Systems and methods for assessing voids and particles in solids

Abstract

A system for evaluating an image of a material. The system includes an image processing unit that obtains an image of an internal portion of a target material, the image including pixels, and a filtering controller that performs noise filtering on the pixels of the received image. The system includes a void separation controller that extracts a subset of pixels corresponding to a target region of the image and segments the subset of pixels into a first portion including voids and particles and a remainder. The system also includes a void and particle count controller that determines a percentage of the first portion relative to the target area and presents a report of the target material, the report including the percentage.

Description

Background Art

[0001] Manufacturing processes often use solder or resin to encapsulate and adhere components to printed circuit boards (PCBs) or other mounting platforms. However, voids, dust particles, etc. often contaminate solder. This increases the possibility of component failure and reduces the effectiveness of the manufacturing process. In addition, glass particles are used as additives for resin deposits, and their concentration is important for determining the characteristics of the resin. Manufacturers use systems and processes to evaluate the percentage of voids, contaminant particles and / or additives in solder or resin / glue to ensure the reliability of adhesive materials and reduce the possibility of defects associated with high ratios or high volumes of voids and / or contaminants. For example, the assessment of the percentage of glass in the resin is used to assess the tolerance and reliability of the resin. The electronics industry sets different limits on the percentage of voids, the maximum void size in solder and glue, and the percentage of glass in the resin, depending on the expected reliability level and specific electronic technology (PWA, MCM, etc.). The method currently used for automatically quantitatively analyzing voids and glass percentages from microscopes and X-ray analysis is usually inaccurate. Summary of the invention

[0002] The object of the present disclosure is to provide a method for material image processing. The method includes obtaining an image of an internal portion of a target material, the image including pixels, and performing noise filtering on the pixels of the received image. The method also includes extracting a subset of pixels corresponding to a target area of ​​the image, and segmenting the subset of pixels into a first portion containing voids and particles and a remaining portion. The method also includes determining a percentage of the first portion relative to the target area and presenting a report of the target material, the report including the percentage. BRIEF DESCRIPTION OF THE DRAWINGS

[0003] The features and advantages of the described embodiments may be more readily understood by referring to the following description in conjunction with the accompanying drawings. For consistency, the same elements in the various drawings are represented by the same reference numerals.

[0004] Figure 1 An imaging system for evaluating voids and particles in solids, glues, and resins according to one or more embodiments of the present disclosure is shown.

[0005] Figure 2 is a method according to one or more embodiments of the present disclosure.

[0006] Figure 3 is an evaluation method according to one or more embodiments of the present disclosure.

[0007] Figure 4.1 , 4.2 , 4.3 and 4.4 show the Figure 3The image associated with the selection step in the method.

[0008] Figure 5.1 and 5.2 A computing system is shown in accordance with one or more embodiments. DETAILED DESCRIPTION

[0009] In the following detailed description of the embodiments of the present disclosure, many specific details are set forth in order to provide a more thorough understanding of the present disclosure. However, it is apparent to those of ordinary skill in the art that the present disclosure can be practiced without these specific details. In other cases, well-known features are not described in detail to avoid unnecessarily complicating the description.

[0010] The present disclosure describes methods and systems for analyzing and quantifying the percentage and size of voids in solder or glue in X-ray images or the percentage of glass in resin in microscope images, including scanning electron microscopes (SEM) and optical microscopes. The disclosed system includes an image processing unit that employs image filtering and image enhancement techniques to produce improved images of the interior of materials such as solder balls or resin / glue balls. The image processing unit enhances the differences between objects inside the material and provides a more accurate measurement of the percentages represented by contaminants, voids, and additives in the bonding material.

