CHARGED PARTICLE BLASTING DEVICE AND SURFACE CONDITION ASSESSMENT METHOD

Abstract

An image processing device 133, which transmits an inclined charged particle beam image of a three-dimensional pattern from a signal processing device 132, wherein the inclined charged particle beam image is a charged particle beam image produced by irradiating a sample with a charged particle beam in a state in which a direction in which the charged particle beam is incident on the sample and a normal of a sample placement surface of a sample carrier have an inclination angle of a predetermined size, and the image processing device evaluates a surface state of a pattern sidewall based on a local brightness distribution in a sidewall image, which is an image of a pattern sidewall of the three-dimensional pattern contained in the inclined charged particle beam image.

Description

Technical field

[0001] The present invention relates to a charged particle beam device and a surface condition evaluation method. State of the art

[0002] In a semiconductor device manufacturing process, an etching step is generally used to create a circuit pattern on a wafer surface. In this etching process, a desired shape is formed on the wafer surface by removing unwanted areas and using a chemical reaction with a liquid, gas, or plasma.

[0003] In addition, PTL 1 discloses a method for evaluating the degree of inclination of a pattern edge segment in order to accurately evaluate a two-dimensional shape of a pattern basis on a charged particle beam image. List of literature on patent literature

[0004] PTL 1: JP2009-222454A Summary of the invention: Technical problem

[0005] PTL 1 refers to a two-dimensional pattern form; however, given the ongoing miniaturization and three-dimensionalization of semiconductor devices, it is necessary to evaluate the pattern three-dimensionally as well. Consequently, there is an increasing need to verify whether a pattern surface, which is difficult to observe from above, has been adequately machined.

[0006] Ideally, if the etching process progresses smoothly, the treated sidewall will become a smooth surface. However, due to a failure of the etching equipment, an external factor (such as a change in the flow rate of an etching gas, a change in the gas composition, or a fluctuation in preload), or a change in the atmosphere surrounding the etching section, the etching process itself may not progress as expected. In this case, the smoothness of the treated surface is lost, and a characteristic deformation may occur.

[0007] Furthermore, an etching defect can lead to a performance defect or degradation of a finished device, which in turn reduces the yield. As a method for evaluating the quality of a side surface of a three-dimensional shape, such as a trench sidewall, after machining and detecting an irregularity, cross-sectional inspection using a scanning electron microscope (SEM) is also performed in the prior art. However, such cross-sectional inspection is a destructive examination that also involves cutting the sample and necessitates wafer alignment. Therefore, it is difficult to use cross-sectional inspection as an in-line examination in a semiconductor device manufacturing process, and the wafer cutting process incurs labor and time costs.For this reason, there is a need for a technique to detect an irregularity at the earliest possible stage of the process, without incurring costs such as "wehalt-out", and to return the irregularity to an etching process. Solution to the problem

[0008] A charged particle beam device according to one embodiment of the invention comprises: a sample carrier on which a sample with a three-dimensional pattern formed on one of its surfaces is placed; an optical charged particle system configured to irradiate the sample with a charged particle beam; a detection system configured to detect a signal electron generated by irradiating the sample with the charged particle beam; a signal processing device configured to generate a charged particle beam image based on the detection of the signal electron by the detection system; and an image processing device configured to perform an image processing of the charged particle beam image.The image processing device further receives an inclined charged particle beam image of the three-dimensional pattern from the signal processing device, wherein the inclined charged particle beam image is a charged particle beam image generated by irradiating the sample with the charged particle beam in a state in which a direction in which the charged particle beam is incident on the sample, a normal of a sample placement surface of the sample carrier has an inclination angle of a predetermined size, and the image processing device evaluates a surface state of a pattern sidewall based on a local brightness distribution in a sidewall image, which is an image of the pattern sidewall of the three-dimensional pattern contained in the inclined charged particle beam image. Advantageous effects of the invention

