / second smoothed complex image G2)). This results in a second phase difference image Z2 (see FIG. 9(b)) that represents a phase fluctuation (ΔL=4) between pixels that are separated by a smoothing distance of 4. Furthermore, the processing unit 5 divides the second smoothed complex image G2 by the third smoothed complex image G3 to obtain a deflection angle, thereby calculating a phase difference of the third smoothed complex image G3 with respect to the second smoothed complex image G2 (i.e., Arg(second smoothed complex image G2 / third smoothed complex image G3)). This results in a third phase difference image Z3 (see FIG. 9(c)) that represents the phase variation (ΔL=8) between pixels that are separated by a smoothing distance of 8. Note that the phase difference between a complex number C1 of a deflection angle Φ1 and a complex number C2 of a deflection angle Φ2 is generally expressed as Arg(C1 / C2).

[0042] FIG. 10(a) is an enlarged view of a portion of a phase image including a reflective foreign object 91. FIG. 10(b) is an enlarged view of a portion of a phase difference image corresponding to the phase image of FIG. 10(a). FIG. 10(b) shows a phase difference image in the case of ΔL=4. The processing unit 5 can obtain the phase difference image of FIG. 10(b) based on the phase image of FIG. 10(a). FIG. 11(a) is an enlarged view of a portion of a phase image including a transparent foreign object 92. FIG. 11(b) is an enlarged view of a portion of a phase difference image corresponding to the phase image of FIG. 11(a). FIG. 11(b) shows a phase difference image in the case of ΔL=4. The processing unit 5 can obtain the phase difference image of FIG. 11(b) based on the phase image of FIG. 11(a).

[0043] The processing unit 5 calculates a first phase variation amount, which is a feature amount, for each pixel (each coordinate position) of the first phase difference image Z1. The first phase variation amount is the standard deviation of the first phase difference image Z1. The processing unit 5 calculates a second phase variation amount, which is a feature amount, for each pixel of the second phase difference image Z2. The second phase variation amount is the standard deviation of the second phase difference image Z2. The processing unit 5 calculates a third phase variation amount, which is a feature amount, for each pixel of the third phase difference image Z3. The third phase variation amount is the standard deviation of the third phase difference image Z3.

[0044] The processing unit 5 acquires the randomness of each pixel based on the first to third phase fluctuation amounts of each pixel (step S14). The randomness is a feature vector in which the first to third phase fluctuation amounts are arranged. The randomness is a multidimensional parameter, and here, is a three-dimensional parameter. The randomness is acquired for each pixel of the image. According to such a randomness, since it is obtained by comparing phase difference images using several types of smoothing filters, it is possible to classify phase structures with different frequencies.

[0045] The processing unit 5 extracts the foreign matter 9 in the interference image. Specifically, the processing unit 5 extracts the foreign matter 9 using the amount of local variation in the phase difference image as an index. The example shown in FIG. 12 is a diagram showing an image of local phase variation in the second phase difference image Z2 of ΔL=4. The processing unit 5 cuts out a region of pixels showing local phase variation exceeding a threshold in the second phase difference image Z2 as a region where the foreign matter 9 exists. Note that the process of extracting the foreign matter 9 is not particularly limited, and various known methods can be used, such as using a bright field image captured separately.

[0046] The processing unit 5 refers to the foreign object classification model stored in the storage area 51 and determines the type of the foreign object 9 from the acquired degree of disorder. The foreign object classification model is a classification standard that is a standard for classifying the foreign object 9. The foreign object classification model corresponds to a classification function that quantifies the "reflectivity foreign object degree" or the "transmittance foreign object degree" by substituting the degree of disorder. The reflectivity foreign object degree is an index that can be linked to each foreign object 9 and is a value that represents the degree of reflectivity of the foreign object 9 with respect to the first light L1. The transmittance foreign object degree is an index that can be linked to each foreign object 9 and is a value that represents the degree of transmittance of the foreign object 9 with respect to the first light L1. The foreign object classification model is obtained by defining a classification surface (boundary surface) that can classify multiple types of foreign objects 9 in a multidimensional space. For example, the foreign object classification model shown in FIG. 13 is a model that classifies three types of foreign objects and is obtained by defining a classification surface MP in a three-dimensional space with the first to third phase fluctuation amounts as axes.

[0047] The display unit 53 displays information relating to the degree of disorder calculated by the processing unit 5. The display unit 53 displays information relating to the type of foreign matter 9 determined by the processing unit 5. The display unit 53 constitutes an output unit that outputs information relating to the degree of disorder acquired by the processing unit 5. For example, the display unit 53 displays a reflectivity foreign matter degree table Tb (see FIG. 14) relating to the reflectivity foreign matter degree of each foreign matter 9 as information relating to the degree of disorder.

