TWI840374B - System and method for inspecting defects of semiconductor device - Google Patents

Abstract

The present disclosure relates to an inspection system and an inspection method. The inspection system includes an image capturing unit and a processing unit. The image capturing unit is configured to capture an image of a semiconductor device. The processing unit is connected to the image capturing unit. The processing unit is configured to compare the image of the semiconductor device with a standard image of the semiconductor device to check whether any abnormal region exists in the image, and if so, to highlight the abnormal region.

Description

System and method for detecting defects in semiconductor devices

The present disclosure relates to inspection systems and methods, and more particularly, to systems and methods for inspecting defects in semiconductor devices.

During the manufacturing process of semiconductor devices, some semiconductor devices have various defects due to process factors, such as delamination, cracks, voids, foreign materials, etc. These defects will reduce the performance of semiconductor devices and even cause failure or malfunction of semiconductor devices. Therefore, using detection systems and methods to detect defects on semiconductor devices can detect problems early in the manufacturing process and improve them to increase yield, thereby increasing profits.

The common detection method is to use an ultrasonic scanning microscope (Scanning Acoustic Tomography, SAT) to scan and take pictures of the semiconductor device to be tested, and technicians will judge the photos obtained by the ultrasonic scanning microscope to find out the defects of the semiconductor device. However, the ability, physical condition, experience and subjective awareness of technicians will cause differences in the judgment results and affect the accuracy of the judgment. In addition, with the miniaturization and complexity of semiconductor devices, the impact of tiny defects in semiconductor devices has also increased, resulting in an increase in the error of human judgment. Therefore, the current semiconductor industry is in urgent need of an accurate, fast and efficient semiconductor defect detection system and method.

One embodiment of the present disclosure is related to a detection system, which includes an image capture unit and a processor. The image capture unit is configured to capture an image of a semiconductor device. The processor is connected to the image capture unit and is configured to compare the image of the semiconductor device with a standard image of the semiconductor device to determine whether the image of the semiconductor device has an abnormal area, and if the image of the semiconductor device has an abnormal area, mark the abnormal area on the image.

Another embodiment of the present disclosure is related to a detection method, which includes: (a) capturing an image of a semiconductor device; (b) comparing the image of the semiconductor device with a standard image of the semiconductor device to determine whether the image of the semiconductor device has an abnormal area; and (c) if the image of the semiconductor device has an abnormal area, marking the abnormal area on the image.

FIG1 is a block diagram of a detection system 1 according to some embodiments of the present disclosure. As shown in FIG1 , the detection system 1 includes an image capture unit 10, a processing unit 11, a storage unit 12 and an output unit 13.

The image capture unit 10 is configured to capture images or feature points of the object under test DUT. According to some embodiments of the present disclosure, the object under test DUT may be a semiconductor device, such as but not limited to: a wafer, a chip bar, a single chip, etc. According to some embodiments of the present disclosure, the image capture unit 10 may be an ultrasonic scanning microscope (Scanning Acoustic Tomography, SAT) or any other image capture unit. The image capture unit 10 may capture the image of the object under test DUT by reflection or penetration. The obtained image of the object under test DUT may be a black and white, grayscale, or color image. The obtained image of the object under test DUT may be a two-dimensional or three-dimensional image. According to the thickness of the DUT, the image capture unit 10 can scan the DUT at different frequencies (e.g., from 15 MHz to 230 MHz).

The processing unit 11 is connected to the image capture unit 10 and is configured to analyze or process the image of the object under test DUT captured by the image capture unit 10. For example, the processing unit 11 is configured to determine whether the object under test DUT has a defect based on the image of the object under test DUT (to be described in detail in the following paragraphs). According to some embodiments of the present disclosure, the image capture unit 10 can transmit data to the processing unit 11 via wired transmission or wireless transmission (such as Bluetooth, Wi-Fi, near field communication (NFC) etc.). According to some embodiments of the present disclosure, the processing unit may be or have a processor, such as a central processing unit (CPU), a microcontroller unit (MCU), a field programmable gate array (FPGA) or other suitable processors.

