WO2021135045A1 - 一种晶圆检测装置、数据处理方法及存储介质 - Google Patents

一种晶圆检测装置、数据处理方法及存储介质 Download PDF

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Publication number
WO2021135045A1
WO2021135045A1 PCT/CN2020/090991 CN2020090991W WO2021135045A1 WO 2021135045 A1 WO2021135045 A1 WO 2021135045A1 CN 2020090991 W CN2020090991 W CN 2020090991W WO 2021135045 A1 WO2021135045 A1 WO 2021135045A1
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noise
power spectrum
detection target
target image
noise reduction
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PCT/CN2020/090991
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English (en)
French (fr)
Chinese (zh)
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三桥隆
马常群
赵宇航
卢意飞
李铭
李琛
王鹏飞
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上海集成电路研发中心有限公司
上海先综检测有限公司
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Priority to JP2022540764A priority Critical patent/JP7423792B2/ja
Publication of WO2021135045A1 publication Critical patent/WO2021135045A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/9501—Semiconductor wafers
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor
    • H01L22/10—Measuring as part of the manufacturing process
    • H01L22/12—Measuring as part of the manufacturing process for structural parameters, e.g. thickness, line width, refractive index, temperature, warp, bond strength, defects, optical inspection, electrical measurement of structural dimensions, metallurgic measurement of diffusions
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L22/00—Testing or measuring during manufacture or treatment; Reliability measurements, i.e. testing of parts without further processing to modify the parts as such; Structural arrangements therefor
    • H01L22/20—Sequence of activities consisting of a plurality of measurements, corrections, marking or sorting steps
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30148—Semiconductor; IC; Wafer
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present invention relates to the technical field of semiconductor manufacturing, and more specifically, to a wafer inspection device, a data processing method and a storage medium.
  • Wafer inspection devices are commonly used and necessary equipment in the semiconductor manufacturing process.
  • the wafer inspection devices usually include a detection target image generation unit and a data processing unit.
  • the detection target image generation unit is used to obtain information related to the inspected object (such as a wafer).
  • the detection target image, the data processing unit is used to process and determine the defect extraction of the detection target image; the key core work of the data processing unit is to distinguish between effective defects (true defects) and suspected defects (noise signals), and suspected defects are detected Defects caused by small non-essential differences in the target image, among which suspected defects occur randomly during the process.
  • the detection target image generation unit may generally be an optical microscope or a detection device using a scanning electron microscope, or the like.
  • Bright Field Inspector BFI
  • Dark Field Inspector Dark Field Inspector
  • Scanning Electron Microscope (SEM) inspection devices etc.
  • FIG. 1 is a schematic structural diagram of a wafer inspection device commonly used in the prior art.
  • reference numeral 100 in Fig. 1 denotes a bright-field optical detection device.
  • Brightfield microscope (brightfield microscope) is the most versatile optical microscope. Using light illumination, each point in the specimen is imaged in a bright background according to its light absorption and reflection.
  • the light generated by the light source 102 illuminates the wafer 106 through the condenser lens 103, the beam splitter 104 and the objective lens 105.
  • the fine structure of the large-scale integrated circuit (LSI) manufactured on the wafer 106 is obtained by the sensor 101 through the objective lens 105 and the beam splitter 104 to form a detection target image.
  • the detection target image captured by the sensor 101 is read into the computer system 110 (data processing unit) as data.
  • the computer system 110 processes the data read from the sensor 101 according to the execution program 112 recorded in the computer readable medium 111.
  • There are various programs executed in the computer system 110 There are various programs executed in the computer system 110. Among them, one of the most important functions is a defect extraction program, which extracts a part of the detection target image corresponding to the defect of the LSI.
  • FIG. 2 is an explanatory diagram of the principle of the defect extraction procedure in the prior art.
  • reference numeral 200 is an example diagram of the defect extraction program processing the inspection target image, the reference image and the difference extraction as the main output obtained in the wafer inspection apparatus 100.