[0009] A charged particle beam device is provided that is capable of inspecting a sample sidewall of a three-dimensional pattern without destroying the sample. Further technical problems and novel features are further clarified based on the descriptions in this document and the accompanying drawings. Brief description of the drawings [ Fig. 1] Fig. Figure 1 shows a configuration example of a charged particle beam device. [ Fig. 2A] Fig. Figure 2A shows a schematic view of an inclined SEM image. [ Fig. 2B] Fig. Figure 2B shows a schematic view of an inclined SEM image. [ Fig. 2C] Fig. Figure 2C shows a schematic view of an inclined SEM image. [ Fig. 3] Fig. Figure 3 shows a process for evaluating the surface condition of a sample sidewall of a three-dimensional pattern. [ Fig. 4A] Fig. Figure 4A shows a diagram illustrating a difference in a tilted SEM image due to a difference in the tilt angle. [ Fig. 4B] Fig. Figure 4B shows a diagram illustrating a difference in a tilted SEM image due to a difference in the tilt angle. [ Fig. 4C] Fig. Figure 4C shows a diagram illustrating a difference in a tilted SEM image due to a difference in the tilt angle. [ Fig. 5] Fig. Figure 5 shows the sequence of a tilt angle adjustment process. [ Fig. 6] Fig. Figure 6 shows the sequence of an analysis process of a charged particle beam image. [ Fig. 7] Fig. Figure 7 shows an example of a GUI. [ Fig. 8] Fig. Figure 8 shows a schematic diagram illustrating a relationship between a processing rating and a quality rating (equipment characteristics). [ Fig. 9] Fig. Figure 9 shows an example of a GUI. Description of the embodiments

[0010] Fig. Figure 1 shows a configuration example of a charged particle beam device for evaluating the sidewall quality (surface condition) of a three-dimensional pattern formed on a semiconductor wafer. A scanning electron microscope, which generates an image based on signal electrons emitted from the surface of a sample by scanning the sample with an electron beam, is described as an example. The scanning electron microscope 100 includes as its main components a column 101 containing an optical electron system, a support mechanism system, and a detection system, a control system 121, and a signal processing system 131. Fig. Figure 1 schematically shows the basic elements that make up the scanning electron microscope 100.

[0011] The optical electron system represents a mechanism that shapes an electron beam 103 into a tiny point and scans a sample with the electron beam 103, and includes an electron source 102, a condenser lens 106, an aperture 107, a deflector 108, an objective lens 109 and the like.

[0012] The carrier mechanism system also has a function for moving, rotating, and tilting a sample 112 under test in a horizontal or up-down direction while holding the sample 112 in a stable position. A sample carrier 110, on which the sample 112 is placed, and a carrier drive unit 111, which changes the position of the sample carrier 110, are shown. The carrier drive unit 111 further has a function for rotating the sample carrier 110 about an axis of rotation perpendicular to a sample placement surface of the sample carrier 110 and a function for tilting the sample carrier 110 about the axis of rotation with respect to a horizontal plane, in order to tilt the sample 112 so that the electron beam 103 is incident on the three-dimensional pattern formed on the sample 112 at a predetermined angle of incidence.

[0013] The detection system further includes a detector that detects signal electrons generated when the sample 112 is irradiated with the electron beam 103. A secondary electron detector 105, which detects secondary electrons 104, is shown for this purpose. In the present embodiment, an example is shown for evaluating etching quality based on a charged particle beam image derived from the detected secondary electrons 104, whereby the signal electrons detected by the detection system need not be limited to the secondary electrons.

[0014] The control system 121 further controls the optical electron system, the support mechanism system, and the detection system, which are integrated into the column 101. For this purpose, a control system is shown as the control system 121, which includes a single control unit that controls individual elements provided in the optical electron system, the support mechanism system, and the detection system, and an overall control unit that sets the operations of the individual elements so that these individual elements interact with each other to perform a desired operation.

[0015] The signal processing system 131 also includes a signal processing device that generates a charged particle beam image based on signal electrons detected by the detection system, and an image processing device 133 that performs an image processing of the charged particle beam image. It should be noted that the image processing device 133 is located in place of a device directly connected to the signal processing device 132, as described in [reference missing]. Fig. 1 shown, can also represent an image processing server and be connected to the signal processing device 132 via a network.

[0016] A display 141 further shows the charged particle beam image (SEM image) generated by the signal processing device 132.