The storage unit 12 is connected to the processing unit 11 and/or the image capture unit 10, and is configured to store the results analyzed or processed by the processing unit 11 and the image of the object under test DUT captured by the image capture unit 10. According to some embodiments of the present disclosure, the storage unit 12 may have a memory unit, such as a random access memory (RAM), a flash memory (Flash), etc.

The output unit 13 is connected to the processing unit 11, the image capture unit 10 and/or the storage unit 12, and is configured to output the result analyzed or processed by the processing unit 11 or the image of the object under test DUT captured by the image capture unit 10 to an external device, such as a printer, a display, or the like. According to some embodiments of the present disclosure, the output unit 13 can output data to the external device via wired transmission or wireless transmission (such as Bluetooth, Wi-Fi, NFC, etc.).

According to some embodiments of the present disclosure, based on different design requirements, the image capture unit 10, the processing unit 11, the storage unit 12 and the output unit 13 can be integrated into a single system or can be a discrete system.

FIG. 2 and FIG. 3A′-3V are flow charts and schematic diagrams of a detection method according to some embodiments of the present disclosure. According to some embodiments of the present disclosure, the detection method of FIG. 2 and FIG. 3A′-3V can be performed by the detection system 1 of FIG. 1. According to other embodiments of the present disclosure, the detection method of FIG. 2 and FIG. 3A′-3V can be performed by other detection systems. FIG. 3A′ shows a standard image of an object to be detected. FIG. 3A-3V shows an image of an object to be detected with defects.

Please refer to FIG. 2. First, in step S21, an image of an object to be tested is captured. The object to be tested may be a semiconductor device, such as but not limited to: a wafer, a chip bar, a single chip, etc. According to some embodiments of the present disclosure, step S21 may be performed by the image capture unit 10 of FIG. 1. For example, the object to be tested may be scanned by SAT to obtain an image of the object to be tested. According to some embodiments of the present disclosure, the image of the object to be tested obtained is a two-dimensional grayscale image, which has A B pixels, and have C kinds of grayscale values, where A, B, and C are positive integers greater than or equal to 1. According to other embodiments of the present disclosure, the image of the object to be tested obtained can be other types of images, such as three-dimensional images, color images, etc. It should be noted that, assuming that the standard image of the object to be tested is a two-dimensional grayscale image, but the image of the object to be tested is a color image, the processing unit 11 can be configured to first map or convert the color image of the object to be tested to the same pixels and color levels as the two-dimensional grayscale image of the standard image of the object to be tested, and vice versa, so that the two images have the same pixels and color levels, which is conducive to comparison.

In step S22, a standard image or a predefined image of the object to be tested is selected. That is, an image of the object to be tested that does not have defects is selected. According to some embodiments of the present disclosure, the standard image of the object to be tested may be provided or input by a user. For example, a technician selects an image without defects from a plurality of images of the objects to be tested. According to some embodiments of the present disclosure, the standard image of the object to be tested may be automatically selected or generated by the detection system. For example, the processing unit 11 of the detection system 1 of FIG. 1 may automatically generate a standard image of the object to be tested based on a wiring diagram or a layout diagram of the object to be tested. For example, the processing unit 11 of the detection system 1 of FIG. 1 may automatically select an image without defects from a plurality of images of the objects to be tested based on a wiring diagram or a layout diagram of the object to be tested. According to some embodiments of the present disclosure, the order of step S21 and step S22 can be interchanged.

