TWM585899U - System for inspecting defects of semiconductor device - Google Patents

Abstract

The present disclosure relates to an inspection system. 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 for detecting defects of semiconductor device

This disclosure relates to inspection systems, and more particularly to systems for detecting defects in semiconductor devices.

During the manufacturing process of semiconductor devices, some semiconductor devices have different defects due to process factors, such as delamination, cracks, holes, and foreign materials. These defects can reduce the performance of the semiconductor device and even cause the semiconductor device to fail or fail. Therefore, the use of inspection systems and methods to detect defects on semiconductor devices can detect problems early in the manufacturing process and improve them to improve yield and profit.

A common detection method is to use a scanning scanning microscope (Scanning Acoustic Tomography, SAT) to scan and photograph the semiconductor device to be tested, and a technician determines the photos obtained by the ultrasonic scanning microscope to find out the defects of the semiconductor device. However, the skills, physical condition, experience, and subjective consciousness of technicians will cause discrepancies in the judgment results and affect the accuracy of the judgment. In addition, with the miniaturization and complication of semiconductor devices, the influence of small defects in semiconductor devices also increases, leading to an increase in human-made judgment errors. Therefore, the semiconductor industry urgently needs an accurate, fast and efficient semiconductor defect detection system and method.

An embodiment of the present disclosure relates to a detection system including an image capturing unit and a processor. The image capturing unit is configured to capture an image of a semiconductor device. The processor is connected to the image capturing unit and 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 semiconductor device If the image has an abnormal area, mark the abnormal area on the image.

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

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

The processing unit 11 is connected to the image capturing unit 10 and is configured to analyze or process the image of the object under test DUT captured by the image capturing unit 10. For example, the processing unit 11 is configured to determine whether the DUT under test is defective according to the image of the DUT under test (to be described in detail in the following paragraphs). According to some embodiments of the present disclosure, the image capturing unit 10 may transmit data to the processing unit 11 through wired transmission or wireless transmission (such as Bluetooth, Wi-Fi, and near field communication (NFC)). According to some embodiments of the disclosure, the processing unit may be or have a processor, such as a central processing unit (CPU), a microcontroller (Microcontroller Unit, MCU), and a field programmable gate array (Field Programmable Gate Array, FPGA) or other suitable processor.

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 DUT of the object to be measured captured by the image capture unit 10. Image. 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), and the like.

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 capture or analyze the results of the processing unit 11 or the image capture unit 10. The DUT image of the object to be tested is output to external devices, such as printers, displays and other external devices. According to some embodiments of the present disclosure, the output unit 13 can output data to an external device through 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 a discrete system.

2 and 3A'-3V are flowcharts 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 methods of FIGS. 2 and 3A'-3V can be performed by the detection system 1 of FIG. According to other embodiments of the present disclosure, the detection methods of FIGS. 2 and 3A'-3V can be performed by other detection systems. Figure 3A 'shows a standard image of an object to be measured. Figures 3A-3V show images of the object under test with defects.

Please refer to FIG. 2. First, in step S21, an image of an object to be measured is captured. The object to be measured may be a semiconductor device, such as, but not limited to, a wafer, a wafer strip, a single wafer, and the like. According to some embodiments of the present disclosure, step S21 may be performed by the image capturing unit 10 of FIG. 1. For example, the SAT can be scanned to obtain an image of the test object. According to some embodiments of the present disclosure, the obtained image of the object to be measured is a two-dimensional grayscale image, which has A A pixel of B has C types 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 obtained image of the object to be measured may be other types of images, such as a three-dimensional image, a color image, and the like. It should be noted that, assuming that the standard image of the object under test is a two-dimensional grayscale image, and the image of the object under test is a color image, the processing unit 11 may be configured to first map or convert the color image of the object under test To the same pixel and color scale as the two-dimensional grayscale image of the standard image of the object to be measured, and vice versa, so that the two images have the same pixel and color scale, which is convenient for comparison.