[0017] Fig. 2A to Fig. Figures 2C are schematic diagrams of SEM images (hereinafter referred to as inclined SEM images) obtained by tilting the sample 112 relative to the horizontal plane to form a trench pattern created by etching. Each inclined SEM image is also shown with coordinate axes, where a longitudinal direction of the trench pattern represents a y-direction and a direction perpendicular to it represents an x-direction. Each inclined SEM image contains a sidewall image 201, a bottom surface image 202, and a surface image 203. The sidewall image 201 represents an image of a pattern sidewall (trench sidewall), the bottom surface image 202 represents an image of a pattern bottom surface (trench bottom), and the surface image 203 represents an image of a surface of the sample. Furthermore, a high-brightness line appears at a boundary 204 between the bottom surface image 202 and the surface image 203, indicating a trench edge.

[0018] All of the Fig. 2A to Fig. 2C represents examples where an irregularity occurs in the trench side wall. Fig. 2A is in particular an example where a mottled pattern appears on the trench side wall. Fig. 2B represents an example where strip steps (arc edges) are created on the trench side wall parallel to the longitudinal direction (y-direction) of the trench pattern. Fig. 2C also provides an example where strip steps are created parallel to the direction (x-direction) and perpendicular to the longitudinal direction of the trench.

[0019] Fig. Figure 3 shows a process for assessing the surface condition of the sample sidewall of the three-dimensional pattern. An example is described below in which the trench pattern is selected as the three-dimensional pattern formed in sample 112, an inclined SEM image of the trench pattern is obtained, and a mottled defect (see Figure 3) is identified. Fig. 2A) the trench side wall is evaluated.

[0020] For this purpose, the sample carrier 110 is first moved to an observation target position by the carrier drive unit 111. A suitable tilt angle of the sample carrier 110 is selected according to the trench pattern to be observed (S01). Any difference in the tilted SEM image due to a difference in the tilt angle will subsequently be analyzed with reference to the Fig. 4A to Fig. 4C described.

[0021] Fig. Figure 4A shows a sample cross-section 401, an inclined SEM image 402, and a line profile 403 in a case where there is no inclination angle (inclination angle T = 0). In each figure, the positions of trench edges a and b in the x-direction are shown. The inclination angle T represents an angle formed by a vertical direction (z-direction) and a normal to the sample placement surface of the sample carrier 110. The line profile 403 is a graph showing a change in brightness along a line 404, which is shown in the inclined SEM image 402. The line 404 is defined along the direction (x-direction) perpendicular to the longitudinal direction (y-direction) of the trench pattern. In a case where there is no inclination angle, since the side wall of the trench pattern is parallel to an optical axis of the electron beam 103, the inclined SEM image 402 also does not contain a side wall image.

[0022] Fig. Figure 4B shows a sample cross-section 411, an inclined SEM image 412, and a line profile 413, in a case where the inclination angle T is appropriate. In each figure, the positions of trench edges c and e and a trench bottom surface d in the x-direction are shown. The line profile 413 is a graph showing a change in brightness along a line 414, which is shown in the inclined SEM image 412. Here, the inclination angle T being "appropriate" means that the inclined SEM image contains a sidewall image that represents the entire trench sidewall to be evaluated, and that the sidewall image occupies a large proportion in the inclined SEM image.

[0023] Fig. Figure 4C also shows a sample cross-section 421, an inclined SEM image 422, and a line profile 423 in a case where the inclination angle T is excessive. In each figure, the positions of trench edges f and g in the x-direction are shown. The line profile 423 is also a graph showing a change in brightness along a line 424, which is shown in the inclined SEM image 422. Here, the inclination angle T being "excessive" means that the inclined SEM image contains a sidewall image that only shows an area near the trench edge in the trench sidewall being evaluated. In other words, it is not possible to observe an area near the trench bottom in the trench sidewall being evaluated.

[0024] In a mass production process, when the surface condition evaluation process of the pattern sidewall of the three-dimensional pattern is performed in-line, the overall control unit of the control system 121 pre-sets a suitable inclination angle for each three-dimensional pattern to be evaluated and then reads and sets the inclination angle corresponding to the three-dimensional pattern to be evaluated. Fig. Figure 5 shows a sequence of a tilt angle adjustment process that is executed when the tilt angle is set. A suitable tilt angle can also be set using this sequence in a case where the surface condition of the pattern sidewall of the three-dimensional pattern is evaluated offline or individually. This sequence includes a process of the overall control unit of the control system 121 and a process of the signal processing device 132.