In step S23, the pixel range for performing a comparison (i.e., the unit comparison pixel) is set. The unit comparison pixel is less than or equal to the pixel of the image of the object to be tested. For example, the unit comparison pixel is aA bB, where A B is the pixel of the image of the object to be tested, and 0 a, b 1. The selection of the unit comparison pixel will affect the comparison time and accuracy. If the unit comparison pixel is too small, the number of comparisons will increase and the comparison time will also increase. If the unit comparison pixel is too large, the comparison result will be less accurate due to the image noise of the object to be tested. According to some embodiments of the present disclosure, a and b are between about 0.003 and 0.1. For example, FIG. 3A shows an image of an object to be tested, and the image of the object to be tested has 35 36 pixels, the pixels included in the dashed frame of FIG. 3A are unit comparison pixels. According to the embodiment of FIG. 3A, the dashed frame includes 3 3 pixels. That is, FIG. 3A performs 3 3 pixels comparison.

In step S24, the image of the object to be tested is compared with the standard image of the object to be tested using the set unit comparison pixel. For example, the image of the object to be tested in FIG. 3A is compared with the standard image of the object to be tested in FIG. 3A′. As shown in FIG. 3A-3T, 3 The 3-pixel dashed frame starts from the upper left corner of the image of the object to be tested and moves to the right in sequence (such as horizontal movement). After the first row is matched, it jumps to the next row for matching until the image matching of the entire object to be tested is completed. According to some embodiments of the present disclosure, the matching can be started from any position of the image of the object to be tested. According to some embodiments of the present disclosure, the vertical matching can be performed first, and after the first row is matched, it jumps to the next row for matching.

According to some embodiments of the present disclosure, the comparison in step S24 is to compare the difference between the grayscale values of the image of the object to be tested and the standard image of the object to be tested (such as the absolute value of the difference). If the grayscale value of a certain pixel of the image of the object to be tested differs from the grayscale value of the corresponding pixel of the standard image of the object to be tested by more than a critical value, the pixel of the image of the object to be tested is determined to be abnormal. If the critical value is too small, the determination result is easily affected by the noise of the image of the object to be tested, thereby generating a misjudgment; if the critical value is too large, some abnormal pixels cannot be determined, thereby affecting the accuracy. According to some embodiments of the present disclosure, the critical value is greater than or equal to 2% of the grayscale value of the image of the object to be tested, and less than or equal to 100% of the grayscale value of the image of the object to be tested. For example, if the image of the object to be tested has a grayscale value of 256, the critical value may be greater than or equal to 5, and less than or equal to 256.

When the abnormal pixel value within a unit comparison pixel is greater than a predetermined number, the position of the object to be tested corresponding to the unit comparison pixel is marked as an abnormal area. The predetermined number can be selected according to the test requirements. If the predetermined number is too small, the judgment result is easily affected by the noise of the image of the object to be tested, thereby causing misjudgment; if the predetermined number is too large, some abnormal areas cannot be judged, thereby affecting the accuracy. According to some embodiments of the present disclosure, the predetermined number is greater than or equal to 30% of the unit comparison pixel, and less than or equal to 100% of the unit comparison pixel. For example, taking Figures 3A-3T as an example, the unit comparison pixel is 3 3, the reserved quantity can be 4.

As shown in Figures 3A-3E, there are no abnormal pixels in the unit comparison pixels, so the position of the object to be tested corresponding to the unit comparison pixels of Figures 3A-3E is marked as a normal area. As shown in Figures 3F-3L and 3O-3Q, although there are abnormal pixels in the unit comparison pixels, the number of such abnormal pixels is less than the predetermined number (4 in this example), so the position of the object to be tested corresponding to the unit comparison pixels of Figures 3F-3L and 3O-3Q is also marked as a normal area. As shown in Figures 3M-3N and 3R-3T, there are abnormal pixels in the unit comparison pixels, and the number of such abnormal pixels is greater than or equal to the predetermined number (4 in this example), so the position of the object to be tested corresponding to the unit comparison pixels of Figures 3M-3N and 3R-3T is also marked as an abnormal area.

If no abnormal area is found in step S24, then as shown in step S25, the object to be tested is determined to be normal and has no defects.

