Pattern segmentation for interference suppression

Abstract

During inspection of a semiconductor wafer, an image of the semiconductor wafer is divided into segments. A standard deviation for each of the segments is determined using the differential image. A threshold is then applied to each of the segments. The threshold may be a multiple of the standard deviation. And determining the pixels containing defects in the image after the threshold value is applied. The pixels outside the threshold are then marked as a defect of interest using a processor.

Description

Technical Field

[0001] The present disclosure relates to semiconductor wafer inspection. Background Art

[0002] The evolution of the semiconductor manufacturing industry places higher demands on yield management and especially on metrology and inspection systems. Critical dimensions continue to shrink, and the industry needs to reduce the time to achieve high-yield, high-value production. Minimizing the total time from detecting a yield problem to resolving the problem maximizes the return on investment for semiconductor manufacturers.

[0003] The manufacture of semiconductor devices such as logic and memory devices typically includes processing a semiconductor wafer using a number of manufacturing processes to form various features and multiple levels of the semiconductor device. For example, photolithography is a semiconductor manufacturing process that involves transferring a pattern from a mask to a photoresist disposed on a semiconductor wafer. Additional examples of semiconductor manufacturing processes include, but are not limited to, chemical mechanical polishing (CMP), etching, deposition, and ion implantation. An arrangement of multiple semiconductor devices manufactured on a single semiconductor wafer may be separated into individual semiconductor devices.

[0004] Inspection processes are used at various steps during semiconductor manufacturing to detect defects on wafers to promote higher yields and therefore higher profits in the manufacturing process. Inspection has always been an important part of manufacturing semiconductor devices, such as integrated circuits (ICs). However, as the size of semiconductor devices decreases, inspection becomes more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause device failures. For example, as the size of semiconductor devices decreases, detection of defects with reduced sizes has become necessary because even relatively small defects can cause undesirable aberrations in semiconductor devices.

[0005] However, as design rules shrink, semiconductor manufacturing processes can operate closer to the limits of the performance capabilities of the process. In addition, as design rules shrink, smaller defects can have an impact on the electrical parameters of the device, which drives more sensitive inspection. As design rules shrink, the population of potential yield-related defects detected by inspection grows significantly, and the population of interference defects detected by inspection also increases significantly. Therefore, more defects can be detected on the wafer, and correcting the process to eliminate all defects can be difficult and expensive. Determining which defects actually have an impact on the electrical parameters and yield of the device can allow process control methods to focus on the defects and generally ignore other defects. In addition, under smaller design rules, in some cases, process-induced failures tend to be systematic. That is, process-induced failures tend to fail under predetermined design patterns that are usually repeated many times within the design. The elimination of spatially systematic, electrically related defects can have an impact on yield.

[0006] Some inspection methods detect repeating defects on the wafer to detect defects on the mask. For example, if a defect is repeatedly detected at multiple locations on the wafer that correspond to the same location on the mask (a "repeat defect"), the defect may be caused by the mask itself. Therefore, the repeat defect can be analyzed to determine whether it is caused by a mask defect rather than some other cause. Generally speaking, repeat defect detection is performed as a wafer post-processing operation. For example, an inspection tool may perform normal die-to-die inspection, and after reporting all wafer defects, repeat defect detection may be performed in a post-processing step in a user interface rather than in a different computer component of the inspection tool. Repeat defects are defined as defects located at the same location (within a specific tolerance) in several dies.

[0007] For several reasons, it is disadvantageous to use inspection techniques such as die-to-die inspection and die-to-standard reference die inspection to detect systematic and other repetitive defects. For example, although die-to-die inspection techniques are successfully implemented in wafer inspection for detecting random defects, such inspection techniques are, by their nature, unable to detect systematic and repetitive defects. In particular, by comparing two test dies to each other, it is impossible to detect systematic and repetitive defects that appear in two test dies. In addition, in semiconductor manufacturing related applications, the adoption of die-to-standard reference die inspection techniques is far less than die-to-die inspection techniques because it is generally difficult to obtain suitable standard reference dies. For example, unlike die-to-die inspection techniques in which the output for the compared die is generally obtained in the same inspection scan of the wafer, the die-to-standard reference die technique is generally complicated due to differences (e.g., color variations) between the test die and the standard reference die (or the test wafer and the standard reference wafer) and due to the difficulty in achieving relatively accurate alignment between the test die and the standard reference die.