[0025] For this purpose, the overall control unit of the control system 121 first sets the inclination angle T = t (S11). Here, t is an arbitrary value. The signal processing device 132 also acquires a line profile from the inclined SEM image, which is generated based on the signal electrons detected by the detection system (S12). In the case of an actual image, in order to reduce noise and the influence of the surface conditions of the trench sidewall, as in Fig. 2A to Fig. 2C is shown, subsequently a large number of line profiles are recorded and an average of these is used as the line profile of the inclined SEM image.

[0026] As in Fig. 4A to Fig. As shown in Figure 4C, the line profile of the inclined SEM image changes, particularly at the sidewall image, the bottom surface image, the surface image, and its boundaries. Therefore, the signal processing device 132 specifies the position of the trench edge and image areas corresponding to the trench sidewall and trench bottom based on features such as a line profile peak and a difference peak, and further performs the following processes.

[0027] First, the signal processing device 132 determines whether a region (ground surface area) corresponding to the trench bottom is present in the line profile of the inclined SEM image (S13). If the ground surface area is not included, the overall control unit determines that the inclination angle T is excessive, sets the inclination angle T to t - Δt (S14), acquires an inclined SEM image at the inclination angle T = t - Δt to acquire a line profile (S12), and repeats the subsequent processes. It should be noted that Δt can be any positive value.

[0028] In a case where the floor area is included, the signal processing device 132 calculates a sidewall area width Ws and a floor area area width Wb (S15). As in Fig. As shown in Figure 4B, the sidewall area width Ws and the bottom area area width Wb are the length (distance between c and d) of an area (sidewall area) corresponding to the trench sidewall in the x-direction, and the length (distance between d and e) of the bottom area in the x-direction, respectively, in the inclined SEM image. The size of the sidewall area width Ws is then evaluated in relation to the bottom area area width Wb (S16). If Ws / Wb represents a predetermined value or greater, it is determined that a condition is met: the sidewall image has a large ratio in the inclined SEM image. This is because the smaller the inclination angle T, the smaller the sidewall area and the larger the bottom area; conversely, the larger the sidewall area and the smaller the bottom area.Therefore, the suitability of the slope angle T is determined by the size of the sidewall area width Ws relative to the bottom area area width Wb. If step S16 is satisfied, the slope angle t is further defined as the angle used to determine the surface condition of the trench sidewall (S18). Conversely, if Ws / Wb is smaller than the predetermined value, the slope angle T is set to t + Δt (S17), a tilted SEM image is acquired at the slope angle T = t + Δt to capture a line profile (S12), and subsequent processes are repeated.

[0029] The description is carried out under the assumption that the entire trench sidewall is to be assessed for the surface condition, whereby, if only a top surface (near the trench edge) of the trench sidewall is to be assessed, an angle can also be specified that is greater than that in the procedure in Fig. There are 5 fixed angles of inclination. Accordingly, it is possible to view an upper area of ​​the trench side wall to be assessed within a wider field of vision.

[0030] The description then returns to the process in Fig. 3. The sample is tilted to the angle of inclination of the selected quantity (S02), and a charged particle beam image is acquired (S03). The signal processing device 132 transmits the acquired charged particle beam image to the image processing device 133, and the image processing device 133 analyzes the acquired charged particle beam image (S04). The charged particle beam image to be analyzed is, for example, the one in Fig. 4B shows inclined SEM image 412.

[0031] The in Fig. The scanning electron microscope 100 shown contains the column 101, which is a vertical column, and the sample carrier 110 is inclined with respect to the horizontal plane by the carrier drive unit 111 to acquire the inclined SEM image. However, a method for providing an inclination angle of a predetermined magnitude between the direction in which the electron beam 103 is incident on the sample 112 and a normal to the sample placement surface of the sample carrier 110 need not be limited to this. The horizontally placed sample 112 can also be irradiated with the electron beam 103 in a state in which the optical axis of the electron beam 103 is inclined with respect to the vertical direction using the deflector 108.Furthermore, if the scanning electron microscope includes a tilted column (a column that is provided in a state in which a central axis of the column is tilted with respect to the vertical direction), a tilted SEM image can also be acquired by irradiating a horizontally placed sample with an electron beam from the tilted column.

[0032] Fig. Figure 6 shows a sequence of an analysis process of a charged particle beam image, which is carried out by the image processing device 133 in step S04. In this analysis, the surface condition of the sample sidewall, in particular the degree of defect occurrence, is evaluated based on a local brightness distribution in the sidewall image contained in the charged particle beam image.

