Photoelectric evolution defect inspection

Abstract

A charged particle beam system may include a primary source (100), a secondary source (200), and a controller. The primary source may be configured to emit a charged particle beam (241) along an optical axis onto a region of a sample (201). The secondary source may be configured to irradiate (242) a region of the sample. The controller may be configured to control the charged particle beam system to change parameters of the output of the secondary source. An imaging method may include: emitting a charged particle beam (241) onto a region of a sample (201), irradiating (242) a region of the sample with the secondary source (200), and changing parameters of the output of the secondary source.

Description

[0001] Cross-references to related applications

[0002] This application claims priority to U.S. Application 62 / 894,573, filed August 30, 2019, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This article describes imaging systems and methods that can be used in the field of charged particle beam systems, and more specifically, the evolution of sources that can be used to detect defects during sample inspection. Background Technology

[0004] During the manufacturing process of integrated circuits (ICs), incomplete or finished circuit components can be inspected to ensure they are manufactured according to the design and free of defects. One way to detect defects is through voltage contrast imaging. In voltage contrast imaging, a charged particle beam system can scan the primary beam across the sample while secondary irradiation is applied to increase surface charge on the sample.

[0005] Applying secondary irradiation to wafers during charged particle beam inspection allows for various enhancements. For example, voltage contrast can be applied in charged particle inspection systems to enhance methods that distinguish features based on grayscale levels. To detect voltage contrast defects, a pre-charging process is typically employed, where charged particles are applied to the section to be inspected before inspection. Pre-charging can amplify the difference in grayscale levels between defective and non-defective structures. Therefore, voltage contrast can enhance imaging, making defective features appear different from surrounding non-defective features under inspection, and allowing certain defects to become detectable. Voltage contrast can also improve the signal-to-noise ratio (SNR) of the inspection method. Summary of the Invention

[0006] Embodiments of this disclosure provide systems and methods for imaging based on charged particle beams. In some embodiments, a charged particle beam system may be provided, including a primary source, a secondary source, and a controller. The primary source may be configured to emit a charged particle beam along an optical axis onto a region of a sample. The secondary source may be configured to irradiate a region of the sample. The controller may have a circuit system and may be configured to control the charged particle beam system to change parameters of the output of the secondary source. The controller may be configured to acquire a first image of the sample with first parameters of the output of the secondary source, and a second image of the sample with second parameters of the output of the secondary source.

[0007] An imaging method may also be provided, comprising: emitting a charged particle beam onto a region of a sample, irradiating the region of the sample with a secondary source, acquiring a first image of the sample with a first parameter of the output of the secondary source, changing the parameter of the output of the secondary source, and acquiring a second image of the sample with a second parameter of the output.

[0008] Alternatively, a method for detecting defects can be provided, comprising examining a sample, generating a first defect distribution, and generating a second defect distribution. Examining the sample may include incident a charged particle beam onto a region of the sample and irradiating that region of the sample with a secondary source. Generating the first defect distribution may be accomplished using a first value of a parameter of the secondary source's output. Generating the second defect distribution may be accomplished using a second value of that parameter.

[0009] It should be understood that the foregoing general description and the following detailed description are exemplary and illustrative only, and are not intended to limit the disclosed embodiments as may be claimed. Attached Figure Description

[0010] The above and other aspects of this disclosure will become more apparent from the description of exemplary embodiments in conjunction with the accompanying drawings.

[0011] Figure 1A and 1B This is a schematic diagram illustrating an exemplary electron beam inspection (EBI) system according to an embodiment of the present disclosure.

[0012] Figure 2 This is a schematic diagram of an exemplary arrangement of a wafer including a loading / locking cavity according to embodiments of the present disclosure, which may be... Figure 1B This is part of an exemplary electron beam inspection system.

[0013] Figure 3 This is a diagram showing the effect of voltage contrast (VC) defect inspection on a wafer according to an embodiment of the present disclosure.

[0014] Figures 4A-4C The effect of applying secondary irradiation to a sample according to an embodiment of the present disclosure is shown.

[0015] Figure 5A Images of samples obtained by electron beam imaging using the EBI system 10, with or without secondary irradiation, are shown according to embodiments of the present disclosure.

[0016] Figure 5B The grayscale level variations of features imaged at different levels of parameter P according to embodiments of the present disclosure are shown.

[0017] Figure 6 Multiple images acquired at different levels of parameter P of the secondary source according to embodiments of the present disclosure are shown.

[0018] Figure 7 The behavior of features that vary with image ID according to embodiments of this disclosure is illustrated.

[0019] Figure 8A and 8B Images and curve data of samples acquired by electron beam imaging in the event of secondary irradiation evolution, according to embodiments of the present disclosure, are shown.

[0020] Figure 9A Images of samples that have been acquired by electron beam imaging in the event of secondary irradiation evolution, according to embodiments of the present disclosure, are shown.

[0021] Figure 9B The distribution of defects detected using different values ​​of the secondary irradiation parameter P according to embodiments of the present disclosure is shown.

[0022] Figure 10 This is a flowchart illustrating a method for evolving a source according to an embodiment of the present disclosure.

[0023] Figure 11 This is a flowchart illustrating a method for detecting defects according to an embodiment of the present disclosure. Detailed Implementation

[0024] Now, reference will be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings, and unless otherwise stated, the same numerals in the different drawings denote the same or similar elements. The embodiments set forth in the following description of the exemplary embodiments do not represent all embodiments according to the invention. Rather, they are merely examples of devices, systems, and methods based on aspects relating to the subject matter detailed in the appended claims.

[0025] Electronic devices consist of circuits formed on silicon wafers called substrates. Many circuits can be formed together on the same silicon wafer and are called integrated circuits or ICs. With advancements in technology, the size of these circuits has been greatly reduced, making it possible to mount more circuits on the substrate. For example, the IC chip in a smartphone can be as small as a thumbnail, yet it can include more than 2 billion transistors, each less than 1,000 times the size of a human hair.

[0026] Fabricating these tiny ICs is a complex, time-consuming, and expensive process, typically involving hundreds of individual steps. Even a mistake in one step can lead to a defect in the finished IC, rendering it useless. Therefore, one goal of the manufacturing process is to avoid such defects in order to maximize the number of functional ICs produced in the process, i.e., to improve the overall yield of the process.

[0027] A key component of improving yield is monitoring the chip fabrication process to ensure that a sufficient number of functional integrated circuits are being produced. One way to monitor this process is to inspect the chip's circuit structure at various stages of its formation. This can be done using a scanning electron microscope (SEM). SEM is used to image these extremely small structures, essentially "taking pictures" of them. The images can be used to determine if the structure is being formed correctly and in the correct location. If defects are found, the process can be adjusted to make them less likely to recur.

[0028] An image of a wafer can be formed by scanning the primary beam of a SEM system on the wafer and collecting particles (e.g., secondary electrons) generated from the wafer surface at a detector. This image can display various features on the wafer surface with different brightness levels. This image can be a grayscale image. Therefore, the brightness of these features can be referred to as "grayscale levels," with brighter features having higher grayscale levels. To detect voltage contrast defects, the grayscale level of a feature can be compared to the grayscale level of a reference feature. For example, the grayscale level of an insert (e.g., a line of material extending through a substrate) can be compared to the grayscale levels of surrounding inserts. Inserts can be designed to be identical, but if their grayscale levels differ, it may indicate that some inserts are defective or have other different characteristics. Determining whether a feature is defective can be based on the difference in grayscale levels between features, which can be compared to a threshold to determine whether the difference is attributable to noise or whether the feature is actually defective. Some defects may be difficult to detect because the difference in grayscale levels may be relatively small and may not exceed the detection threshold.

[0029] The process of detecting voltage contrast defects can involve applying a charge to a sample and observing the effect of the charge (e.g., its effect on particles generated from the wafer surface). In some cases, the charge can be deposited on the sample prior to imaging, in a process known as pre-charging. In this case, the charge can be generated by the same primary beam used for imaging (e.g., in a process known as pre-scanning) or by, for example, a separate diffuse electron gun. In other cases, the charge can be generated simultaneously with imaging. In this case, the charge can be generated by the primary beam or some other source. Because there can be a separate source used to generate the charge, separate from the primary source that generates the primary beam for SEM, this other source can be called a “secondary source” that emits “secondary irradiation.” The process of using voltage contrast in defect detection can include comparing two separate images. The first image can be acquired without adding charge. Then, the second image can be acquired using the added charge from the secondary irradiation generated by the secondary source. The secondary source can be, for example, a diode emitting a laser. In the first image, the primary beam of a charged particle beam device can be projected onto the wafer and secondary electrons can be collected at a detector. In the second image, the primary beam can be projected onto the wafer simultaneously with the laser beam being projected onto it. This can cause changes in the secondary electrons collected at the detector (e.g., the number or energy of the secondary electrons) and may result in the features displayed in the second image having different gray levels than those in the first image.

[0030] Comparing these two images allows for enhanced defect detection. For example, in the second image, with the laser on, the grayscale level of the defective plug may be higher than its surroundings in the first image when the laser is off. Therefore, the defective plug may be more likely to show differences in grayscale levels exceeding a threshold and thus become detectable. Illuminating the sample with a secondary source can enhance the sensitivity of voltage contrast defect detection.