[0011] Figure 1 An imaging system (100) for evaluating voids and particles in solids, glues, and resins according to one or more embodiments of the present disclosure is shown. The imaging system (100) includes a scanner (110), a support surface (130), an image capture device (140), and an image processing unit (150). The support surface (130) supports a unit under test (UUT) (120), which may be, for example, an integrated circuit mounted on a printed circuit board. The integrated circuit may be bonded to the printed circuit board by solder or by glue. It may be desirable to evaluate the solder or glue holding the integrated circuit to the printed circuit board. Alternatively, it may be desirable to evaluate glass particles present in the resin of the integrated circuit.

[0012] In an exemplary embodiment, the scanner (110) is an X-ray generator that projects a scanning beam through the UUT (120). The X-ray image is then captured by an image capture device (140), which may be an X-ray detector. In an alternative embodiment, the scanner (110) may be part of an optical microscope that projects light through the resin in the UUT (120), and the image capture device (140) may be a camera. The image capture device (140) captures an image of the UUT (120) in digital form and transmits the digital image to an image processing unit (150).

[0013] The image processing unit (150) includes a filter controller (161), a post-processing controller (163), a void separation controller (164), a background controller (165), and a void / particle determination controller (166). Each of the filter controller (161), the post-processing controller (163), the void separation controller (164), the background controller (165), and the void / particle determination controller (166) may include software code executed by the image processing unit (150) that performs one or more specific image processing functions. The software code may be implemented as an algorithm, such as Figure 2 and Figure 3 The algorithms shown in .

[0014] The filter controller (161) is configured to filter pixels in the image to reduce image noise, increase image sharpness and reduce defects in brightness. The filter controller (161) can perform, for example, non-local mean filtering. Non-local mean filtering is an image processing algorithm used for image denoising. Unlike a "local mean" filter that takes the average of a group of pixels around a target pixel to smooth an image, a non-local mean filter takes the average of the pixels in the image, which is weighted by the degree of similarity between the pixels and the target pixel. Compared with the local mean algorithm, this provides greater post-filter clarity and less loss of image detail.

[0015] The filter controller (161) may also perform, for example, unsharp filtering of the pixels to sharpen edges on elements without increasing noise. Unsharp filtering is an image sharpening technique that uses a blurred or "unsharpened" negative image to create a mask of the original image. The unsharp mask is then combined with the original positive image to produce an image that is less blurred than the original image. The resulting image is sharper, but may be a less accurate representation of the image subject.

[0016] The post-processing controller (163) enables the removal of small objects from the foreground of the image. The post-processing controller (163) performs a first specific thresholding operation that enables the separation of pixels in a target area or an area of ​​interest (e.g., an area filled with solder, glue, or resin) from the rest of the captured image. The first specific thresholding is performed by a thresholding function that separates the image into two classes of pixels based on the gray level of each pixel. Pixels exceeding the threshold level can be identified as, for example, a portion of the solder, and the remaining pixels of the captured image can be identified as the remaining portions of the contact pad that are not covered by the solder. Alternatively, pixels exceeding the threshold level can be identified as, for example, a portion of the resin, and the remaining pixels of the captured image can be identified as the area around the integrated circuit.

[0017] The post-processing controller (163) also performs a second thresholding operation that separates voids from solder or glass from resin within the target area based on the grayscale level of each pixel. Pixels exceeding the second threshold level can be identified as, for example, voids, and the remaining pixels of the target area can be identified as the remaining portion of the solder. Alternatively, pixels exceeding the second threshold level can be identified as, for example, glass particles, and the remaining pixels of the target area can be identified as the remaining portion of the resin. The second thresholding operation can separate voids from solder or glass particles from resin based on grayscale color, regardless of underlying components or structures having grayscale colors that affect the background.

[0018] The void separation controller (164) is configured to perform distance threshold processing to enable voids or glass to be separated from each other in solder or resin. The void separation controller (164) compares the distance between the separated objects with the distance threshold to identify objects (e.g., voids, glass particles, or any other particles) that are close to each other and are not part of the same object. The term "close" means within a predetermined distance. The void separation controller (164) also separates voids that may blend into each other or blend into the background.