[0033] First, a sidewall image, which represents an analysis target area, is extracted from the tilted SEM image (S21). As described above, the sidewall image can be specified based on the features of the line profile of the tilted SEM image. For example, in the tilted SEM image 412, it is possible to extract a band-shaped area with a left end at x-coordinate c and a right end at x-coordinate d. Subsequently, a background brightness variation is removed from the extracted analysis target area (S22). The background brightness variation represents a brightness change that occurs in an image caused by the three-dimensional structure of a three-dimensional pattern.In the brightness distribution of the trench wall image, secondary electrons generated on a side closer to the trench bottom have a higher probability of colliding with and being absorbed by the trench wall, thus reducing the ratio of secondary electrons reaching a detector. Therefore, the signal intensity on a side of the trench wall closer to the trench bottom is lower than the signal intensity on a side closer to the trench edge, resulting in a dark image. Since this brightness variation occurs regardless of the surface condition of the trench wall, it is necessary to remove this brightness variation.

[0034] For example, the line profile of the sidewall image is averaged along the longitudinal (y-direction) of the trench pattern, and the resulting average brightness value is subtracted from the brightness value of each pixel in the sidewall image. Accordingly, a brightness difference between a trench edge and a trench bottom caused by the three-dimensional structure can be compensated for based on the sidewall image. Alternatively, as another method for removing the background brightness variation, a brightness value of the sidewall image can be calculated as the background brightness variation by performing a planar or square surface fit and subtracted from the brightness value of each pixel in the sidewall image.Additionally, a low-frequency component of the sidewall image can be extracted by a low-pass filter using a fast Fourier transform (FFT), and the brightness difference between the trench edge side and the trench bottom side can be balanced by considering the low-frequency component as the background brightness change.

[0035] After the background brightness change has been removed from the sidewall image, contrast adjustment and noise reduction are also performed (p. 23). Accordingly, a defect (for example, a mottled pattern in the example in Fig. 2A) is highlighted on a sidewall surface. The sidewall image is then further subdivided into small areas, and an index value indicating the degree of defect is obtained for each small area.

[0036] For this purpose, the sidewall image is first subdivided into small areas of equal size (S24). Then, one of the subdivided small areas is selected (S25), and a feature of the brightness distribution of the small area is calculated (S26). Examples of the feature to be calculated include, in particular, a statistical feature (a maximum value, a minimum value, a mean, a standard deviation, a median value, a mode value, kurtosis, skewness, entropy, or the like) or a local feature (a local binary pattern feature [LBP], a histogram of oriented gradients [HOG] feature, or the like) of the brightness distribution. The index value, which indicates the degree of a defect in the small area, is calculated based on one or more such features.Here, the brightness distribution in the small area varies depending on whether a defect is present, and if so, what type and extent of the defect. Therefore, an index value indicating the degree of a defect in the small area is calculated based on the brightness distribution feature of the small area (S27). A method for calculating the index value can be defined according to a defect to be detected (for example, defects that are in the ). Fig. 2A to Fig. (2C are shown). Furthermore, the analysis ends when the index values ​​for all small areas are calculated (S28).

[0037] The above procedure for obtaining the index value is an example, where, for instance, a trained learning model for classifying the presence or absence of the defect contained in the small area, or the degree of a defect, can be created and stored in advance using the feature of the brightness distribution of the small area as an input, and a classification result by the learning model can be used as the index value.

[0038] Referring again to the description of the process in Fig. 3. A processing score, indicating the degree of normality or abnormality of a process, is then calculated based on the index value for each small area obtained as a result of such image analysis (S05). That is, by dividing the sidewall image into small areas, the index values ​​indicating the degree of defect, obtained based on the local brightness distribution, are integrated, and the processing score, indicating the degree of defect in the sample sidewall, is calculated. A simplest example of the processing score is a sum of index values ​​calculated for each small area. Alternatively, the degree of defect can also be assessed by adding positional information to the small area.Furthermore, a processing score can be determined in advance for a large number of sidewall images (reference images) with varying degrees of defects, and the processing score can be determined depending on the degree at which the distribution pattern of the index values ​​in the analyzed sidewall image is similar to the distribution pattern of the index values ​​in the reference image. Alternatively, instead of using the index value itself to calculate the processing score, the index value calculated for each small area can be converted once into binary data indicating the presence or absence of the defect, and the processing score can be determined based on the number of binary data elements indicating the presence of the defect or the distribution pattern of the binary data indicating the presence of the defect.