[0031] However, applying charge with a secondary source may not always produce observable results. For example, for some features, the gray level of the defective plug in the second image (with the laser on) may not change significantly compared to the gray level of the defective plug in the first image (with the laser off). That is, a comparison between the first and second images may not reveal any additional information. Comparing the gray levels of features based solely on one laser-on state may be insufficient for inspecting some defects. Furthermore, even if structures appear to have similar geometries, there may be subtle differences in other characteristics of the feature, such as differences in the feature's composition (e.g., structure).

[0032] To enhance current detection and defect detection methods, it may be effective to change the parameters (power level) of the secondary source and compare multiple images when the secondary source irradiates the sample with different parameters (e.g., taking multiple images with different power levels, frequencies, etc. of secondary irradiation). An evolutionary defect inspection method could involve changing the parameters of the secondary source and acquiring multiple images. With multiple images, multiple data points can be obtained for the same feature, thus, behavioral trends relative to parameters (e.g., the power level of the secondary source) may become apparent. Additional information from the imaged features can be determined using grayscale variation trends. For example, instead of a single data point indicating only the grayscale variation between laser off and laser on states, a curve indicating the grayscale variation behavior across various laser on-state ranges can be obtained. This additional information can aid in defect detection. Alternatively, this information can help distinguish features that might appear identical under other conditions. For example, the resulting curve of the examined feature can be compared with curves of other features, and the overall shape of the curve can show that the feature has characteristics different from the others. This can constitute additional information related to previously unexplored features. This additional information can aid in defect identification. Furthermore, this additional information can have other uses, such as determining a wafer signature. A wafer signature can represent information about the system characteristics of the wafer, such as process parameters, and can be used in process tuning. Therefore, the additional information obtainable using the methods of this disclosure has a variety of potential uses.

[0033] The embodiments of this disclosure can address some problems associated with defect detection based solely on grayscale level comparisons of a single data point. For example, a curve comprising multiple data points can be obtained for the inspected feature, where the grayscale levels are determined within a range of parameter values ​​(e.g., different power levels of a laser used as a secondary source). This can enhance the sensitivity of the defect detection method and enable it to capture structural and compositional differences in the feature. Some embodiments can employ various methods to modify the parameters of the secondary source, such as varying the laser power and wavelength. Parameters can be modified in real time (e.g., during a single inspection). Highly flexible dynamic electron beam inspection can be provided.

[0034] The purposes and advantages of this disclosure can be achieved by the elements and combinations set forth in the embodiments discussed herein. However, embodiments of this disclosure are not necessarily required to achieve such exemplary purposes or advantages, and some embodiments may not achieve any of the stated purposes or advantages.

[0035] Without limiting the scope of this disclosure, some embodiments can be described in the context of providing detection systems and detection methods in systems utilizing electron beams (“e-beams”). However, this disclosure is not limited thereto. Other types of charged particle beams can be similarly applied. Furthermore, systems and methods for evolution sources or for detecting defects can be used in other imaging systems, such as optical imaging, photon detection, X-ray detection, ion detection, etc.

[0036] As used herein, unless otherwise expressly stated, the term "or" covers all possible combinations unless impractical. For example, if a component is stated to include A or B, then unless otherwise expressly stated or impractical, the component may include A, or B, or A and B. As a second example, if a component is stated to include A, B, or C, then unless otherwise expressly stated or impractical, the component may include A, or B, or C, or A and B, or A and C, or B and C, or A and B and C.

[0037] Now for reference Figure 1A This illustrates an exemplary electron beam inspection (EBI) system 10, which can be used for source evolution according to embodiments of the present disclosure. The EBI system 10 may include a scanning electron microscope (SEM) and can be used for imaging. The EBI system 10 may include dual-source outputs. Figure 1A As shown, the EBI system 10 includes a main cavity 11, a loading / locking cavity 20, an electron beam tool 100, and an equipment front-end module (EFEM) 30. The electron beam tool 100 is located within the main cavity 11. The EFEM 30 includes a first loading port 30a and a second loading port 30b. The EFEM 30 may include (multiple) additional loading ports. The first loading port 30a and the second loading port 30b receive a wafer front-opening transfer cassette (FOUP), which contains a wafer (e.g., a semiconductor wafer or a wafer made of (multiple) other materials) or a sample to be inspected (wafers and samples may be collectively referred to herein as “wafers”).

[0038] One or more robotic arms (not shown) in EFEM 30 can transport the wafer to loading / locking chamber 20. Loading / locking chamber 20 is connected to a loading / locking vacuum pump system (not shown), which removes gas molecules from loading / locking chamber 20 to achieve a first pressure below atmospheric pressure. After reaching the first pressure, one or more robotic arms (not shown) can transport the wafer from loading / locking chamber 20 to main chamber 11. Main chamber 11 is connected to a main chamber vacuum pump system (not shown), which removes gas molecules from main chamber 11 to achieve a second pressure below the first pressure. After reaching the second pressure, the wafer is inspected by electron beam tool 100. Electron beam tool 100 can be a single-beam system or a multi-beam system. Controller 109 is electrically connected to electron beam tool 100 and can also be electrically connected to other components. Controller 109 can be a computer configured to perform various controls of EBI system 10. Although controller 109 is in Figure 1A The controller 109 is shown as being located outside the structure comprising the main cavity 11, the loading / locking cavity 20, and the EFEM 30, but it should be understood that the controller 109 may be part of the structure.

[0039] Figure 1B Another example of EBI System 10 is shown. For example... Figure 1B As shown, the EBI system 10 may include a secondary source 200. The secondary source 200 may be configured to provide secondary irradiation to the sample. The secondary source 200 may be configured to emit light onto the sample. The secondary source 200 may be configured to project a beam of photons onto the sample. The secondary source 200 may include a laser.

[0040] Charged particle beam microscopy, such as that formed by or included in EBI system 10, can have resolutions as low as, for example, nanometer scales, and can be used as a practical tool for inspecting IC components on wafers. Using an electron beam system, electrons from a primary electron beam can be focused onto probe points on the wafer being inspected. The interaction between the primary electrons and the wafer can result in the formation of a secondary particle beam. The secondary particle beam can include backscattered electrons, secondary electrons, or Auger electrons generated by the interaction between the primary electrons and the wafer. The characteristics (e.g., intensity) of the secondary particle beam can vary based on the characteristics of the wafer's internal or external structure, thus indicating whether the wafer contains defects.

[0041] The intensity of the secondary particle beam can be determined using a detector. The secondary particle beam can form a beam point on the surface of the detector. The detector can generate an electrical signal (e.g., current, voltage, etc.) representing the intensity of the detected secondary particle beam. The electrical signal can be measured using a measurement circuit system (e.g., an analog-to-digital converter) to obtain the distribution of the detected electrons. The electron distribution data collected during the detection time window, combined with the corresponding scan path data of the primary electron beam incident on the wafer surface, can be used to reconstruct an image of the inspected wafer structure. The reconstructed image can be used to reveal various features of the internal or external structure of the wafer and can be used to reveal possible defects in the wafer.

[0042] However, current electron beam inspection techniques may suffer from low sensitivity when applied to certain types of defects (e.g., plug leaks, short circuits, or open circuits). Furthermore, electron beam inspection techniques may be insensitive to material composition (e.g., doping levels). This can limit sensitivity and throughput.

[0043] Secondary irradiation techniques, such as voltage contrast (VC), can amplify the imaging effects in electron beam inspection, making some additional defects detectable; however, additional limitations may arise. For example, some defects may not become detectable unless a certain amount of secondary irradiation is used. In some cases, features on a wafer may appear identical despite actual differences in structure or composition. For instance, the grayscale level changes between the laser-on and laser-off states of two different features may be identical unless a specific amount of laser power is used. However, the secondary irradiation amount (such as the laser power level) may only be set to one level and may be non-adjustable. Therefore, differences between two seemingly identical features may not be detected.

[0044] Furthermore, current electron beam inspection techniques may not support dynamic inspection. Examples of dynamic inspection include techniques that perform inspections while varying the landing energy of the charged particle beam. In some cases, electron beam systems may require a relatively long time to stabilize. For example, operating an electron beam system may require performing demagnetization to establish a stable magnetic field level between the wafer and system components. Electron beam systems may take time to reach stability, making dynamic inspection techniques difficult to apply.

[0045] Figure 2 An exemplary arrangement of a wafer 201 that may be included in a loading / locking cavity 20 is shown, along with the interaction of the wafer with the electron beam tool 100 and the secondary source 200. A structure for holding the wafer 201 within the EBI system 10 can be provided. The loading / locking cavity 20 may include a platform. Components of the loading / locking cavity 20 may constitute a wafer holder. For example, such as... Figure 2 As shown, the loading / locking cavity 20 may include a base plate 220 disposed on a stage 210, the stage 210 having a wafer holder for holding the wafer 201. The stage 210 may be movable.

[0046] Electron beam tool 100 can be configured to generate a primary beam 241 that can be projected onto wafer 201. Electron beam tool 100 may include a primary source configured to emit a beam of charged particles along an optical axis onto a region of a sample (such as wafer 201). The optical axis of the primary source may be configured perpendicular to the sample. Stage 210 can be adjusted to allow fine-tuning of the X, Y, Z position, tilt, angular orientation, etc., of wafer 201. Stage 210 can be moved to align a desired inspection segment on wafer 201 within the field of view (FOV) of electron beam tool 100. The FOV of electron beam tool 100 can be defined by the range of electron beam tool 100 configured to deflect the primary beam 241. In some embodiments, electron beam tool 100 may include a large FOV device configured to scan the primary beam 241 over a broad segment on wafer 201. In some embodiments, electron beam tool 100 may be configured to generate multiple beams and deflect multiple beams toward wafer 201. In other words, the electron beam tool 100 may include a multi-beam tool. In some embodiments, the electron beam tool 100 may be configured to project the primary beam 241 directly downwards, while the stage 210 is configured to move the wafer 201 through various scanning positions.