  • the processing steps are as follows: First, the test target image 202 (test) obtained from the wafer inspection device is compared with the reference image 201 (reference) to create a difference image 203 (difference); then, the difference image 203 is observed, if there is no defect , Then nothing appears on the difference image 203.
  • the difference image 203 appears as a difference defect 205, and the difference image 203 may have a larger size as shown by the difference defect 205. , It also shows the slight difference defect that does not affect the operation of the LSI as shown in 206. From the above-mentioned difference defects, for example, the difference defect 205 and the difference defect 206, it is necessary to select the difference image element actually related to the defect of the LSI.
  • the difference defect should become zero.
  • the difference cannot be zero due to variations in the manufacturing process of the two images and fine dust. Therefore, the above-mentioned small difference signal that affects the image elements caused by the LSI manufacturing process and conditions is called a noise signal.
  • FIG. 3 shows a flowchart of a procedure for extracting defects based on the difference between the detection target image and the reference image.
  • reference number 300 is a schematic diagram of the process, which includes the following steps:
  • Step 301 The detection target image obtained by the sensor is temporarily stored in the image buffer;
  • Step 302 Obtain a reference image library
  • Step 303 Receive the detection target image of the detected object
  • Step 304 Determine the corresponding reference image, and retrieve the reference image corresponding to the detection target image of the detected object from the reference image library;
  • Step 305 align the image size and image position of the detection target image and the reference image
  • Step 306 Compare and judge the difference between the detection target image and the reference image
  • Step 307 Obtain effective difference defects according to the difference calculated in step 306;
  • Step 308 Determine whether the processing of all the detection target images is finished, if not, repeat the processing from step 303 to step 308 for the new detection target image.
  • the above method of calculating the difference between the detected target image and the reference image is based on morphology (Mathematical Morphology) image processing.
  • Morphology is usually used to extract image components that are effective for expressing and describing the shape of the region from the image to enable subsequent recognition work It can grasp the most essential shape features of the target object, such as the processing of boundaries and connected areas.
  • the processing of the detection target image requires the steps of applying structural elements, that is, selecting the most suitable structural element, combining multiple morphological processing, and realizing the processing corresponding to the target.
  • step 307 in the processing in step 307, it is necessary to discuss in detail how to divide the difference information into valid defects (true defects) and meaningless defects (false defects).
  • the point where the intensity of the difference information exceeds a certain threshold is regarded as a bad point (effective defect).
  • the reference number 400 in FIG. 4 is a schematic diagram showing the difference between the detection target image and the reference image and the difference intensity of the passing defect on a certain straight line.
  • the intensity of the difference between the detection target image and the reference image is represented by the shades of the X-axis 405 and Y-axis 406 coordinate points.
  • the dark spot indicated by reference numeral 402 is a part where the difference signal between the detected object and the reference image is large.
  • the portion indicated by reference number 403 that is brighter than reference number 402 is a portion where the difference between the detected object and the reference image is small.
  • Reference numeral 410 denotes a schematic diagram of the intensity of the difference on the straight line A-A' in the figure 401.
  • Reference numeral 411 represents a curve of the difference signal intensity.
  • Reference numeral 412 is an axis representing position coordinates on the straight line A-A'.
  • Reference numeral 413 is an axis indicating the intensity of the difference.
  • the reference number 414 is one of the threshold values of the difference intensity judged as a defect.
  • the reference number 415 is also the threshold value of the difference intensity, which is lower than the reference number 414.
  • the curve representing the intensity of the difference on the straight line A-A' in Fig. 401 is indicated by reference numeral 411.
  • the threshold it can be determined that the number of defective points is different by setting the threshold to IT1 (shown by reference numeral 414) or IT0 (shown by reference numeral 415). Specifically, if the threshold is set to IT1 (indicated by reference numeral 414), one defect is detected, and if the threshold is set to IT0 (indicated by reference numeral 415), then 3 defects are detected. In other words, whether the detected suspected defect is a valid defect or a meaningless defect depends on the threshold IT.