[0048] The reflectivity foreign matter degree table Tb is generated by the processing unit 5. The reflectivity foreign matter degree table Tb is a table showing the positions of the plurality of foreign matters 9 in the interference image and the reflectivity foreign matter degree of each of the plurality of foreign matters 9. The "area" of the reflectivity foreign matter degree table Tb is the area of ​​the area corresponding to each foreign matter 9 in the interference image. The "volume" of the reflectivity foreign matter degree table Tb is the value obtained by integrating the brightness of the pixels included in the area corresponding to each foreign matter 9 in the interference image over the entire area. For example, the "volume" of an area having 2×2 pixels and a phase value of 3 [radian] is 2×2×3=12 [radian·pixel^2]. The unit may be converted to [radian·μm^2] or may be converted to [μm^3] using the proportional relationship between the thickness of the foreign matter and the phase value. In the example of the reflective foreign matter degree table Tb shown in FIG. 14, a type of foreign matter 9 having a reflective foreign matter degree of 0.9999 can be determined to be a reflective foreign matter 91, and a type of foreign matter 9 having a reflective foreign matter degree of 0.02 can be determined to be a transmissive foreign matter 92.

[0049] The learning unit 54 generates a foreign substance classification model by machine learning using as training data a plurality of types of interference images in the case where the foreign substance 9 is attached to the observation target object 8. The learning unit 54 stores the generated foreign substance classification model in the memory area 51. An example of a method for learning the foreign substance classification model will be described in detail below with reference to the flowchart in FIG.

[0050] First, phase images of multiple types of observation objects 8 to which foreign matter 9 is attached are obtained as teacher data (step S21). The above step S21 can be realized by performing a step of detecting the interference light L3 by the image sensor 4 to obtain a phase image for multiple types of observation objects 8 to which foreign matter 9 is attached. For example, the phase image as teacher data includes information on the type of each foreign matter 9 included in the interference phase image as annotation information. The phase image as teacher data is limited to a quantitative phase image and an image mathematically equivalent thereto. For example, an image in which the real part and the imaginary part of a phase image are stored as complex data can be used to create a corresponding quantitative phase image by arctangent calculation, and therefore can be said to be mathematically equivalent to a quantitative phase image. The phase image as teacher data may be any of the following, or a combination thereof. (a) An interference image measured in advance by the manufacturer of the interference observation device 1 (hereinafter, simply referred to as the "manufacturer") and stored in a computer on a network. (b) An interference image measured in advance by a user of the interference observation device 1 (hereinafter, simply referred to as "user") and stored in a computer managed by the user. (c) Interference image measured by the user when generating a foreign object classification model

[0051] Subsequently, the multiple types of phase images acquired in step S21 are stored in the storage area 51 (step S22). The learning unit 54 generates a foreign object classification model by machine learning using the multiple types of stored phase images as teacher data (step S23). The type and / or configuration of the algorithm used to generate the foreign object classification model is not particularly limited. The type of algorithm is, for example, a random forest decision tree, a multi-layer perceptron (neural network), etc. The configuration of the algorithm is the number of intermediate layers, the number of units in each layer, the number of branches, the objective variable, the explanatory variable, etc. The type and / or configuration of the algorithm may be generated by a support vector machine or deep learning. When the foreign object classification model is a model for classifying two types of foreign objects, the type and / or configuration of the algorithm may be generated by logistic regression analysis.

[0052] The step S22 may be performed by the manufacturer, in which case the obtained interference images may be stored online or in an image database attached to the device. The step S22 may be performed by the user, in which case the obtained interference images may be stored in a file. The step S23 may be performed by the manufacturer or the user.

[0053] [Example] Next, an interference observation method using the interference observation device 1 will be described with reference to Fig. 16. In the following example, a case will be illustrated in which an observation surface R inside an observation object 8 is observed and a foreign matter 9 is found to be attached to the surface (outer surface) of a film part 82 of the observation object 8.

[0054] First, the imaging element 4 acquires an image of the observation object 8 (step S1). In the above step S1, the position of the objective lens 13 is adjusted so that the objective lens 13 is focused on the observation surface R of the observation object 8, and an interference image is acquired by the imaging element 4. Specifically, adjustment is performed so that the observation surface R and the light receiving surface of the imaging element 4 are optically conjugate with each other due to optical imaging by the objective lens 13 and the lens 41. The processing unit 5 executes the processes of the above steps S11 to S14 and acquires the degree of disorder of each pixel in the interference image (step S2).