[0008] Previously, the entire die was designated as a CA. EUV PrintCheck wafers are inspected using multi-die automatic thresholding (MDAT) to potentially avoid missing true repeating defects. MDAT uses multiple die information as a reference to reduce process noise and improve defect extraction. The MDAT algorithm attempts to generate a perfect reference for comparison. The CA is not based on the noise level of the design pattern. When the complete die CA is used for wafer inspection, noisy patterns cause a large number of interferences in the detection of repeating defects (repeaters).

[0009] Therefore, new technologies and systems are needed. Summary of the invention

[0010] In a first embodiment, a system is provided. The system includes a semiconductor re-inspection system having: a light source that generates a light beam; a stage that is configured to hold a semiconductor wafer in the path of the light beam; and a detector that receives the light beam reflected from the semiconductor wafer. The system also includes a processor in electronic communication with the semiconductor re-inspection system. The processor is configured to receive an image of the semiconductor wafer generated using the detector; divide the image of the semiconductor wafer into a plurality of segments; determine a standard deviation of each of the segments using a differential image; apply a threshold to each of the segments; determine pixels in the image that include defects after applying the threshold; and mark the pixels outside the threshold as defects of interest. The threshold is a multiple of the standard deviation.

[0011] The processor may be further configured to send instructions to inspect the semiconductor wafer at locations corresponding to the pixels outside the threshold.

[0012] The multiple of the standard deviation may be equal for each of the segments.

[0013] Some of the segments may have edges that encompass noisy areas of the image.

[0014] In a second embodiment, a method is provided. The method includes using a processor to divide an image of a semiconductor wafer into a plurality of segments. Using the processor, determining a standard deviation for each of the segments using a differential image. Using the processor, applying a threshold to each of the segments. The threshold is a multiple of the standard deviation. Using the processor, determining pixels in the image that contain defects after applying the threshold. Using the processor, marking the pixels outside the threshold as defects of interest.

[0015] The method may include inspecting the pixels outside of the threshold.For example, the method may include imaging the semiconductor wafer at a location corresponding to the pixels using a semiconductor inspection system.

[0016] The method may include imaging the semiconductor wafer to produce the image.

[0017] The multiple of the standard deviation may be equal for each of the segments.

[0018] Some of the segments may have edges that encompass noisy areas of the image.

[0019] In a third embodiment, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium includes one or more programs for performing the following steps on one or more computing devices. The steps include dividing an image of a semiconductor wafer into a plurality of segments; determining a standard deviation for each of the segments using a differential image; applying a threshold to each of the segments; determining pixels in the image that include defects after applying the threshold; and marking the pixels outside the threshold as defects of interest. The threshold is a multiple of the standard deviation.

[0020] The steps may include sending instructions to inspect the semiconductor wafer at locations corresponding to the pixels outside the threshold.

[0021] The multiple of the standard deviation may be equal for each of the segments.

[0022] Some of the segments may have edges that encompass noisy areas of the image. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] For a more complete understanding of the nature and objects of the present disclosure, reference should be made to the following detailed description taken in conjunction with the accompanying drawings, wherein:

[0024] Figure 1 is a flow chart of a method according to the present disclosure;

[0025] Figure 2 is an exemplary segmentation map generated from the design file and the inspection image;

[0026] Figure 3 is an exemplary MDAT cloud when a noisy pattern is present in the frame, where the squares are detected defective pixels from the noisy pattern;

[0027] Figure 4 is an exemplary one-dimensional signal-to-noise ratio (1DSNR) histogram of a noisy pattern segment; and

[0028] Figure 5 is an exemplary system according to the present disclosure. DETAILED DESCRIPTION

[0029] Although the claimed subject matter will be described in terms of specific embodiments, other embodiments (including embodiments that do not provide all of the benefits and features set forth herein) are also within the scope of the present disclosure. Various structural, logical, process step, and electronic changes may be made without departing from the scope of the present disclosure. Accordingly, the scope of the present disclosure is defined solely by reference to the appended claims.