  • the threshold is set low, then the possibility of effective defects becomes higher. Conversely, if the threshold is set high, then the possibility of ignoring effective defects becomes higher.
  • the above-mentioned image processing method of reducing meaningless defect processing using morphology may become a means to improve the accuracy of judging valid defects and meaningless defects.
  • the object of the present invention is to provide a means for accurately estimating parameters such as the power spectrum of the noise signal required for the structure of the noise signal reduction filter.
  • parameters such as the power spectrum of the noise signal required for the structure of the noise signal reduction filter.
  • a wafer inspection device includes a detection target image generation unit and a data processing unit.
  • the detection target image generation unit is used to obtain a detection target image related to a detected object.
  • the data processing unit is used to The image is processed and judged for defect extraction;
  • the data processing unit includes a preprocessing module and a noise reduction module, the preprocessing module includes a lithography simulator and a noise signal extraction module, the lithography simulator is used for the design before inspection
  • the resultant tested wafer layout data is simulated and calculated to obtain the power spectrum Ps(u,v); wherein the tested wafer layout data does not contain noise information;
  • the noise signal extraction module is used to calculate The full signal power spectrum Ps+n(u,v) of the noise of the detection target image on which the noise signal is superimposed, and the full signal power spectrum Ps+n(u,v) is subtracted from the power spectrum Ps( u,v) to obtain the noise power spectrum Pn(u,v);
  • the noise reduction module is used to determine the type
  • the data processing unit further includes a defect extraction module and a restoration module; after obtaining the detection target image, the defect extraction module extracts the detection target image in the detection target image based on morphological image processing technology Elements to obtain the noise image of the detection target image; the restoration module adds the detection target image elements in the detection target image to the detection target image after removing the noise information in the detection target image to form The detection target image after noise is removed.
  • the defect extraction module extracts the detection target image in the detection target image based on morphological image processing technology Elements to obtain the noise image of the detection target image
  • the restoration module adds the detection target image elements in the detection target image to the detection target image after removing the noise information in the detection target image to form The detection target image after noise is removed.
  • the type of the noise reduction filter is a two-dimensional Wiener filter.
  • a data processing method using the above wafer inspection device includes the following steps:
  • Step S11 Perform simulation calculation on the layout data of the tested wafer as the design result before testing to obtain the power spectrum Ps(u,v); wherein the layout data of the tested wafer does not contain noise information ;
  • Step S12 Calculate the full signal power spectrum Ps+n(u,v) of the detection target image superimposed with the noise signal, and subtract the power spectrum Ps from the full signal power spectrum Ps+n(u,v) (u,v), get the noise power spectrum Pn(u,v);
  • Step S13 Determine the noise reduction filter type and its characteristic function according to the signal-to-noise ratio requirement, and use the characteristic function, the noise power spectrum Pn(u,v) and the power spectrum Ps(u,v) as inputs, Obtain the system parameters of the noise reduction filter, and form the noise reduction filter through the system parameters; and filter the detection target image by the noise reduction filter to obtain the detection target image after the noise information is removed.
  • the wafer inspection device further includes: step S10 and step S14:
  • Step S10 After the detection target image is obtained, based on morphological image processing technology, extract the detection target image elements in the detection target image to obtain a noise image of the detection target image;
  • Step S14 The restoration module adds the detection target image elements in the detection target image to the detection target image after removing the noise information in the detection target image to form the detection target image after removing the noise .
  • a computer-readable medium that stores a computer-executable program, and the executable program is used to execute the data processing method of the wafer inspection apparatus; it includes the following programs:
  • the system parameters of the filter are used to form a noise reduction filter through the system parameters; and after the detection target image is filtered by the noise reduction filter, the detection target image after the noise information is removed is obtained.
  • a wafer inspection device includes a detection target image generation unit and a data processing unit.