[0047] Secondary source 200 may be included in EBI system 10. Secondary source 200 may be configured to provide secondary irradiation to wafer 201. Secondary source 200 may be configured to generate secondary irradiation beam 242. As used herein, “secondary irradiation beam 242” refers to the beam generated by secondary source 200 and should not be confused with a secondary charged particle beam that may be generated by interaction with primary beam 241 and wafer 201 and may eventually travel to a detector. Secondary source 200 may be configured to project secondary irradiation beam 242 toward an inspection segment on wafer 201. Secondary source 200 and electron beam tool 100 may be configured to project their respective beams toward the same target. Electron beam tool 100 and secondary source 200 may be configured to emit their respective beams at an angle relative to a vertical line extending from the plane of wafer 201. In some embodiments, the beam spot formed on wafer 201 by secondary irradiation beam 242 may be larger than the beam spot formed by primary beam 241. In other words, the irradiation from the secondary source 200 can cover a wider segment on the wafer 201 than the beam spot formed by the primary beam 241. In some embodiments, both the primary beam 241 and the secondary irradiation beam 242 can be focused to a precise location on the wafer 201. The secondary source 200 can be configured to follow the scanning path of the electron beam tool 100. For example, the electron beam tool 100 and the secondary source 200 can be configured to scan on the sample. In some embodiments, the secondary source 200 can be stationary (and not scanning), while the electron beam tool 100 scans on the sample. Furthermore, although Figure 2 The secondary source 200 may be schematically shown as separate from the electron beam tool 100 (e.g., at a distance), but in some embodiments, the secondary source 200 may be mounted on the primary post of the electron beam tool 100 and configured to point towards the field of view (FOV) of the electron beam tool 100. The secondary source 200 may be included within the electron beam tool 100. For example, in some embodiments, depending on the configuration of the electron beam tool 100, the source of the electron beam tool 100 may also serve as the secondary source 200.

[0048] In some embodiments, the electron beam tool 100 may be configured to operate in a first mode emitting primary irradiation and a second mode emitting secondary irradiation. Examples of charged particle beam tools configured to operate in the first and second modes are discussed in U.S. Patent Application No. 16 / 053,636 (published as U.S. Publication No. 2019-0043691A1), the entire contents of which are incorporated herein by reference.

[0049] The electron beam tool 100 and the secondary source 200 may be included in a dual-output source. The dual-output source may be configured to emit electrons and photons. For example, the dual-output source may be configured to emit a primary beam 241 and a secondary irradiation beam 242.

[0050] Secondary source 200 may include a diode. The diode may be a light-emitting diode (LED). Secondary source 200 may include a laser diode. Secondary source 200 may be configured to generate irradiation that can produce observable effects in the material of wafer 201. For example, wafer 201 may include a semiconductor structure. Features on wafer 201 may be sensitive to specific types of irradiation. Such features may respond to incident photons. Features of wafer 201 may be configured to generate charge due to the photoelectric effect. For example, wafer 201 may include a transistor, which may include a PN junction, as described below.

[0051] One application of semiconductor manufacturing can include computer memory. Static random access memory (SRAM) devices can be formed on a substrate (such as wafer 201). SRAM devices can be formed using manufacturing processes including tungsten chemical mechanical polishing (WCMP). Features formed by WCMP are among the most frequently encountered targets in electron beam inspection. In electron beam inspection of WCMP features, voltage contrast defect detection is popular because it can detect electrical defects such as open contacts and short circuits. An “open circuit” or “short circuit” defect can refer to a feature buried beneath a conductive plug, or it can be associated with high aspect ratio structures such as vias or contacts. Open circuit and short circuit defects can be difficult to detect by other methods, such as optical inspection.

[0052] Figure 3 This is a diagram showing the effect of voltage contrast (VC) defect inspection on a wafer according to an embodiment of the present disclosure. Figure 3 The lower side shows a cross-sectional view of a semiconductor structure that can be formed on wafer 201. This semiconductor structure may include a substrate 300. Substrate 300 may include structures for forming semiconductor logic, such as metal-oxide-semiconductor field-effect transistors (MOSFETs). For example, Figure 3 The substrate 300 shown may include a P-channel metal-oxide-semiconductor (PMOS) and an N-channel metal-oxide-semiconductor (NMOS) MOSFET. NMOS and PMOS refer to the types of MOSFETs herein.

[0053] Substrate 300 may be an intrinsic semiconductor. Substrate 300 may have a relatively low doping degree. Substrate 300 may include a silicon wafer. Substrate 300 may have an intrinsic region 320. Intrinsic region 320 may be formed of a lightly doped p-type semiconductor (e.g., "P-"). Substrate 300 may include an oxide layer 310, which may be the exposed top surface of wafer 201. Oxide layer 310 may be formed of silicon oxide (SiO2).

[0054] Substrate 300 may include first to sixth plugs 331, 332, 333, 334, 335, and 336. The plugs may be configured to connect to a PN junction. Substrate 300 may include a first well 321 and a second well 326. The first well 321 and the second well 326 may be spaced apart and separated by a partition 325. Partition 325 may be formed of a material different from the intrinsic region 320, the first well 321, and the second well 326. The first well 321 may be formed of a p-type semiconductor with a higher doping concentration than the intrinsic region 320. For example, the first well 321 may include a P+ region. The second well 326 may be formed of an n-type semiconductor (e.g., "N+").

[0055] Within each of the first well 321 and the second well 326, additional doped regions may be formed. For example, the first well 321 may include a first n-type semiconductor region 322 and a second n-type semiconductor region 324. The first and second regions 322 and 324 may include N+ regions. The second well 326 may include a first p-type semiconductor region 327 and a second p-type semiconductor region 329. The first and second regions 327 and 329 may include P+ regions. Therefore, a PN junction can be formed between regions with different semiconductor conductivity.

[0056] Voltage contrast (VC) defect inspection can be performed on substrate 300. The results of the VC defect inspection can be displayed on panel 340, such as... Figure 3 As shown on the upper side. Panel 340 can be an image acquired via electron beam inspection. The image acquisition unit included in the EBI system 10 can be configured to acquire images via electron beam inspection and perform image processing. The image acquisition unit can be included in the controller 109. VC defect inspection can involve observation based on the effect of applying charge to the sample surface. Panel 340 can be generated by performing electron beam inspection on substrate 300. Panel 340 can be formed based on secondary electrons 301 generated from substrate 300 due to primary electron beam impact. Depending on the geometry or composition of the structure constituting substrate 300, the effect of applying charge can be additional current flowing through substrate 300. The charge flowing through substrate 300 can affect the brightness (e.g., gray level) of the imaged features. Defects can be identified based on the gray level of features that differ from the surrounding environment.

[0057] For example, a structure well-connected to other features may appear different in an image than an isolated structure. Charge can flow through interconnected features, and charge can accumulate on the surfaces of features where there are no current leakage paths. This can affect the resulting image of the features. Figure 3As shown, plug 331 is well connected to the underlying structure, and its corresponding spot 341 appears relatively bright. However, plug 332 is not directly connected to the first well 321, but rather abuts against region 323. Region 323 insulates plug 332 from other features. Therefore, spot 342 corresponding to plug 332 may appear darker than spot 341. Accordingly, its grayscale level is lower. Furthermore, plug 333 is disconnected from its underlying structure. Plug 333 is not connected to the second region 324 and the first well 321, but is surrounded by oxide layer 310. Therefore, spot 343 corresponding to plug 333 appears darker than spots 341 and 342.

[0058] Furthermore, the photoelectric effect of semiconductor materials can affect VC defect inspection. For example, in VC defect detection, the orientation of the PN junction may affect the resulting grayscale level of features in the image. Figure 3 As shown, plug 334 is connected to a first region 327 that can be formed as a P+ region. Current can travel through the PN junction formed by the first region 327 and the second well 326. An energy barrier may exist in the space charge region (e.g., depletion region) of the PN junction. Electrons can travel from the substrate 300 and flow through the PN junction and reach the top of plug 334 due to charging. Electrons can be emitted from plug 334 and reach the detector, thereby generating a signal and thus causing an increase in the gray level of the imaging features (e.g., spot 344).

[0059] On the other hand, for PN junctions formed in the opposite direction, the built-in potential may be reversed, and different effects may occur. For example, as Figure 3 As shown, plug 331 is connected to a first region 322 that can be formed as an N+ region. Current can enter the PN junction formed by the first region 322 and the first well 321. Electrons can travel from the substrate 300 into the PN junction, but may not be able to jump over the energy barrier in the space charge region of the PN junction. Therefore, plug 331 does not generate additional secondary electrons, and spot 341 may appear darker than spot 344.

[0060] Now for reference Figures 4A to 4CThis illustrates the effect of applying secondary irradiation to a sample according to embodiments of the present disclosure. The sample may be subjected to secondary irradiation from a secondary source (e.g., from a laser). Secondary irradiation may cause structures on the sample to behave in a certain way. For example, incident photons on the sample may cause the generation of electrons in the sample. Furthermore, the sample may be subjected to secondary irradiation alone or together with primary irradiation from a primary charged particle beam (e.g., from electron beam tool 100). Structures on the sample may be sensitive to the photoelectric effect. These structures may respond to the application of primary and secondary irradiation, such that defect detection can be enhanced when secondary irradiation is added. For example, these structures may respond to primary irradiation in a certain way, but may respond differently when primary and secondary irradiation are applied simultaneously.