[0019] The background controller (165) is configured to remove background from the digital image, wherein the background is the area surrounding the region of interest. The background removal step removes the background on the border of the image.

[0020] The void / particle determination controller (166) is configured to identify, count and calculate the areas of voids or glass in the image and combine these calculated areas with the calculated area of ​​the target area (i.e. solder, glue or resin fill) to determine the voids and glass as a percentage of the total area of ​​the target area (or area of ​​interest). The counting voids operation calculates the total percentage of separated objects (voids, glass particles or any other particles) within the pad (the portion filled with solder, resin or matrix) as well as the percentage of each object within the pad.

[0021] According to an embodiment, the void / particle determination controller (166) can calculate the area of ​​voids, glass particles, solder balls, and resin balls by counting the number of pixels within each object. In an example embodiment, the pixels within the object are arranged in horizontal rows. For each horizontal row that intersects the object, the void / particle determination controller (166) can count the number of pixels between the left boundary and the right boundary to determine the pixel count of the horizontal row. This process is repeated for each horizontal row that intersects the object, and then the pixel counts of the rows are summed to determine the total pixel count of the object. The area of ​​the object is determined by the total pixel count. Therefore, a first void having twice as many pixels as a second void is determined to have twice the area.

[0022] Figure 2is a flowchart (200) illustrating a method according to one or more embodiments of the present disclosure. Figure 1 system to execute Figure 2 method.

[0023] In block (205), the image processing unit (150) obtains an image of an interior portion of a target material. The image may be received from an image capture device (140) and include pixel data.

[0024] In block (210), the image processing unit (150) performs noise filtering on the pixels of the received image. The noise filtering may be performed as described above with respect to the noise filter.

[0025] In block (215), the image processing unit (150) extracts a subset of pixels corresponding to a target region of the image. For example, pixels of the target image are identified based on the intensity distribution of pixels surrounding the image. The identified pixels are then extracted as a subset for further processing.

[0026] In block (220), the image processing unit (150) segments a subset of the plurality of pixels into a first portion including voids and particles and a remaining portion. As described above, a pixel intensity threshold may be used to identify whether a given pixel is a pixel in a void, a pixel in a particle, or a pixel in the remaining portion. The pixels are segmented into segments accordingly.

[0027] In block (225), the image processing unit (150) determines a percentage of the area of ​​the first portion relative to the area of ​​the target region. Thus, for example, the total number of pixels in the void may be divided by the total number of pixels in the target region to determine the percentage.

[0028] In block (230), the image processing unit (150) presents a report of the target material, wherein the report includes a percentage of the first portion relative to an area of ​​the target region. The report may include a processed image of the target material, wherein the target region (solder or resin) is presented in a first color, voids are presented in a second color, glass particles are presented in a third color, and so on. The report may be displayed to a user, or may be fed to another automated process as input for further processing.

[0029] In other embodiments, the report may include a ratio of the first portion to the remaining portion. For example, if the area of ​​the first portion is "3A" and the area of ​​the remaining portion is "A" (i.e., the total area of ​​the target area is "4A"), then the first portion is 75% of the target area. Alternatively, the ratio of the first portion to the remaining portion is 3:1.

[0030] Figure 3is a flow chart (300) illustrating a void and particle evaluation process according to one or more embodiments of the present disclosure. Figure 1 system to execute Figure 3 method. Figure 3 The method is Figure 2 A more detailed implementation of the method in . Figure 4.1 A-4.4 shows a method for performing Figure 3 The selection step in the method is associated with the selected image. Figure 3 The context of the box described is referenced Figure 4.1-4.4 .

[0031] In block 305, the imaging system (100) captures an image through the image capture device (140) and transmits the digital image to the image processing unit (150). As described above, the image capture device (140) may be an X-ray detector that captures the digital image.