  • the detection target image generation unit is used to detect and generate a detection target image of a detected object
  • the data processing unit is used to compare the detection target image. Perform processing and judgment of defect extraction; wherein, the data processing unit includes a preprocessing module and a noise reduction module, and the preprocessing module includes:
  • the noise region determination sub-module divides the detection target image formed by the detection target region into M subregions, and selects N subregions smaller than M that contain noise and are used for the estimation of the noise power spectrum to participate in the noise reduction process ;
  • the estimation processing sub-module is used to obtain the full signal power spectrum P N s+n(u,v) of the detection target image of the N sub-regions,
  • the power spectrum estimation stator module obtains the full signal power spectrum Ps+n(u,v) of the detection target image, and uses the full signal power spectrum P N s+n(u,v) of the N sub-regions as the noise power Spectrum Pn(u,v), subtract the noise power spectrum Pn(u,v) from the full signal power spectrum Ps+n(u,v) composed of the M sub-regions to obtain the power spectrum Ps(u,v) );
  • the noise reduction module determines the type of noise reduction filter and its characteristic function according to the signal-to-noise ratio requirement, and combines the characteristic function, the noise power spectrum Pn(u,v) and the power spectrum Ps(u,v) As input, the system parameters of the noise reduction filter are obtained, and the noise reduction filter is formed by the system parameters; and the detection target image is filtered by the noise reduction filter to obtain the detection after the noise information is removed. Target image.
  • the M sub-regions have the same shape, and are preferably rectangular, square, diamond or honeycomb. In addition, sometimes the M sub-regions are preferably seamlessly connected.
  • the average value of the sum of the squared differences of each of the M sub-regions is used, and when the average value is less than a predetermined value, It is determined as N sub-regions composed of noise signal components.
  • a data processing method using the above-mentioned wafer inspection device includes the following steps:
  • Step S20 dividing the detection target image formed by the detection target area into M sub-areas, and selecting N sub-areas smaller than M containing noise to participate in the estimation processing of the noise power spectrum Pn(u,v);
  • Step S21 Obtain the full signal power spectrum P N s+n(u,v) of the detection target image of N signals containing noise and large difference defects;
  • Step S22 Obtain the full signal power spectrum Ps+n(u,v) of the detection target image, and use the full signal power spectrum P N s+n(u,v) of the N sub-regions as the noise power spectrum Pn( u,v), subtracting the noise power spectrum Pn(u,v) from the full signal power spectrum Ps+n(u,v) formed by the M sub-regions to obtain the power spectrum Ps(u,v);
  • Step S23 Determine the type of noise reduction filter and its characteristic function according to the signal-to-noise ratio requirement, and use the characteristic function, the noise power spectrum Pn(u,v) and the power spectrum Ps(u,v) as inputs, Obtain the system parameters of the noise reduction filter, and form the noise reduction filter through the system parameters; and filter the detection target image by the noise reduction filter to obtain the detection target image after the noise information is removed.
  • another technical solution of the present invention is as follows:
  • a computer-readable medium that stores a computer-executable program, and the executable program is used to execute the data processing method of the wafer inspection apparatus; it includes the following programs:
  • the system parameters of the filter are used to form a noise reduction filter through the system parameters; and after the detection target image is filtered by the noise reduction filter, the detection target image after the noise information is removed is obtained.
  • a wafer inspection device includes a detection target image generation unit and a data processing unit.
