Laser annealing pattern suppression

Abstract

The positions of grid lines in an image of a laser annealed semiconductor wafer are determined. An area covered by the grid lines can be filled with a new grayscale value. The new grayscale value can be based on a second grayscale value of a neighborhood surrounding the area. The neighborhood is located outside the area covered by the grid lines.

Description

Technical Field

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

[0002] The evolution of semiconductor manufacturing places increasing demands on yield management, and specifically on metrology and inspection systems. While critical dimensions continue to shrink, the industry needs to shorten the time to achieve high-yield, high-value production. Minimizing the time from detecting a yield issue to resolving it maximizes the return on investment for semiconductor manufacturers.

[0003] The fabrication of semiconductor devices, such as logic and memory devices, typically involves processing semiconductor wafers using a number of fabrication processes to form the various features and multiple levels of the semiconductor devices. For example, photolithography is a semiconductor fabrication process that involves transferring a pattern from a mask to a photoresist disposed on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical mechanical polishing (CMP), etching, deposition, and ion implantation. An arrangement of multiple semiconductor devices fabricated on a single semiconductor wafer can 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 in the manufacturing process and, therefore, increase profits. Inspection has always been an important part of manufacturing semiconductor devices, such as integrated circuits (ICs). However, as semiconductor device sizes decrease, inspection becomes more important to successfully manufacturing acceptable semiconductor devices, as even relatively small defects can cause device failure. For example, as semiconductor device sizes decrease, the detection of defects of reduced size becomes necessary, as 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 process's performance capabilities. Furthermore, as design rules shrink, smaller defects can have an impact on the device's electrical parameters, driving more sensitive inspection. As design rules shrink, the number of potential defects related to yield detected by inspection increases dramatically, and the number of harmful defects detected by inspection also increases dramatically. Consequently, 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 affect the device's electrical parameters and yield allows process control methods to focus on those defects while largely ignoring other defects. Furthermore, under smaller design rules, process-induced failures tend to be systematic in some cases. That is, process-induced failures tend to fail within predetermined design patterns that are repeated multiple times within the design. Eliminating spatially systematic, electrically related defects can have an impact on yield.

[0006] The laser annealing process creates a strong grid pattern on the backside of the wafer, which appears as a discoloration in the areas illuminated and heated by the laser. The grid pattern can appear brighter or darker than the rest of the wafer. Because backside illumination tools inspect bare areas, these grid patterns can be flagged or classified as false defects. To avoid this, inspection algorithms are tuned down or the grid pattern is blacked out. These techniques can prevent being overwhelmed by false defects.

[0007] Detuning the defect detection algorithm results in a loss of sensitivity, which can result in damaged chips passing the quality control step. Blackening the grid lines results in the loss of defects that fall on or near the laser annealing grid lines. Figure 1 shows an image of the backside wafer after the laser annealing process has been applied. The regular grid lines are visible. Defects may appear on or near these grid lines. Some of these defects may coincide with the grid pattern. The intensity of some of these defects is similar to the magnitude of the grid lines themselves, which makes it difficult to detect the defects. If the grid lines are blackened, then subsequent defect detection algorithms will not be able to find defects that are close to or on top of the grid lines.

[0008] Therefore, improved systems and techniques are needed. Summary of the Invention

[0009] In a first embodiment, a system is provided. The system includes a light source that generates light; a stage configured to hold a laser-annealed semiconductor wafer in a path of the light; a detector configured to receive the light reflected from the laser-annealed semiconductor wafer; and a processor in electronic communication with the detector. The processor is configured to: generate an image of the laser-annealed semiconductor wafer using data from the detector; determine the location of grid lines in the image; and fill an area covered by the grid lines with a new grayscale value based on a second grayscale value of a neighborhood surrounding the area. The neighborhood is outside the area covered by the grid lines.

[0010] The processor may be further configured to: compare a first grayscale value of the area covered by the grid line with a second grayscale value of the neighborhood surrounding the area; and divide the first grayscale value in the area by a correction ratio to determine the new grayscale value. The correction ratio is the average grayscale in the area divided by the average grayscale in the neighborhood. The second grayscale value may be taken at a location farther from the first grayscale value than the width of the grid line. The comparison, division, and padding may be repeated for all pixels in the grid line of the image.