[0030] If not detected, mask repeat defects cause yield loss on each mask printed. Without proper segmentation, noisy patterns can cause a large number of interfering repeat defects to be detected using the process of recording (POR) full die care area MDAT. It is difficult and time consuming to identify and separate true yield-killing repeat defects from interfering repeat defects. As disclosed herein, interfering repeat defects from noisy patterns can be suppressed without sacrificing sensitivity to true repeat defects. This provides a faster time to results. The embodiments disclosed herein segment patterns based on noise level and use segmentation maps to verify interference repeat defect suppression. For example, a wafer formed using extreme ultraviolet (EUV) lithography can be tested. Noisy patterns can be segmented from silent patterns based on the noise level of the pattern. By using the 1DSNR detection algorithm disclosed herein, the same 1DSNR ratio can be applied to all fragments. Due to the higher standard deviation, the effective threshold of the noisy pattern will be higher than the silent pattern.

[0031] Figure 1 is a flow chart of method 100. Some or all of the steps in method 100 may be performed using a processor. Method 100 may be applicable to any device, semiconductor wafer, or other workpiece having a noisy pattern.

[0032] An image of a semiconductor wafer is divided into a plurality of segments at 101. The image is generated by imaging the semiconductor wafer. For example, an optical inspection system or an optical re-inspection system may be used to generate the image.

[0033] In an example, the partitioning diagram is based on a design file of a semiconductor wafer. Figure 2 is an exemplary segmentation map. Inspection is typically done on short cycle after-development inspection (ADI) or after-etch inspection (AEI) wafers. This can occur using wafers formed with EUV lithography. For segmentation map generation, the design file from the current inspection step can be used. Patterns can be assigned to their corresponding segments based on their noisiness level, such as Figure 2 In the Figure 2 In the example, each shadow or pattern represents a segment. In this way, noisy patterns can be separated from silent patterns. Patterns can be divided into different segments based on their context. For example, the basic design of the device can be used as at least part of the context. In another example, the noise level can be used as at least part of the context.

[0034] Using embodiments of method 100, different regions of the segmentation map may correspond to areas of interest during inspection. Noisy patterns with greater variance may be subject to more stringent inspection. Quieter segments may be subject to less stringent inspection.

[0035] Return to reference Figure 1, a standard deviation of each of the segments is determined using a difference image at 102. The standard deviation may be determined assuming a Gaussian distribution of noise. The difference image may use a test image (e.g., an evaluated image) and a reference image (e.g., an adjacent die or a synthetic image using an adjacent die).

[0036] A threshold is applied to each of the segments at 103. The threshold is a multiple of the standard deviation. The multiple of the standard deviation may be equal for each of the segments. For example, due to the standard deviation, a multiple of five times the standard deviation will provide a different threshold for noisy areas relative to quiet areas. Thus, interference may be suppressed using this threshold. The threshold may be determined by how many outliers (defects) the user wants to detect. The number of outliers may depend on how defective (clean) the wafer is.

[0037] After applying the threshold, pixels in the image that contain defects are then determined at 104. For example, high sensitivity repetitive defect detection can be performed using broadband plasma (BBP) optical wafer inspection. BBP wafer inspection can provide high sensitivity for key defect types using a 190 to 260 nm wavelength range with high throughput required for reticle re-qualification. This print inspection solution achieves repetitive defect sensitivity using techniques to maximize signal, minimize overall noise, and achieve adequate signal-to-noise ratio.

[0038] Pixels outside the threshold are marked as defects of interest at 105 .

[0039] Figure 3 is an exemplary MDAT cloud when a noisy pattern is present in the frame. The squares are detected defective pixels from the noisy pattern. When a noisy pattern is present in the frame, the noisy pattern tends to have a high variation in differential grayscale, and the MDAT cloud will be sparse for the noisy pattern. These can easily become outliers, such as Figure 3 As a result, many defective pixels will be detected and a large number of noisy repeating defects can be reported in the batch results.

[0040] Figure 4 is an exemplary 1DSNR histogram of noisy pattern segments. Using the embodiments disclosed herein, the standard deviation of each segment is calculated from the difference image in the inspected frame and used in the detection. The standard deviation will be high for noisy segments and low for silent segments. Using the same 1DSNR ratio used in the detection, the effective threshold applied to noisy segments will be higher than that of silent segments. Figure 4 As shown in Figure 3, the same pixels from a noisy pattern that were detected as defective using MDAT are now normalized with 1 DSNR.

[0041] Figure 4The vertical bars in indicate how many pixels have a particular differential gray level. Defective pixels may have a differential gray level above the applied threshold (vertical bar). Figure 4 In the example of , there are no pixels above the 1 DSNR threshold for the segment.