  • the detection target image generation unit is used to obtain a detection target image from a plurality of detection target regions or across a plurality of wafer regions, the data processing The unit is used to perform defect extraction processing and judgment on the detection target image; it is characterized in that the data processing unit includes a preprocessing module and a noise reduction module, and the preprocessing module includes:
  • the power spectrum inference stator module estimates the full signal power spectrum P i s+n(u,v) according to the detection target image;
  • the noise power spectrum deduces the stator module to obtain the full signal power spectrum P Ni s+n(u,v) of the Ni regions, making it the noise power spectrum P i n(u,v);
  • the difference signal power spectrum estimation stator module according to the noise power spectrum P i n(u,v) and the full signal power spectrum P i s+n(u,v) to estimate the difference signal power spectrum P i s(u ,v);
  • the noise signal extraction sub-module estimates the average value of the range of i of the multiple detection target regions to obtain the average value of Pn (u,v);
  • the difference signal extraction sub-module estimates the average of the range of i with respect to multiple detection target regions to obtain the average value of Ps (u,v);
  • the noise reduction module is used to determine the type of noise reduction filter and its characteristic function according to the signal-to-noise ratio requirements, and combine the characteristic function, the average value of the noise power spectrum Pn (u, v) and the average value of the power spectrum Ps ( u, v) as input, obtain the system parameters of the noise reduction filter, and form the noise reduction filter through the system parameters; and filter the detection target image by the noise reduction filter to obtain the noise-removed information The detection target image.
  • a data processing method using the above-mentioned wafer inspection device includes the following steps:
  • Step S31 Estimate the full signal power spectrum P i s+n(u,v) according to the detection target image
  • Step S32 Obtain the full signal power spectrum P Ni s+n(u,v) of the Ni sub-regions, and make it a noise power spectrum P i n(u,v);
  • Step S33 According to the noise power spectrum P i n(u,v) of the selected Ni regions and the full signal power spectrum P i s+n(u,v), the difference signal power spectrum P i s( u, v), of P i s (u, v) to obtain an average value for the range of i to obtain the average Zhi Ps (u, v), the noise power spectrum P i n (u, v) determined for i The average value of the range of, get the average value of Pn (u,v);
  • Step S34 used to determine the type of noise reduction filter and its characteristic function according to the signal-to-noise ratio requirement, and combine the characteristic function, the average value of the noise power spectrum Pn (u, v) and the average value of the power spectrum Ps (u , v) As input, obtain the system parameters of the noise reduction filter, and form the noise reduction filter through the system parameters; and filter the detection target image by the noise reduction filter to obtain the noise information removed The detection target image.
  • a computer-readable medium that stores a computer-executable program, and the executable program is used to execute the data processing method of the wafer inspection apparatus; it includes the following programs:
  • the present invention provides a noise signal reduction technology that converts the difference between the inspection target image and the reference image into defect evaluation data
  • the ratio of effective defects to insignificant defects (noise signal) in that is, the signal to noise ratio (SNR). Since the present invention estimates the correct noise power spectrum, various noise signal reduction filters can be used to further improve the SNR. For this reason, the parameters of the noise signal reduction filter, that is, the correct estimation of the power spectrum of the signal and the noise signal, etc. The change is particularly important.
  • Figure 1 shows a schematic diagram of a wafer inspection device in the prior art
  • FIG. 2 is a schematic diagram showing defect extraction based on the detection target image and the reference image and the difference image of the wafer inspection device in the prior art
  • Figure 3 shows a flow chart of image data processing using a wafer inspection device in the prior art
  • FIG. 4 is a schematic diagram showing the difference signal between the detection target image and the reference image and the difference intensity of the defect on a straight line in the prior art
  • Fig. 5 is a flowchart showing the determination of the parameters of the noise signal reduction system in an embodiment of the present invention
  • Figure 6 is a schematic diagram of comparing the effects of the original image and the noise signal reduction filter through simulation in an embodiment of the present invention
  • FIG. 7 is an explanatory diagram showing the correspondence between the difference information and the position of the detected object in the embodiment of the present invention
  • Fig. 8 is a flowchart of a method for calculating a power spectrum of a noise signal in an embodiment of the present invention
  • Figure 9 shows a flowchart of image processing using morphology in an embodiment of the present invention
  • Figure 10 is a schematic diagram of the parameterized morphological structural elements in the embodiment of the present invention.
  • the wafer inspection device includes an inspection target image generation unit and a data processing unit.