[0011] The light source and the detector may be configured such that the image is a bright field image or a dark field image.

[0012] The processor may be further configured to perform defect inspection on the image after the region is filled with the new grayscale value.

[0013] The determination may use Sobel edge detection.

[0014] The padding may include histogram remapping.

[0015] In a second embodiment, a method is provided. A processor is used to determine the location of grid lines in an image of a laser-annealed semiconductor wafer. The processor is used to fill an area covered by the grid lines with new grayscale values based on a second grayscale value of a neighborhood surrounding the area. The neighborhood is located outside the area covered by the grid lines.

[0016] The method may further include: using the processor to compare a first grayscale value of the area covered by the grid line with a second grayscale value of the neighborhood surrounding the area; and using the processor to divide the first grayscale value in the area by a correction ratio to determine the new grayscale value. The correction ratio is the average grayscale in the area divided by the average grayscale in the neighborhood. The second grayscale value may be taken at a location farther from the first grayscale value than the width of the grid line. The comparing, dividing, and filling may be repeated for all pixels in the grid line of the image.

[0017] The image may be a bright field image or a dark field image.

[0018] The method may further include performing defect inspection on the image after the filling.

[0019] The determination may use Sobel edge detection.

[0020] The padding may include histogram remapping.

[0021] A non-transitory computer-readable medium stores a program configured to instruct a processor to execute the method of the second embodiment. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] For a more complete understanding of the nature and purpose of the present disclosure, reference should be made to the following detailed description taken in conjunction with the accompanying drawings, in which:

[0023] FIG1 is an image of a backside wafer with a visible laser annealed grid pattern;

[0024] Figure 2 is an image of FIG. 1 , wherein a grid pattern is suppressed using embodiments disclosed herein;

[0025] Figure 3 is a flow chart according to an embodiment of the present disclosure;

[0026] Figure 4 is depicted in use Figure 3 An exemplary image of a defect map before the method;

[0027] Figure 5 is depicted in use Figure 3 an exemplary image of defective material after the method; and

[0028] Figure 6 is an embodiment of a 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 electrical changes may be made without departing from the scope of the present disclosure. The scope of the present disclosure is, therefore, to be defined solely by reference to the appended claims.

[0030] The embodiments disclosed herein correct and suppress brightness variations in discolored areas on laser annealed semiconductor wafers. Defect detection algorithms may then no longer find false defects from this grid pattern, which means that the defect detection algorithms can maintain full sensitivity. For example, Figure 2 is the image of FIG. 1 after suppressing the grid pattern using the embodiments disclosed herein. Figure 2 is no longer visible.

[0031] An embodiment of method 100 is shown in Figure 3 In the figure, each step has a corresponding schematic diagram on the right side. Figure 3 Some or all of the steps of may be performed using a processor. Although a single image is disclosed, method 100 may be performed on multiple images of a semiconductor wafer or across multiple semiconductor wafers.

[0032] The location of grid lines 105 in an image of a laser annealed semiconductor wafer is determined at 101. Grid lines 105 are a darker shade (eg, grayscale) than non-grid lines in the exemplary image, but may also be a lighter shade.

[0033] The image can be a bright field image or a dark field image. Depending on whether the image is a bright field image or a dark field image, the grid lines can be bright or dark.

[0034] The position of the grid lines can vary from wafer to wafer. Furthermore, the grid lines may not align with the wafer orientation. Therefore, the grid lines may appear at an angle. Measuring and correcting for this relative angle allows for more accurate determination of the grid position. In one example, the exact position of the grid lines can be determined by using a regular spacing between the grid lines.

[0035] Several other techniques can be used to determine the position of the grid lines. For example, the grid line positions can be determined using Sobel edge detection or a Canny filter.

[0036] In another example, the grid line positions can be determined in frequency space using a Fourier transform. Fourier analysis examines the image in terms of frequency. Any regularly spaced patterns can appear as strong peaks in the frequency spectrum.

[0037] In another example, the grid line positions can be determined using a regularly repeating grid pattern. The expected positions of the grid lines relative to the design starting point can be used. Other techniques besides those listed herein can be used to determine the positions of the grid lines.

[0038] After the grid line positions are determined at 101 , the area covered by the grid lines 105 is filled with a new grayscale value based on a second grayscale value of a neighborhood 107 surrounding the area 106 . The neighborhood 107 is outside the area 106 covered by the grid lines 105 .