[0042] Method 100 may further include inspecting pixels outside of a threshold. For example, a semiconductor inspection system may be used to image a semiconductor wafer at a location corresponding to the pixel. Further steps may be used to determine whether the defect is a repeating defect. For example, a local signal-to-noise ratio, a repeating defect threshold, or other metrics may be used.

[0043] Some of the segments may have edges that encompass noisy areas of the image.When noisy patterns are segmented from silent patterns using the embodiments disclosed herein, a large number of interfering repetitive defects detected using previous techniques are reduced.

[0044] Embodiments disclosed herein can avoid interference suppression techniques by focusing on real defects. Interference suppression techniques can be problematic because the properties in a specific defect are not always known.

[0045] exist Figure 5 . The system 200 includes an optical-based subsystem 201. In general, the optical-based subsystem 201 is configured to generate an optical-based output of the sample 202 by directing light to the sample 202 (or scanning light across the sample 202) and detecting light from the sample 202. In one embodiment, the sample 202 includes a wafer. The wafer may include any wafer known in the art. In another embodiment, the sample 202 includes a reticle. The reticle may include any reticle known in the art.

[0046] exist Figure 5 In the embodiment of the system 200 shown in FIG. 2 , the optical-based subsystem 201 includes an illumination subsystem configured to direct light to the sample 202. The illumination subsystem includes at least one light source. For example, Figure 5 , the illumination subsystem includes a light source 203. In one embodiment, the illumination subsystem is configured to direct light to the sample 202 at one or more incident angles, which may include one or more oblique angles and / or one or more normal angles. Figure 5 , light from light source 203 is directed at an oblique angle of incidence through optical element 204 and then through lens 205 to sample 202. The oblique angle of incidence may include any suitable oblique angle of incidence that may vary depending on the characteristics of sample 202, for example.

[0047] The optical-based subsystem 201 can be configured to direct light to the sample 202 at different incident angles at different times. For example, the optical-based subsystem 201 can be configured to change one or more characteristics of one or more elements of the illumination subsystem so that light can be directed to the sample 202 at different incident angles. Figure 5 202. In one such example, the optical-based subsystem 201 can be configured to move the light source 203, the optical element 204, and the lens 205 so that the light is directed to the sample 202 at different oblique angles of incidence or normal (or near-normal) angles of incidence.

[0048] In some examples, the optical-based subsystem 201 can be configured to simultaneously direct light at more than one incident angle to the sample 202. For example, the illumination subsystem can include more than one illumination channel, one of which can include, for example, Figure 5 203, optical element 204, and lens 205 are shown in FIG. 20, and another of the illumination channels (not shown) may include similar elements that may be configured differently or identically or may include at least a light source and possibly one or more other components (such as those described further herein). If this light is directed to the sample at the same time as other light, one or more characteristics (e.g., wavelength, polarization, etc.) of the light directed to the sample 202 at different incident angles may be different, so that light generated by illuminating the sample 202 at different incident angles can be distinguished from each other at a detector.

[0049] In another example, the illumination subsystem may include only one light source (e.g., Figure 5 203 shown in FIG. 1 ) and the light from the light source may be separated into different optical paths (e.g., based on wavelength, polarization, etc.) by one or more optical elements (not shown) of the illumination subsystem. The light in each of the different optical paths may then be directed to the sample 202. The multiple illumination channels may be configured to direct light to the sample 202 at the same time or at different times (e.g., when different illumination channels are used to sequentially illuminate the sample). In another example, the same illumination channel may be configured to direct light with different characteristics to the sample 202 at different times. For example, in some examples, the optical element 204 may be configured as a spectral filter and the properties of the spectral filter may be changed in a variety of different ways (e.g., by swapping out the spectral filter) so that light of different wavelengths may be directed to the sample 202 at different times. The illumination subsystem may have any other suitable configuration known in the art for directing light with different or the same characteristics to the sample 202 at different or the same angles of incidence, either sequentially or simultaneously.

[0050] In one embodiment, the light source 203 may include a broadband plasma (BBP) source. In this way, the light generated by the light source 203 and directed to the sample 202 may include broadband light. However, the light source may include any other suitable light source, such as a laser. The laser may include any suitable laser known in the art and may be configured to generate light of any suitable wavelength known in the art. In addition, the laser may be configured to generate monochromatic or near-monochromatic light. In this way, the laser may be a narrow-band laser. The light source 203 may also include a polychromatic light source that generates light of multiple discrete wavelengths or bands.