  • the inspection target image generation unit may be an optical inspection device or an inspection device using a scanning electron microscope, which is not limited herein.
  • the data processing unit processes the obtained wafer inspection target image signal, it is the object of the present invention to reduce the meaningless defects contained in the inspection result, that is, to reduce the noise signal contained in the image information.
  • the object of the present invention is to provide an optimal method for reducing the noise signal contained in the image information.
  • the noise signal (suspected defect) in the image information can be reduced in multiple modes, and the present invention takes the difference between the detection target image and the reference image as an effective defect in the defect evaluation data.
  • the method of considering and dealing with the ratio of meaningless defects (noise signals) can also be used in other fields.
  • the data processing unit is used to process and determine the defect extraction of the detection target image;
  • the data processing unit may include a preprocessing module and a noise reduction module, and the preprocessing module includes a lithography simulator and noise signal extraction Module.
  • the lithography simulator is used to simulate and calculate the layout data of the inspected wafer as the design result before the inspection to obtain the power spectrum Ps(u,v); wherein the layout data of the inspected wafer does not contain noise Information;
  • the noise signal extraction module is used to obtain the full signal power spectrum Ps+n(u,v) of the detection target image overlapped with the noise signal, and subtract the full signal power spectrum Ps+n(u,v) Remove the power spectrum Ps(u,v) to obtain the noise power spectrum Pn(u,v);
  • the noise reduction module is used to determine the type of noise reduction filter and its characteristic function according to the signal-to-noise ratio requirements, and combine the characteristics Function, the noise power spectrum Pn(u,v) and the power spectrum Ps(u,v) as input to obtain the system parameters of the noise reduction filter, and the noise reduction filter is formed by the system parameters; and After the detection target image is filtered by the noise reduction filter, the detection target image with reduced noise information is obtained.
  • the wafer inspection device uses the difference between the inspection target image and the reference image as the ratio of the effective defects to the suspected defects in the defect evaluation data (SNR: signal to noise ratio). ) Becomes larger. It is clear to those skilled in the art that by using various noise reduction filters, the SNR can be improved. For this reason, the correct estimation of the parameters of the noise reduction filter, that is, the signal and noise power spectrum, etc. is particularly important.
  • Wiener filtering is a method of filtering noise-mixed signals using the correlation and frequency characteristics of a stationary random process. Under certain constraints, the square of the difference between its output and a given function (usually called the expected output) Reaching the minimum, through mathematical operations, it can finally be transformed into a Toblitz equation solving problem.
  • the Wiener filter is also called the least square filter or least square filter.
  • H(u,v) is a function representing system characteristics, and H(u,v) is 1 when there is no influence other than noise factors.
  • Pn and Ps cannot be detected separately, but are observed in the form of Ps+n(u,v).
  • Pn(u,v) can be estimated based on some assumptions. However, the results of the estimated Pn(u,v) often contain errors.
  • Pn and Ps are separated for estimation, so a high-performance noise signal reduction system can be provided.
  • the reference numeral 500 in FIG. 5 may be a system, a circuit, and a device.
  • the working process will be described below in conjunction with the reference numerals in FIG. 5. If it is a manufacturing method, the steps of the method will be described in conjunction with the accompanying drawings. In the description, since it is difficult to separately describe the structure and operation, etc., it will be explained together with the structure.
  • FIG. 5 is a schematic diagram of a preferred embodiment for determining the parameters of the noise reduction system of the present invention.
  • reference number 509 is a wafer
  • reference number 510 is a light source for illuminating the wafer
  • reference number 501 is layout design data
  • reference number 502 is a lithography simulator (usually a computer simulation program)
  • reference number 503 is a power spectrum Ps (u,v) calculation module
  • 504 is the calculation result of the signal power spectrum Ps(u,v)
  • 505 is the image sensor
  • 506 is the noise signal extraction sub-module
  • 507 is the noise power spectrum
  • the calculation module of Pn, the reference number 508 is the calculation result of the noise power spectrum Pn(u,v)
  • the reference number 511 represents the characteristic function H(u,v) of the System
  • the reference number 512 represents the determination of the noise reduction system (Noise Reduction System)
  • the reference number 513 represents a noise reduction filter (Noise Reduction Filter).