In step S22, a standard image or a predefined image of the object to be measured is selected. That is, an image of the object to be tested is selected without defects. According to some embodiments of the present disclosure, a standard image of the object to be measured can be provided or input by a user. For example, a technician selects a non-defective image from the images of the plurality of objects to be measured. According to some embodiments of the present disclosure, the standard image of the object to be measured can be automatically selected or generated by the detection system. For example, the processing unit 11 of the detection system 1 in FIG. 1 may automatically generate a standard image of the object to be tested according to a line drawing or a layout diagram of the object to be measured. For example, the processing unit 11 of the detection system 1 in FIG. 1 may automatically select an image without defects from the images of the plurality of objects to be tested according to the line drawing or layout of the object to be measured. According to some embodiments of the present disclosure, the order of steps S21 and S22 can be adjusted.

In step S23, a pixel (that is, a unit comparison pixel) in a range where a comparison is performed is set. The unit comparison pixel is less than or equal to the pixel of the image of the object to be measured. For example, the unit comparison pixel is aA bB, where A B is the pixel of the obtained image of the test object, and 0 a, b 1. The choice of unit comparison pixels will affect the comparison time and accuracy. If the unit comparison pixel is too small, the number of comparisons increases and the comparison time also increases. If the unit comparison pixel is too large, the comparison result is less accurate due to the image noise of the object to be measured. According to some embodiments of the present disclosure, a and b are between about 0.003 and 0.1. Taking FIG. 3A as an example, it reveals an image of an object to be measured. The image of the object to be measured has 35 36 pixels. The pixels included in the dashed box in FIG. 3A are unit comparison pixels. According to the embodiment of FIG. 3A, the dashed box contains 3 3 pixels. That is, FIG. 3A is performed 3 at a time. 3 pixel comparison.

In step S24, the comparison of the image of the object to be measured with the standard image of the object to be measured is started with the set unit comparison pixels. For example, the image of the object under test in FIG. 3A is compared with the standard image of the object under test in FIG. 3A ′. As shown in Figures 3A-3T, 3 The 3-pixel dashed frame starts from the upper left corner of the image of the object to be measured, and moves to the right (such as horizontal movement) in sequence. When the first line is compared, it skips to the next line for comparison until the entire line The image comparison of the measured object is completed. According to some embodiments of the present disclosure, the comparison can be performed from any position of the image of the object to be measured. According to some embodiments of the present disclosure, a vertical comparison may be performed first, and after the comparison in the first column is completed, skip to the next column for comparison.

According to some embodiments of the present disclosure, the comparison in step S24 is to compare the difference (such as the absolute value of the difference) between the grayscale values of the image of the object under test and the standard image of the object under test. If the grayscale value of a pixel of the image of the object to be measured differs from the grayscale value of the corresponding pixel of the standard image of the object to be measured, the pixel of the image of the object to be measured is determined to be abnormal. If the critical value is too small, the judgment result is easily affected by the noise of the image of the object to be measured, which will cause misjudgment; if the critical value is too large, some abnormal pixels cannot be judged, which affects 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 under test, and less than or equal to 100% of the grayscale value of the image of the object under test. For example, if the image of the object to be measured has a grayscale value of 256, the critical value may be 5 or more and 256 or less.

When the abnormal pixel value in the unit comparison pixel is greater than a predetermined number, the position of the object to be measured corresponding to the unit comparison pixel is marked as an abnormal area. The predetermined number can be selected according to test requirements. If the predetermined number is too small, the determination result is easily affected by the noise of the image of the object to be measured, which will cause misjudgment; if the predetermined number is too large, some abnormal areas cannot be determined, which affects the accuracy. According to some embodiments of the present disclosure, the predetermined number is greater than or equal to 30% of the unit-aligned pixels and less than or equal to 100% of the unit-aligned pixels. For example, taking Figure 3A-3T as an example, the unit comparison pixel is 3 3, the predetermined number may be 4.

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

If no abnormal area is found in step S24, as shown in step S25, it is determined that the object to be measured is normal and free of 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 image of the object to be measured The position of the defect (as shown by the circled dotted frame in Figure 3V). According to some embodiments of the present disclosure, the defect location is represented by circle selection, highlighting, pixel coordinates, or other suitable methods.