[0061] For example, such as Figure 4A As shown, when a semiconductor structure, such as a diode, is irradiated, it can exhibit characteristic behavior. Curve 411 illustrates the diode's IV behavior according to the reverse bias mode. Applying secondary irradiation to a sample may result in the generation of charge in the semiconductor structure included in the sample. Furthermore, the characteristic behavior can be altered depending on the parameters of the secondary irradiation. For example, as the irradiation power of the secondary irradiation increases (which may result in different charges in the sample), the curve representing the diode's IV behavior may shift downward, for example, due to the effects of charge changes or some other effect related to secondary irradiation, as indicated by arrow 440. Curve 412 is an example of the diode's behavior when a secondary irradiation with a power greater than that in curve 411 is applied. The power used in curve 413 is greater than that in curve 412, and so on. Curves 411 to 413 can represent the photocurrent in the diode. For example, region 419 can represent the dark current region.

[0062] Figure 4B An image of a sample with multiple plugs is shown. This image was acquired using electron beam imaging with an EBI system 10. Figure 4B In this process, by scanning the primary beam of the electron beam tool 100 through the region of the sample, an image can be acquired, thereby forming an image of a portion of the sample within the field of view (FOV) of the electron beam tool 100. Figure 4B The image can be formed without the application of secondary irradiation.

[0063] exist Figure 4BIn this example, plug 421 may be included in row 420. Plug 421 may have a grayscale level determined to be a specific value. Other plugs in row 420, besides plug 421, may have substantially the same grayscale level. Plug 421 may be defective, and its grayscale level may differ from the grayscale levels of other plugs in its row. Plug 421 may have a grayscale level significantly lower than the grayscale levels of other plugs in row 420. For example, plug 421 may have a grayscale level determined to be 46% of the grayscale levels of its surrounding plugs. In other words, it can be determined that the grayscale level of plug 421 has decreased by 54%.

[0064] Gray levels can be measured in any unit. The gray level of the plug in row 420 can be determined as the average value. The gray level of the plug in row 420 can be determined, for example, 100 AU (any unit). The gray level of plug 421 can be determined as 46 AU. Based on this, it can be determined that the gray level of plug 421 differs from the gray level of the other plugs in row 420 by 54%.

[0065] like Figure 4B As shown, another row 430 may also appear in the acquired image. Row 430 includes plug 431. Plug 431 may be defective, and its gray level may be slightly lower than that of the other plugs in its row. For example, plug 431 may have a gray level that is determined to be 70% of the gray level of its adjacent plugs. In other words, the gray level of plug 431 may be determined to have decreased by 30%.

[0066] Determining a defect in a feature may involve comparing the determined feature parameters with parameters indicating other features. A controller, such as controller 109 which may be included in EBI system 10, may be configured to determine the defect. Controller 109 may include an image processor. The image processor may be configured to extract features from an acquired image. The image processor may segment the image into different regions containing different sets of features. For example, controller 109 may be configured to process... Figure 4B The image is determined to include multiple rows (including row 420), the average gray level of row 420 is determined, and the gray level of each feature in row 420 is determined. A set of features, such as two consecutive plugs, may be determined. Controller 109 can be configured to determine that a feature (such as plug 421) is defective when its gray level differs from the average of row 420 by a predetermined amount, and then issue an alarm. The predetermined amount may be a defect detection threshold. In some embodiments, instead of the average of the entire row 420, the gray level of plug 421 may be compared with the gray level of the directly adjacent plug.

[0067] In some embodiments, it may be necessary to increase the defect detection threshold to reduce the impact of noise. For example, if the defect detection threshold is too low, it may lead to a large number of false alarms (e.g., identifying features that are not actually defective as defective). Therefore, a relatively high defect detection threshold, such as 50%, may be set.

[0068] The controller 109 can be configured to determine a feature is defective only if the detected grayscale level difference is 50% or more. In this case, plug 431 will not be identified as defective. Only plug 421 will be correctly identified as defective. Therefore, there may be false detections regarding plug 431 or other features.

[0069] Figure 4C Images of samples obtained using electron beam imaging with the EBI system 10 in the presence of secondary irradiation are shown. Figure 4C In this case, an image of the sample can be acquired by scanning a region of the sample with the primary beam of the electron beam tool 100 while simultaneously applying secondary irradiation to the same region. Features on the sample may be affected by the secondary irradiation, and can be, for example, based on... Figure 4A The photoelectric effect exhibited generates photocurrent. The generation of photocurrent in the irradiated feature can lead to amplification of the imaging effect during electron beam inspection. For example, for a correctly formed transistor, the grayscale level of the imaged feature can be increased. For a defective transistor, the grayscale level of the imaged feature can be lower compared to those correctly formed features. The degree of photoelectric effect can vary depending on the structure type. Applying secondary irradiation can significantly improve the inspection sensitivity for N-type open-circuit (N-open) defects.

[0070] like Figure 4C As shown, secondary irradiation can be applied to the sample, and it can form a structure in which the plug typically has a higher density than... Figure 4B (Images with higher gray levels in which no secondary irradiation is applied) Most plugs can be correctly formed, therefore most plugs can appear brighter. However, plug 421 may be defective and may have a lower gray level than the other plugs in row 420. Figure 4C In this study, it can be determined that the gray level of plug 421 is 50% of the gray level of its surrounding plugs. In other words, it can be determined that the gray level of plug 421 has decreased by 50%. Furthermore, plug 431 may be defective, and its gray level can be determined to be 50% of the gray level of its adjacent plugs. In other words, the gray level of plug 431 can be determined to have decreased by 50%.

[0071] Controller 109 can be configured to determine that a feature is defective using the same algorithm discussed above (i.e., the detected grayscale level difference of the feature is 50% or more). Figure 4C In this case, it can be determined that both plug 421 and plug 431 are defective. Therefore, false detections of plug 431 can be avoided.

[0072] Figures 4A to 4C This can demonstrate the effect of the photoelectric effect on electron beam imaging. By applying secondary irradiation, the photoelectric effect can be used to detect more defects and enhance the sensitivity of defect detection. Figures 4A-4C The mechanism can also be called active charge control (ACC). While this technique can allow the identification of some additional defects, it is not always possible to apply secondary irradiation to enhance defect detection sensitivity. For example, different structures can respond differently to secondary irradiation, and this can complicate the inspection process. Furthermore, the extent of the photoelectric effect can depend on the parameters of the secondary irradiation, such as the source power, and the photoelectric effect may not produce observable changes unless at certain levels of these parameters.

[0073] Figure 5A Images of samples obtained by electron beam imaging using the EBI system 10, with or without secondary irradiation, according to embodiments of the present disclosure are shown. The samples may include an SRAM-WCMP structure. The images can be acquired by SEM imaging. The first image 510 may correspond to imaging conditions without secondary irradiation. For example, the parameter P of the secondary irradiation may be P0, which may be zero. The first image 510 may show a first plug 511 and a second plug 512. The gray levels of the first plug 511 and the second plug 512 in the first image 510 may be substantially equal; therefore, the first plug 511 and the second plug 512 may not be distinguishable based on gray level alone.

[0074] like Figure 5A As shown, the second image 520 can display the same sample area as shown in the first image 510, but under different imaging conditions. The second image 520 can correspond to the imaging conditions under which the first secondary irradiation was applied. For example, the parameter P of the secondary irradiation can be P1 in image 520, and P1 can be greater than P0. Although secondary irradiation has been applied, the gray levels of the first plug 511 and the second plug 512 in the second image 520 can be substantially equal; therefore, it may still be impossible to distinguish the first plug 511 and the second plug 512 based on gray levels. Secondary irradiation can be applied to the areas where the first plug 511 and the second plug 512 respond to secondary irradiation in substantially the same way.

[0075] Defect inspection based on grayscale level changes between states with and without secondary irradiation may face problems such as the following: Even though the grayscale level of a feature may change when secondary irradiation is applied, the inspected feature may respond to secondary irradiation in the same way as the state without secondary irradiation. Therefore, although the imaging effect may be amplified (e.g., by a global increase in the grayscale level of the feature), applying secondary irradiation may not actually enhance detection sensitivity. In some cases, features that actually have different characteristics (e.g., whether they are defective) may appear identical even when secondary irradiation is applied.

[0076] However, as the parameters of the secondary irradiation continue to change, the characteristic differences in the imaging can become apparent. For example... Figure 5A As shown, the third image 530 can display the same sample area as shown in the first image 510 and the second image 520, but under different imaging conditions. The third image 530 can correspond to the imaging conditions under which a second-level secondary irradiation is applied. For example, the parameter P of the secondary irradiation can be P2 in image 530, and P2 can be greater than P1. The secondary irradiation can be applied at different levels at which different responses begin to manifest themselves in the features of the imaging. For example, the gray levels of the first plug 511 and the second plug 512 in the third image 530 can be different. The difference in gray levels between the first plug 511 and the second plug 512 can exceed a threshold, thus, a difference can be determined between the first plug 511 and the second plug 512. A secondary source with a parameter P that changes during the acquisition of multiple images can be said to have evolved. The parameter P can be the power of the secondary source.