[0039] At 102, a first grayscale value of an area 106 covered by a grid line 105 is compared to a second grayscale value of a neighborhood 107 surrounding the area 106. The neighborhood can be located on either side of the grid line 105 or can be an average of the values on both sides of the grid line 105. The second grayscale value can be taken from a location that is farther away from the grid line than the width of the grid line, which prevents the second grayscale value from being too similar to the first grayscale value. In this example, the second grayscale value is within 64 pixels of the first grayscale value.

[0040] Selecting neighborhood 107 as close as possible to grid line 105 may provide a more accurate measurement of the correction value than if neighborhood 107 were selected further away. The exact distance may be different for different examples and may be selected by the user to optimize the results.

[0041] At 103 , a correction amount is determined. For example, the first grayscale value in region 106 is divided by the correction ratio. Dividing the first grayscale value determines a new grayscale value. The correction ratio is the average grayscale level in region 106 divided by the average grayscale level in neighborhood 107 .

[0042] The average grayscale level can be determined as the average of the grayscale levels in a grid or horizontal / vertical lines. The area can depend on whether horizontal / vertical grid lines are being corrected or whether different areas are being corrected. The size of the grid or lines can be selected by the user to optimize the correction results.

[0043] In another embodiment, the correction amount is determined using an additive term.The correction amount of the gray level may be added to or subtracted from the original value rather than using a correction ratio.

[0044] Region 106 is filled with the new grayscale value at 104. Thus, the pixels on the grid line can be corrected to appear closer to their non-grid neighbors. After this grayscale correction, Figure 2 Shown is the same wafer image as in Figure 1. Now, the gridlines are barely visible to the naked eye. As the signal-to-noise ratio increases, defects on or near the gridlines become visible.

[0045] The region 106 may be filled pixel by pixel or region by region. The region 106 may be filled within the boundaries determined during step 101. Thus, it may be applied to a region 106 comprising multiple pixels, a single pixel, or a line.

[0046] Since grayscale can vary significantly across the wafer, depending on the process applied to the backside of the wafer, grayscale variations can be measured and corrected locally. Grayscale measurements on and off the grid lines can be achieved using several different techniques to measure the average grayscale level in one dimension (linear projection) or two dimensions.

[0047] For example, histogram remapping may be performed to fill region 106. The gray level distributions in region 106 and neighborhood 107 may be compared and the endpoints of the histograms may be matched to each other to find the correct distribution. Other color correction schemes may also be applied.

[0048] The comparison 102, correction amount determination 103, and filling 104 may be repeated for all pixels in the grid lines of the image. This may be performed pixel by pixel or may be performed using multiple local areas within the grid lines.

[0049] After performing method 100 on some or all pixels in a grid line of an image, defect inspection may be performed on the image.

[0050] Method 100 uses a one-dimensional measurement of the average grayscale along a column or row of pixels, depending on the proximity of the pixel to a vertical or horizontal grid line. Areas where grid lines intersect or cross can be corrected by measuring the average brightness in two dimensions. In this example, a grid is used as area 106. The grid can be sized to the intersection area between the vertical and horizontal lines. The grid used at this intersection can be sized so as not to fall on other portions of grid line 105 outside the intersection.

[0051] Figure 4 and 5 Shows exemplary defect diagrams. Figure 4 The defect map in contains both defects and grid lines. This can affect inspection or defect rechecking. Grid lines can hide defects or may be false positives. Figure 5 A defect map is shown for the same area after applying method 100. The grid lines are suppressed, allowing true defects to be identified and classified. Figure 5 Defect analysis will result in Figure 4 Defect analysis with fewer errors.

[0052] One embodiment of system 200 is shown in Figure 6 System 200 includes an optical-based subsystem 201. Generally, optical-based subsystem 201 is configured to generate an optical-based output for sample 202 by directing light to (or scanning with light) and detecting light from sample 202. In one embodiment, sample 202 comprises a wafer. The wafer may comprise any wafer known in the art (e.g., a laser-annealed wafer). Other samples are possible.

[0053] exist Figure 6 In the embodiment of the system 200 shown in FIG, 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 6 , 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 orthogonal angles. Figure 6 , light from light source 203 is directed through optical element 204 and then through lens 205 to reach sample 202 at an oblique angle of incidence. The oblique angle of incidence may include any suitable oblique angle of incidence, which may vary depending on, for example, the characteristics of sample 202.