[0051] Light from optical element 204 can be focused onto sample 202 by lens 205. Figure 5 2 is shown as a single refractive optical element, but it is understood that in reality, lens 205 may include several refractive and / or reflective optical elements that focus light from the optical element in combination onto the sample. Figure 5 The illumination subsystem shown in and described herein may include any other suitable optical elements (not shown). Examples of such optical elements include, but are not limited to, polarizing components, spectral filters, spatial filters, reflective optical elements, apodizers, beam splitters (such as beam splitter 213), apertures, and the like, which may include any such suitable optical elements known in the art. Additionally, the optical-based subsystem 201 may be configured to alter one or more elements of the illumination subsystem based on the type of illumination to be used to generate the optical-based output.

[0052] The optical-based subsystem 201 may also include a scanning subsystem configured to scan light across the sample 202. For example, the optical-based subsystem 201 may include a stage 206 on which the sample 202 is positioned during optical-based output generation. The scanning subsystem may include any suitable mechanical and / or robotic assembly (including the stage 206) that may be configured to move the sample 202 such that light may be scanned across the sample 202. Additionally or alternatively, the optical-based subsystem 201 may be configured such that one or more optical elements of the optical-based subsystem 201 perform a certain scan of light across the sample 202. The light may be scanned across the sample 202 in any suitable manner, such as in a serpentine path or in a spiral path.

[0053] The optical-based subsystem 201 further includes one or more detection channels. At least one of the one or more detection channels includes a detector configured to detect light from the sample 202 due to illumination of the sample 202 by the subsystem and to generate an output in response to the detected light. For example, Figure 5The optical-based subsystem 201 shown in FIG. 1 includes two detection channels, one detection channel formed by a light collector 207, an element 208, and a detector 209, and the other detection channel formed by a light collector 210, an element 211, and a detector 212. Figure 5 , two detection channels are configured to collect and detect light at different collection angles. In some examples, the two detection channels are configured to detect scattered light, and the detection channels are configured to detect light scattered at different angles from the sample 202. However, one or more detection channels may be configured to detect another type of light (e.g., reflected light) from the sample 202.

[0054] like Figure 5 As further shown in FIG, two detection channels are shown as being positioned in the plane of the paper and the illumination subsystem is also shown as being positioned in the plane of the paper. Thus, in this embodiment, the two detection channels are positioned (e.g., centered) in the plane of incidence. However, one or more detection channels may be positioned outside the plane of incidence. For example, the detection channel formed by the light collector 210, the element 211, and the detector 212 may be configured to collect and detect light scattered from the plane of incidence. Thus, such detection channels may be collectively referred to as "side" channels, and such side channels may be centered in a plane substantially perpendicular to the plane of incidence.

[0055] Although Figure 5 An embodiment of an optical-based subsystem 201 including two detection channels is shown, but the optical-based subsystem 201 may include a different number of detection channels (e.g., only one detection channel or two or more detection channels). In one such example, the detection channel formed by the light collector 210, the element 211, and the detector 212 may form one side channel as described above, and the optical-based subsystem 201 may include an additional detection channel (not shown) formed as another side channel positioned on the opposite side of the incident plane. Thus, the optical-based subsystem 201 may include a detection channel that includes the light collector 207, the element 208, and the detector 209 and is centered in the incident plane and is configured to collect and detect light at a scattering angle normal to or close to normal to the surface of the sample 202. Thus, this detection channel may be generally referred to as a "top" channel, and the optical-based subsystem 201 may also include two or more side channels configured as described above. Thus, the optical-based subsystem 201 may include at least three channels (ie, one top channel and two side channels), and each of the at least three channels has its own collector, each collector configured to collect light at a different scattering angle than each other collector.

[0056] As further described above, each detection channel included in the optical-based subsystem 201 can be configured to detect scattered light. Figure 5The optical-based subsystem 201 shown in FIG. 1 may be configured for dark field (DF) output generation of a sample 202. However, the optical-based subsystem 201 may also or alternatively include a detection channel configured for bright field (BF) output generation of the sample 202. In other words, the optical-based subsystem 201 may include at least one detection channel configured to detect light specularly reflected from the sample 202. Thus, the optical-based subsystem 201 described herein may be configured for only DF imaging, only BF imaging, or both DF imaging and BF imaging. Although in FIG. Figure 5 Each light collector is shown in FIG. 5 as a single refractive optical element, but it is understood that each light collector may include one or more refractive optical dies and / or one or more reflective optical elements.