[0077] Figure 5B The grayscale level variations of features imaged at different levels of parameter P according to embodiments of the present disclosure are shown. Figure 5B The evolution of the secondary source over time can be illustrated. Multiple images can be acquired under different imaging conditions. The parameter P can vary over time, and images can be acquired at different values ​​of parameter P. Image processing can be performed on each image. Features can be extracted and analyzed. Parameters of features, such as their gray levels, can be determined from the images. The gray levels can change with variations in the parameter P of the secondary source. Tracking these gray level changes as parameter P varies can be useful. Thus, curves representing the response of features to secondary irradiation can be generated.

[0078] Figure 5BGraphs of different features are shown. For example, features F1-F4 (first to fourth) are shown. These features can respond to secondary irradiation in different ways. This may be due to differences in the materials and structures constituting the features. When secondary irradiation is applied, features F1-F4 can exhibit characteristic behaviors indicative of their properties, and this can be shown in the graph data of each feature. In some embodiments, for example, feature F1 can be identified as corresponding to the PMOS drain, feature F2 as corresponding to the NMOS drain, feature F3 as corresponding to the gate, and feature F4 as corresponding to the background. The PMOS can have terminals including a source and a drain, and can be formed as a P+ doped region with an N-doped well. The NMOS can have terminals including a source and a drain, and can be formed as an N+ doped region within a P-doped well.

[0079] Curves can help obtain additional information about the features being examined. For example, based on curve data, it's possible to determine that a feature belongs to a specific type of structure. Figure 5B In the initial region (e.g., between P0 and P2), the second feature F2 exhibits a rapid increase in grayscale level, while in other regions (e.g., between P2 and P4), it tends to plateau. This type of behavior can be characteristic of terminals (e.g., NMOS drains) in an NMOS. Based on the curve data, it can be determined that the first plug 511 is an NMOS drain and the second plug 512 is of a different structure.

[0080] Curve data can take various forms. For example, a feature may respond uniformly to increasing laser power, resulting in a substantially flat curve. In some embodiments, a feature may respond positively to increasing laser power, meaning the grayscale level change may also increase with increasing laser power. An example of such a feature could be an NMOS drain. In some embodiments, a feature may respond negatively to increasing laser power, meaning the grayscale level change may decrease with increasing laser power. An example of such a feature could be a PMOS drain. All these trends can be useful information that can be used to determine the characteristics of a feature. Furthermore, determining whether a feature is defective can be based on what type of feature it is.

[0081] Figure 6Multiple images acquired at different levels of parameter P of a secondary source according to embodiments of the present disclosure are shown. Electron beam inspection can be used to acquire the images. Secondary irradiation can be applied by a secondary source. The parameter P of the secondary source can be varied, and images can be acquired at different levels of parameter P. Images can be acquired sequentially. For example, a first image 610 can be acquired when the value of parameter P is P0. P0 can be equal to 0, therefore, the first image 610 can be acquired without secondary irradiation. Then, a second image 620 can be acquired when the value of parameter P is P1. P1 can be greater than P0. A third image 630 can be acquired when the value of parameter P is P2, and so on. Images 610, 620, 630, 640, 650, 660, 670, and 680 can be patch images. A patch image can be a larger image. A patch image can include a cropped portion of the larger image, such as a portion including a defect of interest. The image can be divided into several patch images. In some embodiments, patch images can be generated from partial pixel data. For example, patch image processing can be used, where patch images are generated before a full scan of the region of interest of the sample is completed. Images can be acquired in order of increasing image index as the value of parameter P increases.

[0082] In some embodiments, images can be acquired in reverse order. For example, images can be acquired using image indices that increase as the value of parameter P decreases. Furthermore, in some embodiments, images may not be acquired in a specific order corresponding to changes in parameter P. For example, P2 may be less than P1, and P3 may be greater than P2. Secondary irradiation can be configured to have low time dependence or hysteresis effects. Therefore, the secondary irradiation generated by the secondary source can be arbitrarily varied.

[0083] In some embodiments, a range for the parameter P can be predetermined. A method for evolving secondary sources may include scanning multiple points where the value of parameter P varies within this range. This range can be configured to include points where the largest difference between similar features can be determined to be significant. For example, the range can be configured to include point Py, which will be referenced below. Figure 8B This will be discussed further. In some embodiments, the range can be configured to include point Py and predetermined buffers on both sides of Py. In some embodiments, point Py can be determined based on prediction. For example, it can be determined that the sample includes SRAM-WCMP devices, and therefore, point Py can be effectively included within a range where the grayscale level variation between the expected NMOS terminal plugs can be maximized.

[0084] Figure 7 The behavior of features that vary with image ID according to embodiments of this disclosure is illustrated. Figure 7The graph shows the image ID on the x-axis and the gray level variation (GLV) on the y-axis. As discussed above, advancing the image ID can correspond to increasing the parameter P of the secondary source. Figure 7 The figure shows the first to third curves, 710, 720, and 730. Curve data representing a specific feature can be prepared in advance. For example, data points representing gray levels of a known structure can be extracted from previously acquired images.

[0085] Curve 720 can correspond to the normal behavior of a known structure. Curve 720 can correspond to an NMOS drain. For example, curve 720 can be prepared by analyzing a predetermined number of NMOS drains and averaging them. Therefore, a normal NMOS drain can be expected to exhibit behavior similar to curve 720 in a newly imaged sample. Deviation from curve 720 can indicate that the imaged features are of a different structure or are defective.

[0086] Furthermore, certain types of defects may exhibit characteristic behavior when the image ID is changed (e.g., the parameter P of the secondary source). Figure 7 As shown, curves 710 and 730 are also provided. Curves 710 and 730 can also correspond to NMOS drains, but may have other differences from normal NMOS drains. For example, curves 710 and 730 can correspond to defects. Curve 710 can correspond to a leakage defect. Curve 730 can correspond to a high-R defect. For example, curve data for various types of defects can be prepared in advance, and these curve data can be determined based on averaging a predetermined number of known samples. Curves associated with defect types can also be associated with structure types.

[0087] Figure 8A and 8B Images and curve data of samples acquired by electron beam imaging in the event of secondary irradiation evolution, according to embodiments of the present disclosure, are shown. Figure 8A This can represent SEM images of samples that may include SRAM-WCMP structures. The same three plugs, such as Nplug1, Nplug2, and Nplug3, are used in... Figure 8A Highlighted in each of the images.

[0088] Figure 8BThis includes graphs showing the dependence of gray levels on the parameter P of secondary irradiation for multiple features. The gray levels can vary depending on the parameter P, which in turn depends on the characteristics of the feature. For example, curve 810 could correspond to a leakage defect. Curve 850 could correspond to a high-R defect. Curves 820, 830, and 840 could correspond to plugs without defects (e.g., plugs Nplug1, Nplug2, and Nplug3), but are still different from each other. Nplug1, Nplug2, and Nplug3 can respond differently to changes in parameter P of secondary irradiation. For example, due to their different compositional composition, Nplug1, Nplug2, and Nplug3 can respond differently to laser power evolution.

[0089] like Figure 8B As shown, some features may exhibit the same response at some points and different responses at others. For example, curves 820, 830, and 840 may converge at points P = Px and P = Pz. In comparison methods, ACC can be applied to points where differences between features might be missed. For example, a sample can be imaged at point P = Px, and it can be determined that plugs Nplug1, Nplug2, and Nplug3 are identical. Imagery can also be performed at point P = 0 for comparison. The grayscale level variations in the imaged features can be analyzed based on images acquired at P = 0 and P = Px. However, even when imaging the sample while applying secondary irradiation (e.g., via ACC), no additional information is generated compared to the information collected from the image acquired at P = 0. Therefore, false detections of defects may occur, or other differences between features may be overlooked.

[0090] Compared to comparative methods, the evolution of secondary irradiation can avoid such false detections and enable detection methods to capture data indicating differences that appear to be identical features. For example, Figure 8B As shown, the grayscale difference between curves 820, 830, and 840 reaches its maximum at the point P = Py. The grayscale difference with known defects, such as curve 850 representing a high-R defect, is also evident at this point. Images acquired at P = Py can facilitate visualization of differences between features and allow for easy analysis of different features. The evolution of secondary irradiation can enable the determination of optimal points for acquiring and analyzing SEM images. Furthermore, additional information derived from the curve data may be useful for other applications. Providing such additional information can enhance the sensitivity of detection.

[0091] In some embodiments, the secondary irradiation can evolve within a range of parameter P. This range can be determined based on points that maximize the differences between the curves. For example, Figure 8BThe range R1 centered at point P = Py is displayed. This range can be extended by a predetermined amount from the center point. It can be determined that a check is performed simultaneously with the evolution of secondary irradiation within range R1.

[0092] The deviation of a curve from a standard curve can indicate differences in the structure, composition, or other characteristics of an imaged feature. Analysis can be performed to determine the similarity between the imaged feature and a standard curve stored in a database. Standard curves can be provided for features commonly encountered for specific types of inspections. For example, a SRAM-WCMP standard feature database can be loaded before inspecting a sample. Furthermore, curve data can be analyzed to determine the extent to which an imaged feature deviates from a standard curve. For example, the root mean square (RMS) error from the standard curve can be determined. Measurements of other deviations (such as offset, skewness, etc.) can be determined. Analysis based on standard curves can indicate that while an imaged feature may be similar to a standard curve, there may be structural or compositional irregularities requiring further investigation. Analysis can also indicate defects in the imaged feature. Analysis can also be used to classify features into specific types of structures, such as terminals, gates, bodies, etc., of PMOS or NMOS. Analysis can be used to classify defects into specific types of defects, such as short circuits, open circuits, leakage, high R, etc.