[0054] The optical-based subsystem 201 can be configured to direct light at different angles of incidence at different times to the sample 202. 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 the light can be directed differently. Figure 6 , the light is directed to the sample 202 at different oblique angles of incidence or normal (or near normal) angles of incidence. In one such example, the optics-based subsystem 201 can be configured to move the light source 203, the optical element 204, and the lens 205 so that light is directed to the sample 202 at different oblique angles of incidence or normal (or near normal) angles of incidence.

[0055] 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 a light source 203, an optical element 204, and a lens 205 (e.g., Figure 62 (shown in FIG. 1 ) and the other of the illumination channels (not shown) may include similar elements, which may be configured differently or identically, or may include at least one light source and may include one or more other components (such as those described further herein). If this light is directed to the sample simultaneously with the other light, one or more characteristics (e.g., wavelength, polarization, etc.) of the light directed to the sample 202 at different angles of incidence may differ, such that light resulting from illumination of the sample 202 at the different angles of incidence can be distinguished from one another at a detector.

[0056] In another case, the lighting subsystem may include only one light source (e.g., Figure 6 ) and the light from the light source can 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 can then be directed to the sample 202. The multiple illumination channels can be configured to direct light to the sample 202 at the same time or at different times (e.g., when the specimen is illuminated sequentially using different illumination channels). In another example, the same illumination channel can be configured to direct light to the sample 202 having different characteristics at different times. For example, in some examples, the optical element 204 can be configured as a spectral filter, and the properties of the spectral filter can be changed in various ways (e.g., by replacing the spectral filter) so that light of different wavelengths can be directed to the sample 202 at different times. The illumination subsystem can have any other suitable configuration known in the art for directing light having different or the same characteristics to the sample 202 sequentially or simultaneously at different or the same angles of incidence.

[0057] In one embodiment, light source 203 may comprise a broadband plasma (BBP) source. In this manner, the light generated by light source 203 and directed toward sample 202 may comprise broadband light. However, the light source may comprise any other suitable light source, such as a laser. The laser may comprise any suitable laser known in the art and may be configured to generate light of any suitable wavelength or wavelengths known in the art. Additionally, the laser may be configured to generate monochromatic or nearly monochromatic light. In this manner, the laser may be a narrowband laser. Light source 203 may also comprise a polychromatic light source that generates light at multiple discrete wavelengths or wavelength bands.

[0058] Light from the optical element 204 can be focused onto the sample 202 by the lens 205. Although the lens 205 is Figure 6 2 is shown as a single refractive optical element, but it is understood that in practice, lens 205 may include several refractive and / or reflective optical elements that, in combination, focus light from the optical element onto the sample. Figure 6The 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, polarization 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 optics-based subsystem 201 may be configured to change one or more of the elements of the illumination subsystem based on the type of illumination to be used to generate the optics-based output.

[0059] The optical-based subsystem 201 may also include a scanning subsystem configured to scan light over the sample 202. For example, the optical-based subsystem 201 may include a stage 206 on which the sample 202 is positioned during generation of the optical-based output. 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 so that the light can be scanned over 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 some scanning of the light over the sample 202. The light can be scanned over the sample 202 in any suitable manner, such as in a serpentine path or in a spiral path.

[0060] 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 responsive to the detected light. For example, Figure 6 The optical-based subsystem 201 shown in FIG includes two detection channels, one formed by a light collector 207, an element 208, and a detector 209, and the other formed by a light collector 210, an element 211, and a detector 212. Figure 6 , 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 sample 202. However, one or more of the detection channels can be configured to detect another type of light from sample 202 (e.g., reflected light).

[0061] like Figure 6As further shown in FIG, the 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 in (e.g., centered) the plane of incidence. However, one or more of the 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, this detection channel may generally be referred to as a "side" channel, and this side channel may be centered in a plane that is generally perpendicular to the plane of incidence.

[0062] although Figure 6 An embodiment of the optical-based subsystem 201 is shown that includes two detection channels, 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, which is positioned in 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 scattered angles normal to or near 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 (i.e., one top channel and two side channels), and each of the at least three channels has its own concentrator, each of the concentrators being configured to collect light at a different scattering angle than each of the other concentrators.