[0032] Regarding box 305, refer to Figure 4.1 . Figure 4.1 is an example of a captured image. The captured image includes a target area (420) and a background area (410). The background area (410) covers the entire captured image and is shaded with a cross-hatching pattern. The target area (420) is an irregularly shaped area of ​​interest (e.g., solder, glue / resin, or other deposits). The target area (420) includes a void (430A), a void (430B), and a void (430C), which are shaded with a vertical line pattern. The target area (420) also includes glass particles (440A) and glass particles (440B), which are shaded with a dot pattern. Finally, the target area (420) may include very small contaminants (450A, 450B, 450C, 450D), which appear as black spots in the captured image.

[0033] In block 310, the imaging system (100) performs one or more filtering operations on the captured image. As described above, the filtering operations may include, for example, non-local means filtering, unsharpening filtering, or other types of filtering. The filtering operations process pixels in the image to reduce image noise, increase image sharpness, and reduce brightness defects.

[0034] In block (320), the image processing unit (150) performs a first thresholding operation to separate the target region (420) from a region of the background (410) outside the target region (420). The thresholding operation separates the image into two categories of pixels based on the gray level of each pixel. For example, pixels exceeding the first threshold level may be identified as solder, and the remaining pixels of the image region may be identified as contact pads. Alternatively, pixels exceeding the first threshold level may be identified as resin or deposits, and the remaining pixels of the target region may be identified as pads of the PCB.

[0035] In box (325), the image processing unit (150) performs a second thresholding operation that separates voids from solder and glass from resin within the target area (420). The second thresholding operation separates voids from solder or glass from resin within the target area (420) based on the grayscale level of each pixel. Pixels exceeding the second threshold level can be identified as voids, and the remaining pixels of the target area (420) can be identified as solder. Alternatively, pixels exceeding the second threshold level can be identified as glass balls, and the remaining pixels of the target area (420) can be identified as resin. The second thresholding operation separates voids from solder or glass from resin / glue based on grayscale color, independent of underlying components or structures having grayscale colors that affect the background.

[0036] In block (330), the image processing unit (150) performs a distance thresholding operation that separates voids or glass particles that may be fused to each other. The distance thresholding operation compares the distance between separate objects to a distance threshold to identify objects (e.g., voids, glass particles, or any other particles) that are close to each other and are not part of the same object. With respect to block (325), reference is made to Figure 4.2 . Figure 4.2 is an example of a captured image in which glass particles have been separated. Figure 4.1 In the captured image, the glass particle 440A and the glass particle 440B are in contact with each other. After the distance thresholding operation, the glass particle 440A and the glass particle 440B have been separated in the captured image.

[0037] In block (330), the image processing unit (150) also performs a post-processing operation that identifies small contaminants (450A, 450B, 450C, 450D) that appear as small dots in the captured image after the thresholding operation is completed. The image processing unit (150) may identify a small dot as any object having less than a threshold number of pixels. Alternatively, the image processing unit (150) may identify a small dot as any subject pixel having a significantly different grayscale color than pixels surrounding the subject pixel.

[0038] Then, the image processing unit (150) removes the black spots of the contaminants (450A, 450B, 450C, 450D) from the captured image. With respect to box (330), refer to Figure 4.3 . Figure 4.3 is an example of a captured image from which contaminants (450A, 450B, 450C, 450D) have been removed. Note that the image processing unit (150) may perform a post-processing operation to remove black spots prior to the distance thresholding operation in order to more easily identify objects separated by less than a distance threshold.

[0039] In block (335), the image processing unit (150) removes the background region (410) from the captured image. Figure 4.4 is an example of a captured image in which the background area (410) has been removed, leaving the target area (420).

[0040] In block (340), the image processing unit (150) identifies, counts, and calculates the areas of voids or glass from the captured image, and compares these calculated areas with the calculated target area (420) (i.e., the solder, glue, or resin filled area) to obtain a percentage of voids or glass relative to the solder, glue, or resin filled area. For example, the image processing unit (150) can calculate the areas of voids, glass particles, solder balls, and resin balls by counting the number of pixels within each corresponding object.