[0093] Evolutionary analysis based on secondary irradiation can be used for mapping. For example, resistance mapping and composition mapping can be performed. Mapping can help identify systemic characteristics of a sample, rather than local characteristics. Mapping can be used to determine wafer signatures. Wafer signatures can indicate the presence of system-level irregularities, such as irregularities in the manufacturing process. Wafer signature-based analysis can be used for batch processing adjustments. Mapping methods can be performed before or after the probing process.

[0094] In some embodiments, the parameter P of the secondary irradiation can be the power of the secondary source. For example, parameter P can correspond to the power of the operating laser diode. In some embodiments, parameter P can correspond to the wavelength of the secondary source. For example, parameter P can correspond to the wavelength of the light emitted by the laser diode.

[0095] The secondary source can be configured to generate secondary irradiation at multiple wavelengths. For example, the secondary source 200 may include a laser diode module. The laser diode module may include multiple laser diodes, each configured to emit laser light at a different wavelength. The EBI system 10 is configured to acquire multiple images of the sample, and the secondary source 200 can change the wavelength of its laser output. A laser diode may be configured to emit at one wavelength at a time during electron beam imaging.

[0096] Figure 9A Images of samples that can be acquired by electron beam imaging in the event of secondary irradiation evolution according to embodiments of the present disclosure are shown. Figure 9ASEM images of samples that may include 3DNAND (floating gate) structures can be represented. The evolution of secondary irradiation can be determined by altering, for example, the wavelength of secondary irradiation applied by a secondary source. Voltage contrast imaging via SEM can reveal the presence of defects, such as... Figure 9A The defects highlighted by indicator 901. For example... Figure 9A As shown in the image, P = P0 can represent the case where no secondary irradiation is applied. P1 can be a first wavelength. P2 can be a second wavelength, and P2 can be less than P1. That is, a higher frequency of secondary irradiation can be applied in the case of P = P2 compared to the case of P = P1. P3 can be a third wavelength, and P3 can be less than P1 or P2. That is, a higher frequency of secondary irradiation can be applied in the case of P = P3 compared to the cases of P = P1 or P = P2. The power of the secondary irradiation can remain constant. However, in some embodiments, the power of the secondary irradiation can also be varied along with other parameters of the secondary irradiation.

[0097] Different types of defects can respond to changes in laser wavelength in different ways. Some defects can be more easily detected when a specific value of the secondary irradiation parameter P is used. The evolution of secondary irradiation at various laser wavelengths can reveal which wavelength is best suited for detecting specific types of defects. Furthermore, the evolution of secondary irradiation can enhance the defect detection sensitivity for weak defects.

[0098] Figure 9B The distribution of defects detected using different values ​​of the secondary irradiation parameter P according to embodiments of the present disclosure is shown. Curve 910 shows the defect distribution at P = P1. Curve 920 shows the defect distribution at P = P3. Curve 930 shows the defect distribution at P = P2. Defect detection analysis can be performed based on electron beam inspection while secondary irradiation is applied. The results of the defect detection analysis can reveal the optimal value of parameter P that maximizes defect detection efficiency. For example, curve 920 can be determined to represent the optimal conditions for defect detection on the sample being inspected.

[0099] Defect detection results can be compared and further analysis can be performed. For example... Figure 9BAs shown, a first table 921 and a second table 922 can be generated based on the results of defect detection analysis. A first defect detection analysis can be performed on the defect distribution corresponding to P = P1, and a second defect detection analysis can be performed on the defect distribution corresponding to P = P3. The first table 921 can be generated based on a comparison of the first and second defect detection analyses. This comparison can include repeatability analysis. Repeatability analysis can involve comparing defects detected in the defect distribution of P = P1 with defects detected in the defect distribution of P = P3. For example, the percentage of defect overlap, such as AA%, can be determined. This can also be expressed as a raw count; for example, BB defects may overlap between the defect distributions of P = P1 and P = P3. Furthermore, compared to the distribution of P = P1, CC additional defects can be counted in the distribution of P = P3.

[0100] Similarly, a second table 922 can be generated based on a comparison between the third and second defect detection analyses. This comparison can include the repeatability analysis described above. The repeatability analysis may involve comparing defects detected in a defect distribution of P = P2 with defects detected in a defect distribution of P = P3. For example, a defect overlap of EE% can be determined. This can also be expressed as a raw count, for example, FF defects may overlap between the defect distributions of P = P2 and P = P3. Furthermore, compared to the distribution of P = P2, GG additional defects can be counted in the distribution of P = P3.

[0101] The defect distribution at a certain parameter P value can enhance the robustness of defect detection. For example, such as Figure 9B As shown, curve 920 displays a defect distribution with a wider range and greater diversity than curves 910 or 930. Furthermore, curve 920 includes defect subgroups in regions spaced apart from the median of the distribution. Additional defect counts, such as those discussed above with reference to tables 921 and 922, can be attributed to defects captured in these regions.

[0102] In some embodiments, a method for evolving a source may be provided. The source may include a primary source configured to generate primary irradiation of a primary beam comprising a charged particle beam device, and the source may include a secondary source configured to generate secondary irradiation. Secondary irradiation may include the power or wavelength of a laser diode.

[0103] Figure 10 An evolution source method according to an embodiment of this disclosure is illustrated. Figure 10 The method can be executed by a controller (such as controller 109). Controller 109 can issue instructions to components of, for example, a charged particle beam system or other systems to perform certain functions. Controller 109 may include circuitry configured to perform various functions. (Initial) Figure 10 In the routine, the method may proceed to step S101. Step S101 may include forming a beam. The beam may include a primary charged particle beam that can be generated by a charged particle beam device. Step S101 may include generating a primary beam 241 by an electron beam tool 100.

[0104] The primary beam 241 can scan a region passing through a sample (such as wafer 201). Wafer 201 may have a region of interest that can be the object of inspection. Step S101 may include scanning a first location on the sample. This first location may correspond to the location of a feature. This first location may correspond to a first pixel in an image generated by the charged particle beam device. Step S101 may also include scanning a second location on the sample, and so on. This second location may correspond to a second pixel.

[0105] Next, the method may proceed to step S102. Step S102 may include acquiring an image. The image may include multiple pixels. The image acquired in step S102 may include a base image. The base image may be an image in which a sample image is acquired using only a primary source (e.g., the primary beam 241 of electron beam tool 100). The base image may be a base image in which a sample image is acquired without applying secondary irradiation to the sample. Step S102 may include acquiring the image by electron beam inspection. Step S102 may include voltage contrast imaging. Step S102 may include image processing. Image processing may include extracting features, determining regions in the image, and determining parameters of the features of the imaging. For example, controller 109 may be configured to process the image acquired in step S102 to determine that the image includes multiple rows of plugs, determine the average gray level of the row, and determine the gray level of each feature in the row. The gray level of a feature may include the gray level of one or more pixels.

[0106] The method can then proceed to steps S103 and S104. Step S103 may include generating a primary irradiation, and step S104 may include generating a secondary irradiation. Steps S103 and S104 may be performed simultaneously, separately, or overlapped (e.g., step S104 may be performed before and during step S103). Generating the primary irradiation may include, for example, by means of... Figure 2 The electron beam tool 100 shown generates a primary beam 241. Generating secondary irradiation may include, for example, by means of... Figure 2 The secondary source 200 shown generates a secondary irradiation beam 242. This secondary irradiation can be applied to an imaging region on the sample, and the imaging region can be the same as the region to which the primary beam 241 is projected. Due to effects such as the photoelectric effect, secondary irradiation on the sample may cause features in the imaging region to emit secondary particles in a different manner than when no secondary irradiation is applied. These secondary particles can be detected by a detector.

[0107] In step S105, an image of the Nth evolution (where N is an integer) can be acquired. For example, an image of the first evolution, the second evolution, or the third evolution can be acquired, and so on. This image can be based on a signal generated in the detector in response to the reception of secondary particles. Because secondary irradiation is applied, the image of the Nth evolution may differ from the image acquired in step S102. N can be a variable representing an index. Figure 10 The method can begin with N starting from 1 and can increment each time step S105 is executed. N can correspond to the number of evolved images acquired so far. Step S105 may include acquiring patch images.

[0108] Next, the method can proceed to step S106. Step S106 may include determining whether N is greater than a threshold T. The threshold T may be a set parameter, such as the number of images to be acquired. Step S106 may include reading the value of N stored in memory. The threshold T may be preset. The threshold T may be determined based on the number of data points determined to be sufficient to form a curve. In some embodiments, T may be 2. That is, the evolutionary method may include acquiring a base image and then acquiring at least two images with secondary irradiation applied. In some embodiments, N may be determined based on the amount of time available for performing imaging. Figure 6 In the example, the threshold T can be set to 6, and the imaging method can continue until image 680 is acquired, at which point N can reach a value of 7. In some embodiments, imaging can continue after acquiring the 8th patch image, such as... Figure 6 As shown.

[0109] In step S106, if it is determined that N is less than or equal to T, the method can proceed to step S107. Step S107 may include adjusting the secondary irradiation. Step S107 may include changing the parameter P of the secondary irradiation. Step S107 may include changing the power of the secondary source 200. Step S107 may include changing the wavelength of the secondary irradiation generated by the secondary source 200.

[0110] From step S107, the method can return to steps S103 and S104. In step S104, secondary irradiation can be applied to the same sample with a different parameter P value than previously performed. Therefore, the secondary source may have evolved. Then, in step S105, the Nth evolved image can be acquired. The acquired image may differ from the previously acquired image. Step S105 may include increasing the value of N.