[0055] Next, the processing unit 5 extracts the foreign matter 9 in the interference image (step S3). In the above step S3, the pixels (coordinate positions) where each foreign matter 9 exists in the interference image are extracted using the local fluctuation amount of the phase difference image obtained from the interference image as an index. Next, the processing unit 5 acquires the degree of randomness of the region of the foreign matter 9 in the interference image. The processing unit 5 acquires the degree of randomness of the image of each foreign matter region based on the degree of randomness of each pixel of the interference image acquired in the above step S2 and the pixels of each foreign matter 9 extracted in the above step S3. Then, the processing unit 5 uses the foreign matter classification model stored in the memory area 51 to discriminate the foreign matter from the degree of randomness of the image of each foreign matter region (step S4: processing step). The display unit 53 displays the reflective foreign matter degree table Tb as an output of information on the degree of randomness of the interference image in the region of each foreign matter 9 (step S5: output step). In the above step S5, the display unit 53 may further display information on the type of each foreign matter 9.

[0056] [Action and Effects] It is found that in an interference image of an observation object 8 to which a foreign object 9 is attached, the degree of disorder in the area of ​​the foreign object 9 differs depending on the type of the foreign object 9. For example, in the case of a reflective foreign object 91, the foreign object 9 scatters the first light L1 and the reflected light from the foreign object 9 and the reflected light from the observation surface (reflection surface) R overlap, so that the image becomes like a scattering diffraction pattern, but it is found that this does not occur easily in the case of a transparent foreign object 92. Therefore, it is found that if the degree of disorder in the area of ​​the foreign object 9 in the interference image can be obtained, it becomes possible to accurately discriminate the foreign object 9, which is difficult to discriminate only by the aspect ratio or optical volume. In this regard, in the interference observation device 1 and the interference observation method, when a foreign object 9 is attached to the observation object 8, information on the degree of disorder in the area of ​​the foreign object 9 in the interference image is output. Therefore, according to the interference observation apparatus 1 and the interference observation method, based on information relating to the degree of disorder in the region of the foreign matter 9 in the output interference image, it is possible to distinguish and identify the type of foreign matter 9 adhering to the observation object 8, i.e., to discriminate the foreign matter 9.

[0057] The interference observation device 1 and the interference observation method include a memory area 51 that stores a foreign object classification model, and the processing unit 5 refers to the foreign object classification model stored in the memory area 51 and discriminates the type of foreign object from the acquired degree of disorder. In this case, it becomes possible to discriminate the foreign object 9 adhering to the observation target object 8 by referring to the foreign object classification model in the memory area 51.

[0058] The interference observation device 1 and the interference observation method include a learning unit 54 that generates a foreign matter classification model by machine learning using as training data a plurality of interference images in the case where a foreign matter 9 adheres to an observation target 8. In this case, it is possible to generate a foreign matter classification model by machine learning.

[0059] In the interference observation apparatus 1 and the interference observation method, the foreign matter 9 includes a reflective foreign matter and a transmissive foreign matter. In this case, it is possible to distinguish whether the foreign matter 9 is a reflective foreign matter or a transmissive foreign matter. In the interference observation apparatus 1 and the interference observation method, the reflective foreign matter 91 includes silicon, which is a semiconductor, and the transmissive foreign matter 92 includes an organic matter. In this case, it is possible to distinguish whether the foreign matter 9 attached to the observation target 8 is silicon having reflectivity or an organic matter having transmissivity.

[0060] The interference observation device 1 and the interference observation method include a display unit 53 that displays information about the degree of disorder. In this case, the information about the degree of disorder in the region of the foreign substance 9 in the interference image can be easily grasped.

[0061] In the interference observation device 1 and the interference observation method, in the above steps S11 to S14 for acquiring the degree of randomness, a phase difference image based on a smoothed image of a complex image is used, so that the amount of local phase variation in an interference image including phase aliasing of 2π or more can be quantified. Since phase unwrapping is not used in the image processing in the above steps S11 to S14, there is no influence of various errors caused by phase unwrapping. Moreover, regardless of whether the image includes phase aliasing of 2π or more, the same calculation can be used to extract locations with large local phase variations.

[0062] In the interference observation device 1 and the interference observation method, the phase variation (standard deviation) of the phase difference image is used as an example of a multidimensional parameter of the degree of disorder. The phase variation of the phase difference image can be evaluated in many different ways by changing the size of the smoothing filter, so it is easy to make it multidimensional. Another example of the multidimensional parameter of the degree of disorder may be the contrast difference of the reflection intensity of the interference fringe amplitude in the region of the foreign substance 9 compared to the surroundings.