[0063] As further described above, each of the detection channels included in the optical-based subsystem 201 can be configured to detect scattered light. Figure 6 The optical-based subsystem 201 shown in FIG may be configured for dark field (DF) output generation of the sample 202. However, the optical-based subsystem 201 may also or instead 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 DF only, BF only, or both DF and BF imaging. Although each of the light collectors is Figure 6 1 and 2. Although shown as a single refractive optical element in FIG, it is understood that each of the light collectors may include one or more refractive optical dies and / or one or more reflective optical elements.

[0064] 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 manner, if the detector is a non-imaging detector, each of the detectors may be configured to detect a specific characteristic of scattered light (e.g., intensity), but may not be configured to detect such characteristic as a function of position within the imaging plane. Thus, the output generated by each of the detectors included in each of the detection channels of the optical-based subsystem may be a signal or data, but not an image signal or image data. In such an example, a processor (e.g., processor 214) may be configured to generate an image of 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. Thus, the optical-based subsystem may be configured to generate an optical image or other optical-based output described herein in a variety of ways.

[0065] It should be noted that this article provides Figure 6 To generally illustrate the configuration of an optical-based subsystem 201 that may be included in or that may generate an optical-based output used by the system embodiments described herein. The configuration of the optical-based subsystem 201 described herein may be varied to optimize the performance of the optical-based subsystem 201, as is typically done when designing commercial output acquisition systems. Additionally, the systems described herein may be implemented using existing systems (e.g., by adding the functionality described herein to an existing system). For some such systems, the methods described herein may be provided as an optional feature of the system (e.g., in addition to other functionality of the system). Alternatively, the systems described herein may be designed as entirely new systems.

[0066] Processor 214 can be coupled to components of system 200 in any suitable manner (e.g., via one or more transmission media, which may include wired and / or wireless transmission media) so that processor 214 can receive output. Processor 214 can be configured to use the output to perform a number of functions. System 200 can receive instructions or other information from processor 214. Processor 214 and / or electronic data storage unit 215 can optionally be in electronic communication with a wafer inspection tool, a wafer metrology tool, or a wafer re-inspection tool (not shown) to receive additional information or send instructions. For example, processor 214 and / or electronic data storage unit 215 can be in electronic communication with a scanning electron microscope.

[0067] The processor 214, other systems, or other subsystems described herein may be part of a variety of systems, including personal computer systems, graphics computers, mainframe computer systems, workstations, network appliances, Internet appliances, or other devices. The subsystem or system may also include any suitable processor known in the art (e.g., a parallel processor). Additionally, the subsystem or system may include a platform with high-speed processing and software, either as a stand-alone or networked tool.

[0068] 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 a stand-alone control unit or in a centralized quality control unit. Multiple processors 214 or electronic data storage units 215 may be used.

[0069] Processor 214 may be implemented by any combination of hardware, software, and firmware. Furthermore, its functions as described herein may be performed by a single unit or divided among different components, each of which may be implemented by any combination of hardware, software, and firmware. Program code or instructions for processor 214 to implement various methods and functions may be stored in a readable storage medium, such as a memory in electronic data storage unit 215 or other memory.

[0070] If the system 200 includes more than one processor 214, different subsystems may be coupled to each other so that images, data, information, instructions, etc. can be sent between the subsystems. For example, one subsystem may be coupled to another subsystem by any suitable transmission medium, which may include any suitable wired and / or wireless transmission medium known in the art. Two or more such subsystems may also be effectively coupled by a shared computer-readable storage medium (not shown).

[0071] The processor 214 can be configured to use the output of the system 200 or other output to perform a number of functions. For example, the processor 214 can be configured to send the output to the electronic data storage unit 215 or another storage medium. The processor 214 can be configured according to any of the embodiments described herein. The processor 214 can also be configured to use the output of the system 200 or use images or data from other sources to perform other functions or additional steps.

[0072] The various steps, functions, and / or operations of the system 200 and the methods disclosed herein are performed 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 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 wired, cabled, or wireless transmission links. For example, the various steps described throughout this disclosure may be performed by a single processor 214 or, alternatively, by multiple processors 214. Furthermore, different subsystems of the system 200 may include one or more computing or logic systems. Therefore, the above description should not be construed as limiting the present disclosure, but is merely illustrative.