[0039] Subsequently, a result of the surface condition assessment of the trench side wall is displayed on the display 141, a display device of the image processing device 133 or an end device connected to the image processing device 133 (S06). Fig. Figure 7 is an example of this and specifically presents an example of a GUI that displays a monitoring result in an in-line investigation of the mass production process. A display screen 500 shows a graph 501 indicating a time-series change in the processing score. If the processing score exceeds a preset threshold Th, an alarm 502 is also displayed.

[0040] Here, the threshold Th for issuing an alarm can be determined based on the processing evaluation, based on a correlation between a quality assessment from a sample test or similar after device production and the processing evaluation. Examples of quality evaluations could also include electrical properties (resistance, current, voltage, dielectric strength, lifetime, etc.) or a defect rate.

[0041] Fig. Figure 8 is a schematic diagram showing a relationship between the processing rating and the quality rating (equipment characteristics). If the normality and abnormality of the equipment can be determined by a predetermined threshold Qth of the equipment characteristic, and the nature of the relationship between the processing rating and the equipment characteristic is known, it is possible to generate an alarm by linking the surface condition rating and a probability of defect occurrence.

[0042] Graph 511 presents an example where the processing score and the device property have a substantially linear relationship. In this case, a threshold Th1 for issuing an alarm can be set based on a value of the processing score, which is the device property Qth. Graph 512 presents an example where the device properties degrade rapidly when the processing score exceeds a certain change point. Since, in this example, the device property Qth is not satisfied when the processing score exceeds the change point, this change point can be set as a threshold Th2 for issuing an alarm.

[0043] As another example of the results display Fig. Figure 9 presents an example of a GUI that displays the processing score, which is calculated by performing the surface condition assessment of the pattern sidewall at specific coordinates of chip patterns that are regularly arranged on a wafer surface. A Fig. Display screen 600, shown in section 9, evaluates the processing score in three levels A to C and displays a processing score distribution on the wafer surface using a wafer map 601 and / or a scoring result table 602. The processing scores A to C can be obtained by detecting the same type of surface condition and classifying the surface conditions according to their degree. This corresponds, for example, to a case where macular results, as in Fig. 2A shows, detects, and displays the macular results separately according to the degree of defect. Alternatively, processing ratings A to C can detect various surface conditions and indicate the most conspicuous surface condition. Ratings A to C correspond, for example, to cases where the surface conditions, as in Fig. 2A to Fig. 2C is shown, detected, and the most conspicuous surface condition in each chip pattern is displayed.

[0044] The invention is not limited to the above embodiment and includes various modifications. For example, the flowchart in Fig.3. In the analysis step of the charged particle beam image (S04), the image is divided into small regions to analyze the surface condition, although the invention is not limited thereto. For example, semantic segmentation can also be performed on the extracted sidewall image to determine whether each pixel represents a pixel forming a defect, and the number of defects contained in the sidewall image can be obtained as the processing evaluation. In this case, it is not necessary to divide the image into small regions and extract the feature of the image.

[0045] As described above, embodiments and modifications have been described in such detail only to facilitate understanding of the invention; however, the invention need not necessarily be limited to those embodiments that include all of the configurations described above. Furthermore, a part of a configuration in one embodiment or modification can be replaced by a configuration in another embodiment or modification, and a configuration in one embodiment or modification can be added to a configuration in another embodiment or modification. A part of a configuration in each of the embodiments and modifications can also be added, deleted, or replaced by a different configuration. Reference symbol list 100 scanning electron microscopes 101st pillar 102 electron source 103 Electron beam 104 secondary electrons 105 Secondary electron detector 106 Condenser lens 107 Aperture 108 Deflector 109 Lens 110 sample carriers 111 Carrier drive unit 112 Sample 121 Tax system 131 Signal processing system 132 Signal processing device 133 Image processing device 141 advertisement 201 Side panel 202 Floor area image 203 Surface image 204 border 401, 411, 421 Sample cross-section 402, 412, 422 inclined SEM image 403, 413, 423 Line profile Lines 404, 414, and 424 500 display screen 501 Graph 502 Alarm 511, 512 Graph 600 display screen 601 wafer card 602 Evaluation results table QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] JP 2009-222454A