If an abnormal area is found in step S24, as shown in step S26, the adjacent dashed boxes marked as abnormal areas in step S24 are superimposed (as shown in FIG. 3U ) to mark the defect position in the image of the object to be tested (as shown in the dashed original box circle in FIG. 3V ). According to some embodiments of the present disclosure, the defect position is indicated by circling, highlighting, pixel coordinates or other suitable methods.

In step S27, the defect type of the object to be tested is determined according to the grayscale value of the defect position marked in the image of the object to be tested. According to some embodiments of the present disclosure, the grayscale value of full black is 0, and the grayscale value of full white is 255. According to other embodiments of the present disclosure, the grayscale value of full black can be 255, and the grayscale value of full white can be 0. According to some embodiments of the present disclosure, if the image of the object to be tested is obtained by SAT reflection, when the grayscale value of the defect position marked in the image of the object to be tested is greater than the grayscale value of the relative position of the standard image of the object to be tested (for example, it is whiter), the defect of the object to be tested is determined to be caused by delamination or contamination (that is, delamination or contamination exists at the defect position); and when the grayscale value of the defect position marked in the image of the object to be tested is less than the grayscale value of the relative position of the standard image of the object to be tested (for example, it is darker), the defect of the object to be tested is determined to be caused by a hole (that is, a hole exists at the defect position). According to some embodiments of the present disclosure, if the image of the object to be tested is obtained by SAT penetration, when the grayscale value indicating the defect position in the image of the object to be tested is smaller than the grayscale value of the relative position of the standard image of the object to be tested (for example, darker), it is determined that the defect of the object to be tested is caused by delamination, contamination or voids (i.e., delamination, contamination or voids exist at the defect position).

In step S28, a test result report is provided. The test result report may include but is not limited to an image of the object to be tested, a standard image of the object to be tested, whether the object to be tested has defects, defect ratio, defect location, defect type, etc. According to some embodiments of the present disclosure, steps S22-S27 may be performed by the processing unit 11 of FIG. 1. According to some embodiments of the present disclosure, the test result report may be stored in the storage unit 12 of FIG. 1 and output by the output unit 13 of FIG. 1.

According to the embodiments disclosed herein, the defect detection of the object to be tested using the method of FIGS. 2 and 3A'-3V can greatly increase the accuracy (e.g., close to 100%) and reduce the time required for the detection. In addition, as semiconductor devices become smaller and more complex, the impact of small defects in semiconductor devices also increases. The defect detection of the object to be tested using the method of FIGS. 2 and 3A'-3V can also greatly increase the accuracy.

Although the technical content and features of the present invention are as described above, those with ordinary knowledge in the technical field of the present invention can still make many changes and modifications without departing from the teachings and disclosures of the present invention. Therefore, the scope of the present invention is not limited to the disclosed embodiments but includes other changes and modifications that do not depart from the present invention, which are covered by the scope of the following patent application.

1: Detection system 10: Image acquisition unit 11: Processing unit 12: Storage unit 13: Output unit

FIG. 1 is a block diagram of a detection system according to some embodiments of the present disclosure. FIG. 2 is a flow chart of a detection method according to some embodiments of the present disclosure. FIG. 3A’-3V are schematic diagrams of a detection method according to some embodiments of the present disclosure.

1: Detection system

10: Image capture unit

11: Processing unit

12: Storage unit

13: Output unit

Claims (4)

A detection system includes: an image capture unit configured to capture an image of a semiconductor device; and a processor connected to the image capture unit and configured to: set a unit comparison pixel to aA×bB, where A×B is a pixel of the image of the semiconductor device, and 0<a, b