[0057] The one or more detection channels may include any suitable detector known in the art. For example, the detector may include a photomultiplier tube (PMT), a charge coupled device (CCD), a time delay integration (TDI) camera, and any other suitable detector known in the art. The detector may also include a non-imaging detector or an imaging detector. In this way, if the detector is a non-imaging detector, each detector may be configured to detect certain characteristics (such as intensity) of the scattered light but may not be configured to detect such characteristics that vary depending on the position within the imaging plane. Thus, the output generated by each detector in each detection channel included in the optical-based subsystem may be a signal or data, rather than an image signal or image data. In such examples, a processor (such as processor 214) may be configured to generate an image of the sample 202 from the non-imaging output of the detector. However, in other examples, the detector may be configured as an imaging detector configured to generate an imaging signal or image data. Therefore, the optical-based subsystem may be configured to generate an optical image or other optical-based output described herein in several ways.

[0058] It should be noted that this article provides Figure 5 To generally illustrate configurations of an optical-based subsystem 201 that may be included in or that may produce an optical-based output used by system embodiments described herein. The optical-based subsystem 201 configurations described herein may be altered to optimize the performance of the optical-based subsystem 201 as is typically performed when designing a commercial output capture system. Additionally, the systems described herein may be implemented using existing systems (e.g., by adding the functionality described herein to the existing systems). For some such systems, the methods described herein may also be provided as optional functionality of the system (e.g., as a supplement to other functionality of the system). Alternatively, the systems described herein may be designed as completely new systems.

[0059] The processor 214 may be coupled to the components of the system 200 in any suitable manner (e.g., via one or more transmission media, which may include wired and / or wireless transmission media) such that the processor 214 may receive the output. The processor 214 may be configured to perform a number of functions using the output. The system 200 may receive instructions or other information from the processor 214. The processor 214 and / or the electronic data storage unit 215 may optionally be in electronic communication with a wafer inspection tool, a wafer metrology tool, or a wafer recheck tool (not illustrated) to receive additional information or send instructions. For example, the processor 214 and / or the electronic data storage unit 215 may be in electronic communication with a scanning electron microscope.

[0060] The processor 214, other systems, or other subsystems described herein may be part of a variety of systems, including a personal computer system, an image computer, a mainframe computer system, a workstation, a network appliance, an Internet appliance, or other device. The subsystem or system may also include any suitable processor known in the art, such as a parallel processor. In addition, the subsystem or the system may include a platform with high-speed processing and software, either as a stand-alone tool or a networked tool.

[0061] Processor 214 and electronic data storage unit 215 may be disposed in or otherwise be part of system 200 or another device. In an example, processor 214 and electronic data storage unit 215 may be part of an independent control unit or in a centralized quality control unit. Multiple processors 214 or electronic data storage units 215 may be used.

[0062] In practice, the processor 214 may be implemented by any combination of hardware, software, and firmware. Furthermore, its functions as described herein may be performed by one unit or divided among different components, each of which may in turn be implemented by any combination of hardware, software, and firmware. Program code or instructions for the processor 214 to implement various methods and functions may be stored in a readable storage medium, such as a memory in the electronic data storage unit 215 or other storage.

[0063] If the system 200 includes more than one processor 214, the different subsystems may be coupled to each other so that images, data, information, instructions, etc. may be sent between the subsystems. For example, one subsystem may be coupled to an additional subsystem via any suitable transmission media that may include any suitable wired and / or wireless transmission media known in the art. Two or more such subsystems may also be operatively coupled via a shared computer-readable storage medium (not shown).

[0064] The processor 214 may be configured to perform a number of functions using the output of the system 200 or other output. For example, the processor 214 may be configured to send the output to an electronic data storage unit 215 or another storage medium. The processor 214 may be configured according to any of the embodiments described herein. The processor 214 may also be configured to perform other functions or additional steps using the output of the system 200 or using images or data from other sources.