[0004]

Claims

[1] A charged particle beam device comprising: a sample holder on which a sample with a three-dimensional pattern formed on one of its surfaces is placed; an optical charged particle system designed to irradiate the sample with a charged particle beam; a detection system designed to detect a signal electron generated by irradiating the sample with the charged particle beam; a signal processing device configured to generate a charged particle beam image based on the detection of the signal electron by the detection system; and an image processing device which is set up to perform an image processing of the charged particle beam image, wherein The image processing device receives an inclined charged particle beam image of the three-dimensional pattern from the signal processing device. The inclined charged particle beam image is a charged particle beam image that is produced by irradiating the sample with the charged particle beam in a state in which a direction in which the charged particle beam is incident on the sample and a normal of a sample placement surface of the sample support have an inclination angle of a predetermined size, and The image processing device evaluates a surface state of a pattern sidewall based on a local brightness distribution in a sidewall image, which is an image of the pattern sidewall of the three-dimensional pattern contained in the inclined charged particle beam image. [2] The charged particle beam device according to claim 1, wherein the image processing device removes a background brightness change, which is a brightness change caused by a three-dimensional structure of the three-dimensional pattern, from the sidewall image prior to evaluating the surface state of the pattern sidewall. [3] The charged particle beam device according to claim 1, wherein the image processing device divides the sidewall image into a plurality of small areas, obtains an index value that indicates a degree of defect for each small area based on a feature of a brightness distribution in each of the small areas, and integrates the index values ​​obtained for each of the small areas to calculate a processing rating that indicates the surface condition of the pattern sidewall. [4] The charged particle beam device according to claim 3, wherein the image processing device stores a trained learning model that classifies the presence or absence of a defect or the degree of a defect in the small area based on the feature of the brightness distribution in the small area, and specifies a classification result obtained by the learning model as an index value of the small area. [5] The charged particle beam device according to claim 3, wherein the image processing device defines a sum of the index values ​​obtained for each of the small areas as the processing score, or determines the processing score according to a degree at which a distribution pattern of index values ​​in the side wall image is similar to a distribution pattern of index values ​​in a reference image, and The reference image is a multitude of sidewall images with varying degrees of defects, the processing evaluation values ​​of which are determined in advance. [6] The charged particle beam device according to claim 1, wherein the image processing device determines whether each pixel is a pixel forming a defect by performing semantic segmentation on the sidewall image, and specifies the number of pixels forming the defect contained in the sidewall image as a processing evaluation indicating the surface condition of the pattern sidewall. [7] The charged particle beam device according to claim 1, wherein the inclined charged particle beam image includes the side wall image, a bottom surface image which is an image of a sample bottom surface of the three-dimensional pattern, and a surface image which is an image of a surface of the sample. [8] The charged particle beam device according to claim 3, wherein an alarm is issued when the processing rating calculated by the image processing device exceeds a threshold. [9] The charged particle beam device according to claim 8, wherein the threshold is determined based on a correlation between the processing evaluation and a device property of a device containing the three-dimensional pattern. [10] A surface condition evaluation method for evaluating a surface condition of a pattern sidewall of a three-dimensional pattern formed on a surface of a sample, using a charged particle beam device, wherein the charged particle beam device comprises a sample carrier on which the sample is placed, an optical charged particle system that is set up to irradiate the sample with a charged particle beam, a detection system designed to detect a signal electron generated by irradiating the sample with the charged particle beam, a signal processing device that is set up to generate a charged particle beam image based on the detection of the signal electron by the detection system, and an image processing device configured to perform an image processing of the charged particle beam image, wherein the image processing device receives an inclined charged particle beam image of the three-dimensional pattern from the signal processing device, The inclined charged particle beam image is a charged particle beam image that is produced by irradiating the sample with the charged particle beam in a state in which a direction in which the charged particle beam is incident on the sample and a normal of a sample placement surface of the sample support have an inclination angle of a predetermined size, and The image processing device evaluates a surface state of a pattern sidewall based on a local brightness distribution in a sidewall image, which is an image of the pattern sidewall of the three-dimensional pattern contained in the inclined charged particle beam image.

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