In step S27, the type of the defect of the object under test is determined according to the grayscale value indicating the position of the defect in the image of the object under test. According to some embodiments of the present disclosure, the grayscale value of all blacks is 0, and the grayscale value of all whites is 255. According to other embodiments of the present disclosure, the grayscale value of all blacks may be 255, and the grayscale value of all whites may be 0. According to some embodiments of the present disclosure, if the image of the object to be measured is obtained by SAT reflection, when the grayscale value indicating the defect position in the image of the object is greater than the relative position of the standard image of the object Gray level value (such as white), it is determined that the defect of the object to be tested is caused by delamination or pollutants (that is, there is a delamination or pollutant at the defect location); and when the defect is marked in the image of the object to be tested The grayscale value of the position is smaller than the grayscale value of the relative position of the standard image of the object to be measured (for example, blackish), it is determined that the defect of the object to be measured is caused by a hole (that is, there is a hole in 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 of the defect location in the image of the object to be tested is smaller than the relative image of the standard image of the object to be tested The gray scale value of the location (for example, it is black), it is determined that the defect of the object to be measured is caused by delamination, pollutants or holes (that is, the layer has delamination, pollutants or holes).

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 image of the object to be tested, whether the object to be tested has a defect, a defect ratio, a defect location, a defect type, and the like. 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 embodiment of the present disclosure, using the methods of FIGS. 2 and 3A′-3V to perform defect detection on the object to be tested can greatly increase the accuracy (eg, close to 100%) and reduce the time required for detection. In addition, with the miniaturization and complication of semiconductor devices, the impact of small defects in semiconductor devices also increases. Using the methods of Figures 2 and 3A'-3V to detect defects in objects to be tested can also greatly increase accuracy.

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

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 flowchart of a detection method according to some embodiments of the present disclosure.
3A'-3V are schematic diagrams of a detection method according to some embodiments of the present disclosure.

Claims (9)

A detection system comprising:
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:
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 abnormality on the image region. The detection system of claim 1, wherein the processor is further configured to set a unit comparison pixel to aA bB, where A B is the pixel of the image of the semiconductor device, and 0 a, b 1. If the detection system of item 2 is requested, 0.003 a, b 0.1. If the inspection system of claim 2, wherein the processor is further configured to compare the grayscale value of each pixel in the unit comparison pixel of the image of the semiconductor device with the standard image of the semiconductor device The difference between the grayscale values of the pixels. The detection system of claim 4, wherein the processor is further configured to set a grayscale value between a pixel of the image of the semiconductor device and a grayscale value of a corresponding pixel of the standard image of the semiconductor device. When the difference is greater than a critical value, the pixel is determined to be an abnormal pixel. If the detection system of claim 5, wherein the processor is further configured to determine that an abnormal pixel in the unit comparison pixel of the image of the semiconductor device is greater than a predetermined number, determine that the unit is included in the unit comparison pixel The area is an abnormal area. If the detection system of claim 6, wherein when the number of the abnormal regions is greater than 1, the processor is further configured to overlap the abnormal regions, and based on the overlapped abnormal regions, the processor Defect locations on the semiconductor device are marked on the image. If the inspection system of claim 7, wherein the processor is further configured to determine the semiconductor device based on the grayscale value indicating the defect position in the image of the semiconductor device and the grayscale value of the standard image of the semiconductor device. Defect type. The detection system of claim 8, wherein the processor is further configured to:
If the image of the semiconductor device is obtained by means of reflection from an ultrasonic scanning microscope (SAT), when the grayscale value of the defect position in the image of the semiconductor device is greater than the gray of the relative position of the standard image of the semiconductor device When the level value is determined, the semiconductor device is delaminated or contaminated; and when the gray level value indicating the defect position in the image of the semiconductor device is smaller than the gray level value of the relative position of the standard image of the semiconductor device, Determine 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 in the image of the semiconductor device is less than that of the standard image of the semiconductor device When the gray value of the relative position is determined, it is determined that the semiconductor device has delamination, contaminants or holes.