[0041] In box (345), the image processing unit (150) performs an evaluation operation, wherein results are generated, such as a spreadsheet with area percentages and a final image of the separated void and target areas (420), such as in Figure 4.4 In the final image, the target area (solder or resin) can be presented in a first color, the voids can be presented in a second color, the glass particles can be presented in a third color, and so on.

[0042] The disclosed systems and methods can separate voids from solder or glass spheres from resin based on their color on the grayscale image and independently of underlying components or structures having a changing background grayscale color. The disclosed systems and methods can then calculate the area percentage of the separated voids / glass spheres relative to the total area of ​​interest (resin / solder).

[0043] Embodiments of the present invention may be implemented on a computing system specifically designed to achieve improved technical results. When implemented in a computing system, the features and elements of the present disclosure provide a significant technical improvement over a computing system that does not implement the features and elements of the present disclosure. Any combination of mobile devices, desktops, servers, routers, switches, embedded devices, or other types of hardware may be improved by including the features and elements described in the present disclosure. For example, Figure 5.1As shown, the computing system (500) may include one or more computer processors (502), non-persistent storage devices (504) (e.g., volatile memory, such as random access memory (RAM), cache memory), persistent storage devices (506) (e.g., hard disk, optical drive (such as compact disk (CD) drive or digital versatile disk (DVD) drive), flash memory, etc.), communication interface (512) (e.g., Bluetooth interface, infrared interface, network interface, optical interface, etc.), and many other elements and functions that implement the features and elements of the present disclosure.

[0044] The computer processor (502) may be an integrated circuit for processing instructions. For example, the computer processor may be one or more cores or micro-cores of a processor. The computing system (500) may also include one or more input devices (510), such as a touch screen, keyboard, mouse, microphone, touchpad, electronic pen, or any other type of input device.

[0045] The communication interface (512) may include an integrated circuit for connecting the computing system (500) to a network (not shown) (e.g., a local area network (LAN), a wide area network (WAN) such as the Internet, a mobile network, or any other type of network) and / or to another device (such as another computing device).

[0046] In addition, the computing system (500) may include one or more output devices (508), such as a screen (e.g., a liquid crystal display (LCD), a plasma display, a touch screen, a cathode ray tube (CRT) monitor, a projector, or other display device), a printer, an external storage device, or any other output device. One or more of the output devices may be the same as or different from the input devices. The input and output devices may be connected locally or remotely to the computer processor (502), the non-persistent storage device (504), and the persistent storage device (506). There are many different types of computing systems, and the aforementioned input and output devices may take other forms.

[0047] The software instructions in the form of computer-readable program code for executing the embodiments of the present invention may be stored in whole or in part, temporarily or permanently, on a non-transitory computer-readable medium, such as a CD, a DVD, a storage device, a floppy disk, a tape, a flash memory, a physical memory, or any other computer-readable storage medium. Specifically, the software instructions may correspond to a computer-readable program code that, when executed by a processor, is configured to execute one or more embodiments of the present invention.

[0048] Figure 5.1 The computing system (500) in the embodiment may be connected to or be part of a network. Figure 5.2As shown, the network (520) may include multiple nodes (e.g., node X (522), node Y (524)). Each node may correspond to a computing system, such as Figure 5.1 The computing system shown, or a group of nodes in combination, may correspond to Figure 5.1 The computing system shown. By way of example, embodiments of the present invention may be implemented on a node of a distributed system connected to other nodes. By way of another example, embodiments of the present invention may be implemented on a distributed computing system having multiple nodes, wherein each portion of the present invention may be located on a different node within the distributed computing system. In addition, one or more elements of the aforementioned computing system (500) may be located at a remote location and connected to other elements via a network.