[0073] In an example, processor 214 is in communication with system 200. Processor 214 is configured to generate an image of sample 202 (e.g., a laser annealed semiconductor wafer) using data from detectors 209 and / or 212; determine the location of grid lines in the image; and fill the area covered by the grid lines with new grayscale values based on the second grayscale values of a neighborhood surrounding the area covered by the grid lines. The neighborhood is outside the area covered by the grid lines. This can be accomplished using any of the embodiments disclosed herein.

[0074] Additional embodiments relate to a non-transitory computer-readable medium storing program instructions executable on a controller for performing a computer-implemented method for adjusting grayscale values on gridlines of a laser-annealed semiconductor wafer, as disclosed herein. In particular, Figure 6 , 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 steps of any method described herein, including method 100.

[0075] Program instructions may be implemented in any of a variety of ways, including process-based, component-based, and / or object-oriented techniques, etc. For example, program instructions may be implemented using ActiveX controls, C++ objects, JavaBeans, Microsoft Foundation Classes (MFC), Streaming SIMD Extensions (SSE), or other technologies or methods as needed.

[0076] Illumination using system 200 may be located on the front or back of sample 202 .

[0077] 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 can be made without departing from the scope of the present disclosure. Therefore, the present disclosure is considered to be limited only by the appended claims and a reasonable interpretation thereof.

Claims

1. A detection system comprising: a light source, which generates light; a stage configured to hold the laser-annealed semiconductor wafer in the path of the light; a detector configured to receive the light reflected from the laser-annealed semiconductor wafer; and a processor in electronic communication with the detector, wherein the processor is configured to: using data from the detector to generate an image of the laser annealed semiconductor wafer; determining the locations of grid lines in the image; and The area covered by the grid lines is filled with new grayscale values based on second grayscale values of a neighborhood surrounding the area, wherein the neighborhood is outside the area covered by the grid lines.

2. The detection system of claim 1 , wherein the processor is further configured to: comparing a first grayscale value of the area covered by the grid lines with the second grayscale value of the neighborhood surrounding the area; and The new grayscale value is determined by dividing the first grayscale value in the region by a correction ratio, wherein the correction ratio is the average grayscale in the region divided by the average grayscale in the neighborhood. 3 . The detection system according to claim 2 , wherein the second grayscale value is taken from a position farther away from the first grayscale value than the width of the grid line. 4 . The detection system of claim 2 , wherein the comparing, the dividing, and the filling are repeated for all pixels in the grid lines of the image.

5. The detection system of claim 1, wherein the light source and the detector are configured such that the image is a bright field image. The detection system of claim 1 , wherein the light source and the detector are configured such that the image is a dark field image.

7. The inspection system of claim 1, wherein the processor is further configured to perform defect inspection on the image after the region is filled with the new grayscale value. The detection system of claim 1 , wherein the determining uses Sobel edge detection.

9. The detection system of claim 1, wherein the padding comprises histogram remapping.

10. A detection method comprising: using a processor to determine positions of grid lines in an image of the laser annealed semiconductor wafer; Using the processor, the area covered by the grid lines is filled with new grayscale values based on second grayscale values of a neighborhood surrounding the area, wherein the neighborhood is outside the area covered by the grid lines.

11. The detection method according to claim 10, further comprising: comparing, using the processor, a first grayscale value of the area covered by the grid lines with a second grayscale value of the neighborhood surrounding the area; and The new grayscale value is determined by dividing the first grayscale value in the region by a correction ratio using the processor, wherein the correction ratio is the average grayscale in the region divided by the average grayscale in the neighborhood. 12 . The detection method according to claim 11 , wherein the second grayscale value is taken from a position farther from the first grayscale value than the width of the grid line. 13 . The detection method according to claim 11 , wherein the comparing, the dividing, and the filling are repeated for all pixels in the grid lines of the image. The detection method according to claim 10 , wherein the image is a bright field image. The detection method according to claim 10 , wherein the image is a dark field image. The inspection method of claim 10 , further comprising performing defect inspection on the image after the filling. The detection method according to claim 10 , wherein the determining uses Sobel edge detection. The detection method according to claim 10 , wherein the padding comprises histogram remapping.

19. A non-transitory computer-readable medium storing a program configured to instruct a processor to execute the method according to claim 10.

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