[0063] In addition, the interference optical system 3 of this embodiment is configured to be able to change the optical path length (reference optical path length) of the optical path of the second light L2. In this case, as exemplified below, by changing the optical path length of the optical path of the second light L2, it is possible to highlight the foreign matter 9 attached to the surface other than the observation surface R of the observation target 8 in the acquired interference image, and it is possible to easily distinguish the foreign matter 9. That is, the processing unit 5 controls the piezoelectric element 19 to move the reference mirror 15 to the front position (first position), and the image sensor 4 acquires the first phase image. The processing unit 5 controls the piezoelectric element 19 to move the reference mirror 15 to the rear position (second position), and the image sensor 4 acquires the second phase image. The processing unit 5 performs threshold processing, which is a known method, on the average image of the first phase image K1 and the second phase image K2 to extract only bright points above the threshold, and acquires a bright point extraction phase image, which is an interference image in which the foreign matter 9 stands out. By acquiring the degree of disorder in the area of ​​the foreign matter 9 in this bright point extraction phase image, it is possible to easily distinguish the foreign matter 9. In this case, a stepping motor 18 may be used as a moving mechanism for the reference mirror 15 instead of the piezoelectric element 19. The focal condition of the optical path of the second light L2 may be changed by moving the reference objective lens 14. Instead of extracting bright spots using an average image of the first phase image K1 and the second phase image K2, either the first phase image K1 or the second phase image K2 may be used.

[0064] <Modification> The above-described aspect of the present invention is not limited to the above-described embodiment.

[0065] In the above embodiment, the randomness is a three-dimensional parameter, but the present invention is not limited to this, and the randomness may be a multidimensional parameter having four or more dimensions. For example, the dimension of the randomness may be arbitrarily increased by setting four or more types of smoothing window sizes when acquiring multiple types of smoothed complex images. In addition, the randomness may be a one-dimensional or two-dimensional parameter by reducing the dimension using a method such as principal component analysis. As an example, the randomness may be a two-dimensional parameter in which the first and second phase fluctuation amounts are arranged. In this case, the foreign object classification model may be a model for classifying three types of foreign objects, as shown in FIG. 17, for example, and may be obtained by defining a classification curve MP2 in a two-dimensional space with the first and second phase fluctuation amounts as axes.

[0066] In the above embodiment, the foreign object classification model generated by the learning unit 54 through machine learning is stored in the memory area 51, but this is not limiting. The learning unit 54 may not be provided, and a foreign object classification model generated in advance by a manufacturer or a user may be stored in the memory area 51. In this case, the hardware used to generate the foreign object classification model may be the user's personal computer or a cloud server. This reduces the amount of data to be read, and speeds up the operation during initial setup.

[0067] In the above embodiment, the reflectivity foreign matter degree table Tb relating to the reflectivity foreign matter degree of each foreign matter 9 is displayed on the display unit 53, but instead of or in addition to this, information regarding the transmittance foreign matter degree may be displayed on the display unit 53, or information regarding other degrees of disorder may be displayed on the display unit 53. In the above embodiment, after the reflectivity foreign matter degree or the transmittance foreign matter degree is quantified, it may be determined whether it is greater or smaller than a threshold value, and the foreign matter 9 may be discriminated based on the determination result.

[0068] In the above embodiment, the display unit 53 is provided as the output unit, but the output unit is not limited to the display unit 53, and may output information related to the degree of disorder acquired by the processing unit 5. For example, the output unit may be a transmission unit that transmits information related to the degree of disorder. For example, the output unit may be a part that outputs information related to the degree of disorder to an element outside or inside the interference observation device 1. In the above embodiment, instead of or in addition to displaying information related to the degree of disorder on the display unit 53, the information may be stored in the memory area 51 or a separate storage medium, or may be transmitted to the outside.

[0069] In the above embodiment, the foreign matter classification model is acquired by reading it from the memory area 51, but it may be acquired by downloading it from an external source such as a cloud server, or by reading it from a recipe database attached to the apparatus. In the above embodiment, various objects may be used as the observation target 8. For example, the observation target may be a so-called capped device, a so-called bonded wafer, or a single wafer.

[0070] In the above embodiment, the foreign matter 9 is extracted (step S3) after the degree of disorder is obtained (step S2), but the degree of disorder may be obtained after the extraction of the foreign matter 9. In this case, for example, a region in the interference image where the foreign matter 9 exists is extracted by threshold processing or the like, and the degree of disorder of the interference image of the extracted foreign matter 9 is calculated. Incidentally, when the foreign matter 9 is extracted after the degree of disorder of the interference image is obtained, simple processing is possible for the interference image, and therefore the processing speed can be increased.