[0065] The various steps, functions and / or operations of the system 200 and the methods disclosed herein are implemented by one or more of the following: electronic circuits, logic gates, multiplexers, programmable logic devices, ASICs, analog or digital controls / switches, microcontrollers, or computing systems. Program instructions implementing methods such as the methods described herein may be transmitted via or stored on a carrier medium. The carrier medium may include storage media such as read-only memory, random access memory, magnetic or optical disks, non-volatile memory, solid-state memory, magnetic tape, and the like. The carrier medium may include transmission media such as wires, cables, or wireless transmission links. For example, the various steps described throughout the present disclosure may be implemented by a single processor 214 or alternatively multiple processors 214. In addition, different subsystems of the system 200 may include one or more computing or logic systems. Therefore, the above description should not be interpreted as limiting the present disclosure but is merely illustrative.

[0066] In an example, the processor 214 is in communication with the system 200. The processor 214 is configured to perform some or all of the steps of the method 100.

[0067] Additional embodiments relate to a non-transitory computer-readable medium storing program instructions executable on a controller to perform a computer-implemented method for detecting repetitive defects, as disclosed herein. In particular, Figure 5 , the electronic data storage unit 215 or other storage medium may contain non-transitory computer-readable media including program instructions executable on the processor 214. The computer-implemented method may include any of the steps of any method described herein, including method 100.

[0068] Program instructions may be implemented in any of a variety of ways, including, among others, procedural, component-based, and / or object-oriented techniques. For example, program instructions may be implemented using ActiveX controls, C++ objects, JavaBeans, Microsoft Foundation Classes (MFC), Streaming SIMD Extensions (SSE), or other techniques or methodologies as desired.

[0069] Although the present disclosure has been described with respect to one or more specific embodiments, it should be understood that other embodiments of the present disclosure may be made without departing from the scope of the present disclosure. Therefore, the present disclosure is deemed to be limited only by the appended claims and reasonable interpretations thereof.

Claims

1. A system comprising: Semiconductor re-inspection system, comprising: a light source that generates a light beam; a stage configured to hold a semiconductor wafer in the path of the light beam; and a detector that receives the light beam reflected from the semiconductor wafer; and a processor in electronic communication with the semiconductor re-inspection system, wherein the processor is configured to: receiving an image of the semiconductor wafer generated using the detector; dividing the image of the semiconductor wafer into a plurality of segments; determining a standard deviation of each of the segments using the difference image; applying a threshold to each of the segments, wherein the threshold is a multiple of the standard deviation; determining pixels in the image that contain defects after applying the threshold; and The pixels outside the threshold are marked as defects of interest. 2 . The system of claim 1 , wherein the processor is further configured to send instructions to inspect the semiconductor wafer at locations corresponding to the pixels outside the threshold.

3. The system of claim 1, wherein the multiple of the standard deviation is equal for each of the segments. 4 . The system of claim 1 , wherein some of the segments have edges that encompass noisy areas of the image.

5. A method comprising: dividing the image of the semiconductor wafer into a plurality of segments using a processor; using the processor, determining a standard deviation for each of the segments using the difference image; applying, with the processor, a threshold to each of the segments, wherein the threshold is a multiple of the standard deviation; using the processor, determining pixels in the image that contain defects after applying the threshold; and The pixels outside the threshold are marked as defects of interest using the processor. The method of claim 5 , further comprising examining the pixels outside of the threshold.

7. The method of claim 6, further comprising imaging the semiconductor wafer at locations corresponding to the pixels using a semiconductor inspection system.

8. The method of claim 5, further comprising imaging the semiconductor wafer to produce the image.

9. The method of claim 5, wherein the multiple of the standard deviation is equal for each of the segments.

10. The method of claim 5, wherein some of the segments have edges that encompass noisy areas of the image.

11. A non-transitory computer-readable storage medium comprising one or more programs for performing the following steps on one or more computing devices: dividing an image of a semiconductor wafer into a plurality of segments; determining a standard deviation of each of the segments using the difference image; applying a threshold to each of the segments, wherein the threshold is a multiple of the standard deviation; determining pixels in the image that contain defects after applying the threshold; and The pixels outside the threshold are marked as defects of interest.

12. The non-transitory computer-readable storage medium of claim 11, wherein the steps further include sending instructions to inspect the semiconductor wafer at locations corresponding to the pixels outside the threshold.

13. The non-transitory computer-readable storage medium of claim 11, wherein the multiple of the standard deviation is equal for each of the segments.

14. The non-transitory computer-readable storage medium of claim 11, wherein some of the segments have edges that encompass noisy areas of the image.

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