[0111] Returning to step S106, it can be determined again whether N is greater than T. If N is greater than T, the method can proceed to step S108. In step S108, a curve can be generated. This curve can be based on data from multiple acquired images. Features in the acquired images can be analyzed. For example, gray-level changes of features can be tracked during the acquisition of multiple images. For example, the curve can indicate the trend of gray-level changes in power level or wavelength relative to changes in secondary irradiance.

[0112] Next, in step S109, the curves can be compared. The curve generated in step S108 can be compared with the previously generated curve. Other curves may include curves stored in a database. In some embodiments, multiple features can be identified in each image, and a curve can be generated for each of the multiple features. The curves for each feature can be compared with each other.

[0113] Next, the method may proceed to step S110. Step S110 may include determining the characteristics of the feature. Step S110 may include determining the characteristics of the feature based on a curve that may have already been generated in step S108. Step S110 may include determining that the feature is a defect. Step S110 may include determining that the feature is a specific type of structure. Step S110 may include determining a specific type of defect. The characteristics determined in step S110 may include a range of characteristics of the feature. For example, step S110 may include determining a specific range of components of the structure.

[0114] The method may end after step S110. Other processing may be performed after step S110. In some embodiments, the method may be repeated as needed. For example, Figure 10 The method can be executed first when the method focuses on a specific type of structure, and then executed at a subsequent time when focusing on another type of structure. It can be determined that the sample includes SRAM-WCMP devices, and that the sample includes a large number of NMOS MOSFETs. An optimal point (e.g., P = Py) can be determined, around which evolution can be efficiently performed based on the types of structures included in the sample. Therefore, the threshold T can be set to a relatively low number, and the method can be executed... Figure 10 The method determines the characteristics that can be focused on the structure of the NMOS MOSFET (such as the NMOS source or drain terminals). Subsequently, adjustments can be made based on another structure type. Figure 10 The method is then executed again. Throughput can be improved by lowering the threshold T and initially setting the level of parameter P around the predetermined point.

[0115] In some embodiments, a method for detecting defects may be provided. This method may involve operating a charged particle beam device. The charged particle beam device may include: a primary source configured to generate primary irradiation comprising a primary beam from the charged particle beam device; and a secondary source configured to generate secondary irradiation. The secondary source may be configured to generate secondary irradiation at a predetermined level of parameter P. The parameter P of the secondary irradiation may include the power or wavelength of a laser diode.

[0116] Figure 11 A defect detection method according to an embodiment of the present disclosure is shown. Figure 11 The method can be executed by a controller, such as controller 109. Controller 109 can issue instructions to components of, for example, a charged particle beam system or other systems to perform certain functions. At the beginning Figure 11 In the routine, the method may proceed to step S201. Step S201 may include examining the sample. Step S201 may include performing electron beam imaging on the sample. Performing electron beam imaging may include generating a primary charged particle beam using a charged particle beam device and projecting it onto the sample. Step S201 may include, for example, Figure 2 The primary beam 241 is generated by electron beam tool 100, and may include generating a secondary irradiation beam 242 by secondary source 200. Step S201 may include acquiring an image. Step S201 may include acquiring an evolving image. Step S201 may be performed with a predetermined value of parameter P of the secondary irradiation, and may be performed within a range of values ​​for parameter P. Step S201 may include performing voltage contrast imaging. Step S201 may include performing image processing.

[0117] Next, the method can proceed to step S202. Step S202 may include generating a defect distribution. Generating the defect distribution may be based on the sample inspection in step S201. Step S202 may be performed based on electron beam imaging while applying secondary irradiation. Step S202 may include identifying and confirming defects. Step S202 may include generating icons representing defects, such as histograms. Step S202 may include determining parameters of the defect distribution, such as mean, median, standard deviation, etc. Figure 9B As shown, the defect distribution may include a centerline superimposed on it.

[0118] Next, from Figure 11The method proceeds from step S202 to step S203. Step S203 may include comparing defect distributions. Step S203 may include comparing the defect distribution generated in step S202 with another distribution. For example, the other distribution may include a previously generated distribution or a pre-stored distribution. The other distribution may include a distribution corresponding to imaging performed with different levels of secondary irradiation parameters P. Step S203 may include comparing a first distribution generated by imaging with a first level of parameter P with a second distribution generated by imaging with a second level of parameter P, and so on. Changing parameter P may include changing the wavelength or power of the secondary irradiation. Step S203 may include performing a repeatability analysis.

[0119] Next, the method can proceed to step S204. Step S204 may include generating the final defect distribution. Step S204 may include generating tables based on the results of defect detection analysis. Step S204 may include generating a first table 921 and a second table 922, for example, as per the information about... Figure 9B As discussed, step S204 may include modifying the existing defect distribution. Step S204 may include adding defects captured in other defect distributions such that a single defect distribution reflects defects detected using imaging at different levels with parameter P.

[0120] The method can end after step S204, or other processing can be performed afterward. Figure 10 The method can be with Figure 11 The methods are combined. One method may include various modifications, with or without. Figure 10 Or elements of the flowchart in step 11.

[0121] In some embodiments, identifying defects may include comparison curves, such as in Figure 10 The curve generated in step S108 of the method. For example, Figure 11 Step S202 may include, for example: Figure 10 The comparison curve in step S109. For example, a defect can be identified by comparing a curve of imaging features with a standard defect curve.

[0122] In some embodiments, a mapping method may be provided. A mapping method may include determining the characteristics of multiple features on a sample. These characteristics may be resistance. The method may include, for example, using... Figure 10 The method images the sample and generates a resistance map of the features on the sample. In some embodiments, the method may include generating a composition map of the features on the sample.

[0123] The embodiments may be further described using the following terms:

[0124] 1. A charged particle beam system, comprising:

[0125] The primary source is configured to emit a beam of charged particles along the optical axis onto a region of the sample.

[0126] The secondary source, configured to irradiate the sample area; and

[0127] The controller has a circuit system and is configured to:

[0128] Controlling a charged particle beam system to change the parameters of the secondary source's output;

[0129] The first image of the sample is obtained using the first parameter of the output of the secondary source; and

[0130] The second image of the sample is obtained using the second parameter of the output of the secondary source.

[0131] 2. The charged particle beam system according to Clause 1, wherein:

[0132] The controller is configured to acquire the base image of the sample, where the base image of the sample is acquired using only the primary source.

[0133] 3. The charged particle beam system according to Clause 1 or Clause 2, wherein:

[0134] The controller is configured to acquire multiple images of the sample within a range of parameters.

[0135] 4. The charged particle beam system according to any one of clauses 1 to 3 further includes an image acquisition unit, wherein the image acquisition unit is configured to acquire a first image and a second image.

[0136] 5. The charged particle beam system according to Clause 4, wherein the controller includes an image acquisition unit.

[0137] 6. The charged particle beam system according to any one of clauses 1 to 5 further comprises:

[0138] Dual output source, in which a primary source and a secondary source are included in the dual output source.

[0139] 7. A charged particle beam system according to any one of clauses 1 to 5, wherein the primary source and the secondary source are spaced apart from each other.

[0140] 8. A charged particle beam system according to any one of clauses 1 to 7, wherein:

[0141] The primary source is configured to emit a beam of charged particles to generate the first spot on the sample, and

[0142] The secondary source is configured to emit a secondary irradiation beam to generate a second spot on the sample.

[0143] 9. The charged particle beam system according to Clause 8, wherein:

[0144] The first and second beams are approximately the same size.

[0145] 10. A charged particle beam system according to any one of clauses 1 to 9, wherein:

[0146] The controller is configured to determine the characteristics of features in a region of a sample based on an image acquired with predetermined values ​​of parameters.

[0147] 11. A charged particle beam system according to any one of clauses 1 to 10, wherein:

[0148] The controller is configured to generate curves based on multiple images obtained from the changing values ​​of the parameters.

[0149] 12. A charged particle beam system according to any one of clauses 1 to 11, wherein:

[0150] The controller is configured to generate grayscale level variation curves of features in the regions of the samples based on the first and second images.

[0151] 13. A charged particle beam system according to any one of clauses 1 to 12, wherein:

[0152] The secondary source includes a laser diode.

[0153] 14. A charged particle beam system according to any one of clauses 1 to 13, wherein:

[0154] The secondary source is configured to emit photons.

[0155] 15. An imaging method, comprising:

[0156] A beam of charged particles is fired onto a region of the sample.

[0157] The area of ​​the sample was irradiated with a secondary source;

[0158] The first image of the sample is obtained using the first parameter of the output of the secondary source;

[0159] Change the parameters of the secondary source's output; and

[0160] The second image of the sample is obtained using the second parameter of the output of the secondary source.

[0161] 16. The method described under Clause 15 further includes:

[0162] Acquire a base image of the sample in the region where the secondary source does not irradiate the sample.

[0163] 17. The method according to any one of Clauses 15 or 16, wherein:

[0164] The parameters include the output power of the secondary source.

[0165] 18. The method according to any one of clauses 15 to 17, wherein:

[0166] The parameters include the wavelength of the secondary source's output.

[0167] 19. The method according to any one of clauses 15 to 18 further comprises:

[0168] Based on the image obtained with predetermined values ​​of parameters, the characteristics of features in the sample region are determined.

[0169] 20. The method according to any one of clauses 15 to 19 further comprises:

[0170] Multiple image generation curves are obtained based on the changing values ​​of parameters.