[0071] In the above embodiment, the focus condition of the optical path of the second light L2 is changed by moving the reference mirror 15. Alternatively or additionally, the focus condition of the optical path of the second light L2 may be changed by moving the reference objective lens 14. In this case, the processing unit 5 may construct a first interference image based on a plurality of interference images acquired by the imaging element 4 in a state in which the reference objective lens 14 is moved to a front position by controlling the stepping motor 17. The processing unit 5 may construct a second interference image based on a plurality of interference images acquired by the imaging element 4 in a state in which the reference objective lens 14 is moved to a rear position by controlling the stepping motor 17. Then, the processing unit 5 may separate the observation surface information and the non-observation surface information based on the first interference image and the second interference image.

[0072] In the above embodiment, the objective lens 13 is moved along the Z direction by the actuator 16 to adjust the relative position between the objective lens 13 and the observation object 8. Instead of or in addition to this, the stage S may be moved along the Z direction to adjust the relative position. In this case, the stage S is configured to be movable in the Z direction in addition to the XY directions. In this case, the actuator 16 may be omitted. The stage S may be movable along a direction intersecting the direction in which the first light L1 is incident on the observation object 8, and may be movable in a direction inclined with respect to the XY plane, for example. The actuators that drive the reference objective lens 14 and the reference mirror 15 are not limited to the stepping motors 17 and 18, and may be other actuators such as a servo motor.

[0073] In the above embodiment, the interference optical system 3 is configured as a Linnik interference type, but the interference optical system 3 may be configured as a Michelson interference type or a Mirau interference type. The reference objective lens 14 may be omitted. In the above embodiment, the number of interference images used to construct a phase image is not particularly limited, and may be one or more.

[0074] The respective configurations in the above-described embodiment and the above-described modified examples are not limited to the above-described materials and shapes, and various materials and shapes can be applied. Furthermore, the respective configurations in the above-described embodiment and modified examples can be arbitrarily applied to the respective configurations in other embodiments or modified examples. [Explanation of symbols]

[0075] 1...Interference observation device, 2...Light source, 3...Interference optical system, 4...Imaging element, 5...Processing unit, 8...Object to be observed, 9...Foreign body, 14...Reference objective lens, 15...Reference mirror, 51...Memory area (memory unit), 53...Display unit (output unit), 91...Reflective foreign body, 92...Transparent foreign body, L1...First light, L2...Second light, L3...Interference light, R...Observation surface, Tb...Reflective foreign body degree table (information regarding disorder degree).

Claims

1. A light source that outputs light; an interference optical system having a reference mirror, which splits light output from the light source into a first light and a second light, and outputs interference light between the first light reflected by an observation object and the second light reflected by the reference mirror; an imaging element for detecting the interference light; a processing unit that acquires an interference image based on a detection result of the imaging element, and acquires a degree of disorder of an image of a foreign matter region adhering to the observation object in the interference image; and an output unit that outputs information regarding the degree of disorder acquired by the processing unit.

2. a storage unit that stores a foreign object classification model relating to a relationship between the degree of disorder of the foreign object and the type of the foreign object, 2. The interference observation apparatus according to claim 1, wherein said processing unit refers to said foreign object classification model stored in said storage unit to determine the type of said foreign object from the acquired degree of disorder.

3. 3. The interference observation apparatus according to claim 2, further comprising a learning unit that generates a foreign matter classification model relating to a relationship between the degree of disorder of the foreign matter and the type of the foreign matter by machine learning using a plurality of types of interference images in a case where the foreign matter is attached to the observation object as training data.

4. 4. The interference observation apparatus according to claim 2, wherein the foreign matter includes a reflective foreign matter and a transparent foreign matter.

5. the reflective foreign matter includes a semiconductor; The interference observation apparatus according to claim 4 , wherein the transparent foreign matter includes an organic matter.

6. 2. The interference observation apparatus according to claim 1, wherein the output section includes a display section that displays information related to the degree of disorder.

7. 2. The interference observation apparatus according to claim 1, wherein the interference optical system is configured so that an optical path length of the optical path of the second light is variable.

8. a light output step of outputting light from a light source; an interference light output step of splitting the light output from the light source into a first light and a second light, and outputting interference light between the first light reflected by an observation object and the second light reflected by a reference mirror; an interference light detection step of detecting the interference light by an image sensor; a processing step of acquiring an interference image based on a detection result of the imaging element, and acquiring a degree of disorder of an image of a foreign matter region adhering to the observation object in the interference image; and an output step of outputting information regarding the degree of disorder acquired in the processing step.

Images