1; comparing the image of the semiconductor device with a standard image of the semiconductor device to determine whether the image of the semiconductor device has an abnormal area, wherein the comparison includes: comparing the difference between the grayscale value of each pixel in the unit comparison pixel of the image of the semiconductor device and the grayscale value of the corresponding pixel of the standard image of the semiconductor device; when the difference between the grayscale value of a pixel of the image of the semiconductor device and the grayscale value of the corresponding pixel of the standard image of the semiconductor device is greater than or equal to 0. When the value is greater than a critical value, the pixel is determined to be an abnormal pixel; when the number of abnormal pixels in the unit comparison pixel of the image of the semiconductor device is greater than a predetermined number, the area covered by the unit comparison pixel is determined to be an abnormal area, and when the number of the abnormal areas is greater than 1, the abnormal areas are superimposed, and based on the superimposed abnormal areas, the defect position of the semiconductor device is marked on the image of the semiconductor device; according to the gray scale of the defect position marked in the image of the semiconductor device, The type of defect of the semiconductor device is determined by comparing the grayscale value of the semiconductor device and the grayscale value of the standard image of the semiconductor device; and if the image of the semiconductor device is obtained by ultrasonic scanning microscope (SAT) reflection, when the grayscale value of the defect position marked in the image of the semiconductor device is greater than the grayscale value of the relative position of the standard image of the semiconductor device, it is determined that the semiconductor device has delamination or contamination; and when the grayscale value of the defect position marked in the image of the semiconductor device is less than the grayscale value of the relative position of the standard image of the semiconductor device, it is determined that the semiconductor device has delamination or contamination. If the grayscale value of the relative position of the standard image of the semiconductor device is less than the grayscale value of the relative position of the standard image of the semiconductor device, it is determined that the semiconductor device has holes; or if the image of the semiconductor device is obtained by SAT penetration, when the grayscale value of the defect position marked in the image of the semiconductor device is less than the grayscale value of the relative position of the standard image of the semiconductor device, it is determined that the semiconductor device has delamination, contamination or holes; and if the image of the semiconductor device has an abnormal area, the abnormal area is marked on the image. For the test system in claim 1, 0.003

a, b

0.1. A detection method comprises: (a) capturing an image of a semiconductor device; setting a unit comparison pixel to be aA×bB, wherein A×B is a pixel of the image of the semiconductor device, and 0<a, b

1; (b) comparing the image of the semiconductor device with a standard image of the semiconductor device to determine whether the image of the semiconductor device has an abnormal area, wherein step (b) further comprises: comparing the difference between the grayscale value of each pixel in the unit comparison pixel of the image of the semiconductor device and the grayscale value of the corresponding pixel of the standard image of the semiconductor device; when the grayscale value of a pixel of the image of the semiconductor device is greater than the grayscale value of the corresponding pixel of the standard image of the semiconductor device, the difference between the grayscale value of each pixel in the unit comparison pixel of the image of the semiconductor device and the grayscale value of the corresponding pixel of the standard image of the semiconductor device is greater than the grayscale value of the corresponding pixel of the standard image of the semiconductor device. When the difference between the two is greater than a critical value, the pixel is determined to be an abnormal pixel; when the number of abnormal pixels in the unit comparison pixel of the image of the semiconductor device is greater than a predetermined number, the area covered by the unit comparison pixel is determined to be an abnormal area; when the number of the abnormal areas is greater than 1, further comprising: superimposing the abnormal areas; and marking the defective position on the semiconductor device on the image of the semiconductor device based on the superimposed abnormal areas; according to the abnormal areas marked in the image of the semiconductor device The type of defect of the semiconductor device is determined by comparing the grayscale value of the defect position and the grayscale value of the standard image of the semiconductor device; and if the image of the semiconductor device is obtained by ultrasonic scanning microscope (SAT) reflection, when the grayscale value of the defect position marked in the image of the semiconductor device is greater than the grayscale value of the relative position of the standard image of the semiconductor device, it is determined that the semiconductor device has delamination or contamination; and when the grayscale value of the defect position marked in the image of the semiconductor device is less than (c) if the image of the semiconductor device has an abnormal area, marking the abnormal area on the image. As in the test method of claim 3, 0.003

a, b

0.1.

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