[0171] 21. The method according to any one of clauses 15 to 20, further comprising:

[0172] Gray level variation curves of features in the region of the sample generated based on the first and second images.

[0173] 22. A method for detecting defects, comprising:

[0174] The sample is examined using a beam of charged particles incident on the sample area, and the sample area is irradiated with a secondary source;

[0175] A first defect distribution is generated using the first values ​​of the parameters output by the secondary source; and

[0176] The second defect distribution is generated using the second value of the parameter.

[0177] 23. The method described under Clause 22 further includes:

[0178] Compare the first defect distribution and the second defect distribution; and

[0179] Determine the final defect distribution.

[0180] 24. A method for scanning a wafer using an electron microscope, the method comprising:

[0181] The first position on the sample was scanned multiple times using an electron microscope;

[0182] During each of the multiple occurrences, the first position is irradiated with light from a laser; and

[0183] The parameters of the laser are adjusted multiple times so that the energy or frequency of the light irradiating the first position varies between the multiple adjustments.

[0184] 25. The method described according to Clause 24, wherein:

[0185] The first position corresponds to a pixel in an image generated by an electron microscope.

[0186] 26. The method described under Clause 24 further includes:

[0187] Determine the characteristics of features at a first location on an image acquired with predetermined values ​​of parameters.

[0188] 27. The method according to Clause 26, wherein the features include gray levels of the features.

[0189] 28. The method described under Clause 25 further includes:

[0190] Determine the grayscale level of the pixel.

[0191] 29. The method according to Clause 27, wherein the features include the trend of gray level variation of the feature at different values ​​of the parameter.

[0192] 30. The method according to Clause 27, wherein the features include a trend of grayscale level variation under different amounts of energy or frequency of light illuminating the first position.

[0193] 31. The method described under Clause 28 further includes:

[0194] Determine the trend of grayscale level changes of pixels under different parameter values.

[0195] 32. The method according to any one of clauses 24 to 31 further comprises:

[0196] Multiple images are generated using an electron microscope, each image corresponding to one of many; and

[0197] Multiple image generation curves are obtained based on different parameter values.

[0198] 33. The method according to any one of clauses 24 to 32 further includes:

[0199] Detect defects on the sample;

[0200] Generate the first defect distribution using the first value of the parameter, and

[0201] The second defect distribution is generated using the second value of the parameter.

[0202] 34. The method described under clause 33 further includes:

[0203] Compare the first defect distribution and the second defect distribution; and

[0204] Determine the final defect distribution.

[0205] 35. The method described under Clause 24 further includes:

[0206] The second position on the sample is scanned together with the first position in multiple scans.

[0207] 36. The method described under Clause 35 further includes:

[0208] Compare the characteristics of the first position with the characteristics of the second position.

[0209] 37. A scanning electron microscope, comprising:

[0210] A primary electron source is configured to generate a primary electron beam to be scanned multiple times over a region of the sample, including a first position;

[0211] A laser is configured to irradiate a first position with light from the laser; and

[0212] The controller is configured to adjust the parameters of the laser multiple times, so that the energy or frequency of the light irradiating the first position is different between the multiple times.

[0213] 38. The scanning electron microscope according to Clause 37, wherein:

[0214] The controller is configured to determine the characteristics of features at a first location based on an image acquired with predetermined values ​​of parameters.

[0215] 39. The scanning electron microscope according to Clause 37, wherein:

[0216] The controller is configured to determine the gray level of the pixel corresponding to a first location in an image generated by a scanning electron microscope.

[0217] 40. A scanning electron microscope according to Clause 37, wherein:

[0218] The controller is configured to determine the grayscale level variation trend of the feature at the first location under different values ​​of the parameter.

[0219] 41. The scanning electron microscope according to Clause 37, wherein:

[0220] The controller is configured to generate multiple images acquired from varying values ​​of parameters; and

[0221] Curves are generated based on multiple images.

[0222] 42. The scanning electron microscope according to Clause 37, wherein:

[0223] The controller is configured to detect defects on the sample.

[0224] Generate the first defect distribution using the first value of the parameter, and

[0225] The second defect distribution is generated using the second value of the parameter.

[0226] 43. The scanning electron microscope according to Clause 37, wherein:

[0227] The laser is mounted on the column of the electron microscope.

[0228] 44. A scanning electron microscope according to Clause 37 or Clause 43, wherein:

[0229] The laser is configured to scan together with a primary electronic source.

[0230] 45. A scanning electron microscope as described in Clause 37 or Clause 43, wherein:

[0231] The laser is configured to project a beam spot larger than that of the primary electron beam onto the sample.

[0232] In some embodiments, the source, such as a primary charged particle beam source or a secondary source, may communicate with a controller that controls the charged particle beam system. The controller may instruct components of the charged particle beam system to perform various functions, such as controlling the charged particle source to generate the charged particle beam and controlling the deflector to scan the charged particle beam across the sample. The controller may also perform various other functions, such as adjusting the position of the sample. The controller may include a storage device as a storage medium, such as a hard disk, random access memory (RAM), or other types of computer-readable storage. This storage device may be used to save scanned raw image data as a raw image and to store post-processed images. A non-transitory computer-readable medium may be provided to store instructions for the processor of controller 109 to perform charged particle beam inspection, image processing, defect detection, source evolution, or other functions and methods according to this disclosure. Common forms of non-transitory media include, for example, floppy disks, flexible disks, hard disks, solid-state drives, magnetic tape or any other magnetic data storage media, CD-ROMs, any other optical data storage media, any physical media with a perforated pattern, ROMs, PROMs and EPROMs, FLASH-EPROMs or any other flash memory, NVRAMs, caches, registers, any other memory chips or cassettes, and their networked versions.

[0233] The block diagrams in the figures can illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer hardware / software products according to various exemplary embodiments of the present disclosure. In this respect, each block in the diagrams can represent certain arithmetic or logical operations that can be implemented using hardware, such as electronic circuits. Blocks can also represent modules, segments, or code portions that include one or more executable instructions for implementing a specified logical function. It should be understood that in some alternative implementations, the functions indicated in the blocks may not occur in the order shown in the figures. For example, depending on the functions involved, two blocks shown consecutively may be executed or implemented substantially simultaneously, or the two blocks may sometimes be executed in reverse order. For example, Figure 10 Step S104 can be performed before or during step S103. Some boxes can also be omitted. Any one or more boxes can be omitted. For example, the following boxes can be omitted: Figure 10 Step S102. A method may include acquiring an evolutionary image without acquiring a base image. It should also be understood that each block of the block diagram, and combinations of blocks, may be implemented by a dedicated hardware-based system that performs a specified function or action, or by a combination of dedicated hardware and computer instructions.

[0234] It should be understood that the present invention is not limited to the exact structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A charged particle beam system, comprising: The primary source is configured to emit a beam of charged particles along the optical axis onto a region of the sample. A secondary source is configured to irradiate the region of the sample; as well as The controller has a circuit system and is configured to: Control the charged particle beam system to change the parameters of the secondary source's output over time; At a first moment, a first image of the region of the sample is obtained using the first parameter of the output of the secondary source; At the second moment, a second image of the region of the sample is obtained using the second parameter of the output of the secondary source; as well as The first and second images for the region are analyzed to detect defects.

2. The charged particle beam system according to claim 1, wherein: The controller is configured to acquire a base image of the region of the sample, wherein the base image of the sample is acquired using only the primary source.

3. The charged particle beam system according to claim 1, wherein: The controller is configured to acquire multiple images of the region of the sample within the range of the parameters.

4. The charged particle beam system of claim 1 further includes an image acquisition unit, wherein the image acquisition unit is configured to acquire the first image and the second image.

5. The charged particle beam system of claim 4, wherein the controller includes the image acquisition unit.

6. The charged particle beam system according to claim 1, further comprising: A dual-output source, wherein the primary source and the secondary source are included in the dual-output source.

7. The charged particle beam system of claim 1, wherein the primary source and the secondary source are spaced apart from each other.

8. The charged particle beam system according to claim 1, wherein: The primary source is configured to emit the charged particle beam to generate a first spot on the sample, and The secondary source is configured to emit a secondary irradiation beam to generate a second spot on the sample.

9. The charged particle beam system according to claim 8, wherein: The first beam spot and the second beam spot are substantially the same size.

10. The charged particle beam system according to claim 1, wherein: The controller is configured to determine the characteristics of a feature to be inspected in the region of the sample based on an image acquired at predetermined values ​​of the parameters, wherein the characteristics include the gray level or gray level variation trend of the feature to be inspected.

11. The charged particle beam system according to claim 1, wherein: The controller is configured to generate a curve based on multiple images obtained with varying values ​​of the parameter.

12. The charged particle beam system according to claim 1, wherein: The controller is configured to analyze the first image and the second image for the region in the following manner: based on the first image and the second image, generate a grayscale level variation curve of the features in the region of the sample.

13. The charged particle beam system according to claim 1, wherein: The secondary source includes a laser diode.

14. The charged particle beam system according to claim 1, wherein: The secondary source is configured to emit photons.

15. An imaging method, comprising: A beam of charged particles is fired onto a region of the sample. Irradiate the region of the sample with a secondary source; A first image of the region of the sample is obtained using a first parameter from the output of the secondary source; Change the parameters of the output of the secondary source; A second image of the region of the sample is obtained using the second parameter of the output of the secondary source; as well as The first and second images for the region are analyzed to detect defects.

Images