[0049] Although not in Figure 5.2 , but a node may correspond to a blade in a server chassis that is connected to other nodes via a backplane. By another example, a node may correspond to a server in a data center. By another example, a node may correspond to a computer processor or a micro-core of a computer processor with shared memory and / or resources.

[0050] The nodes in the network (520) (e.g., node X (522), node Y (524)) can be configured to provide services to the client device (526). For example, the node can be part of a cloud computing system. The node can include functionality to receive requests from the client device (526) and send responses to the client device (526). The client device (526) can be a computing system such as Figure 5.1 The computing system shown. In addition, the client device (526) may include and / or execute all or part of one or more embodiments of the present invention.

[0051] Figure 5.1 and 5.2 The computing system or computing system group described in the present invention may include the functionality to perform various operations disclosed herein. For example, (one or more) computing systems may perform communications between processes on the same or different systems. Various mechanisms that employ some form of active or passive communication may facilitate data exchange between processes on the same device. Examples representing these inter-process communications include, but are not limited to, implementations of files, signals, sockets, message queues, pipelines, semaphores, shared memory, message passing, and memory mapped files. Further details regarding several of these non-limiting examples are provided below.

[0052] While the disclosure has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments can be devised which do not depart from the scope of the disclosure as disclosed herein.

Claims

1. A method for material image processing, comprising: obtaining an image of an interior portion of a target material, the image comprising a plurality of pixels; performing noise filtering on a plurality of pixels of the received image; extracting a subset of a plurality of pixels corresponding to a target region of the image; segmenting a subset of the plurality of pixels into a first portion including voids and particles and a remaining portion; determining a percentage of the first portion relative to the target area; and A report of the target material is presented, the report including the percentages. 2 . The method of claim 1 , wherein performing noise filtering on a plurality of pixels comprises increasing sharpness of the image. The method of claim 1 , wherein performing noise filtering on the plurality of pixels comprises reducing a defect in brightness of the image. The method of claim 1 , wherein performing noise filtering on the plurality of pixels comprises performing unsharp filtering on the plurality of pixels. The method of claim 1 , wherein performing noise filtering on the plurality of pixels comprises performing non-local means filtering on the plurality of pixels. The method of claim 1 , wherein the particles comprise glass particles. 7 . The method of claim 1 , wherein the target area comprises an area filled with one of solder, glue, or resin.

8. The method of claim 1, wherein the image comprises an X-ray image.

9. The method of claim 1, wherein the image comprises an optical microscope or a scanning electron microscope image.

10. The method of claim 1, wherein segmenting the subset of the plurality of pixels further comprises separating objects that are close to each other and are not part of the same object.

11. A system for evaluating an image of a material, comprising: an image processing unit configured to obtain an image of an internal portion of a target material, the image comprising a plurality of pixels; a filter controller configured to perform noise filtering on a plurality of pixels of the received image; A gap separation controller, the gap separation controller being configured to: extracting a subset of a plurality of pixels corresponding to a target region of the image; and segmenting a subset of the plurality of pixels into a first portion including voids and particles and a remaining portion; A void and particle count controller configured to: determining a percentage of the first portion relative to the target area; and A report of the target material is presented, the report including the percentages.

12. The system of claim 1, wherein the filter controller filters a plurality of pixels to increase sharpness of the image.

13. The system of claim 1, wherein the filter controller filters a plurality of pixels to reduce a defect in brightness of the image.

14. The system of claim 1, wherein the filter controller is an unsharp filter that enhances edges of the image.

15. The system of claim 1, wherein the filter controller comprises a non-local means filter.

16. The system of claim 1, wherein the particles comprise glass particles.

17. The system of claim 1, wherein the target area comprises an area filled with one of solder, glue, or resin.

18. The system of claim 1, wherein the image comprises an X-ray image.

19. The system of claim 1, wherein the image comprises an optical microscope or a scanning electron microscope image.

20. The system of claim 1, wherein the gap separation controller is configured to separate objects that are close to each other and are not part of the same object.

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