  • the lithography simulator 502 performs simulation calculation on the layout data 501 without noise information. Based on the simulation calculation result, the power spectrum 503 can be calculated, and the data 504 of the power spectrum Ps(u,v) can be obtained.
  • the detection target image corresponding to the layout data 501 of the wafer 509 is acquired by the sensor 505, and the detection target image obtained from the sensor and the difference image obtained from the reference image contain noise signals.
  • the light source 510 is used to illuminate the wafer 509.
  • the detection target image and the simulation result of the lithography simulator 502 are used as input to complete the step of extracting noise signal information (reference number 506); then, through the calculation step of noise power spectrum Pn (reference number 507), the noise power spectrum can be obtained Pn(u,v), (denoted by reference numeral 508).
  • the characteristic functions H(u,v), Pn(u,v) and Ps(u,v) of the system (System) shown by the reference number 511 are used as input, and the noise reduction system (Noise The parameters of the Reduction System are determined and further constitute a Noise Reduction Filter (No. 513).
  • the detection target image signal obtained by the sensor from the wafer through the optical detection device due to manufacturing, optical, electrical, and other factors, noise signals are superimposed on the detection target image obtained.
  • the image information obtained through lithography simulation simulation does not contain noise signals, and the lithography simulation simulation is based on Layout Data, etc., which are the design information of the LSI. Therefore, the power spectrum Ps(u,v) of the detection target image can be obtained from the image of the optical simulation, and the power spectrum Pn(u,v) of the noise signal can be based on the detection target image obtained by the detection device and the image obtained by lithography simulation simulation. The difference is calculated. Therefore, using the above two power spectra, a high-precision noise signal reduction system can be realized.
  • the detection target image signal of the wafer illuminated by the light source 510 captured by the sensor 505 is superimposed with the following main defect signals: noise signals related to fine defects caused by particles on the wafer, based on optical The noise signal of system defects and the noise signal related to fine defects caused by the wafer manufacturing process.
  • noise signals related to fine defects caused by particles on the wafer, based on optical The noise signal of system defects and the noise signal related to fine defects caused by the wafer manufacturing process.
  • the above-mentioned noise signals cannot be eliminated by data processing steps such as difference calculation in the detection device.
  • the sensor detection output signal Ps+n(u,v) is a mixture of the noise signal Pn(u,v) and the image signal Ps(u,v) of the detected object.
  • the extraction of the signal related to the noise signal Pn(u,v) is the basis of the high-performance noise signal reduction filter shown in formula (1) .
  • a signal Ps(u,v) based only on image information can be obtained, and the image-based signal Ps without a noise signal can be obtained by lithography simulation.
  • the noise signal Pn(u,v) can be obtained.
  • a high-performance noise signal reduction filter can be realized.
  • the number 600 in the figure is a schematic diagram of the effect of comparing the detection target image obtained by the sensor with the image information processed by the noise reduction filter.
  • Numeral 601 shows a schematic diagram of the difference defect between the detected target image including the noise signal obtained by the wafer inspection device and the reference image, wherein the image shown by the numeral 601 has not been processed to reduce the noise signal.
  • Numerals 602 and 603 indicate X-axis and Y-axis, respectively, and on the plane generated by the X-axis and Y-axis, the intensity of the difference signal is expressed in shades.
  • the number 604 is a large difference caused by the existence of the defect, and a thin spot can be seen in the area of the number 605, which corresponds to a noise signal.
  • the reference number 610 is the image after the noise is removed. Please refer to the figure on the right.
  • the image numbered 610 is a simulation result of using a noise signal reduction filter.
  • Numerals 612 and 613 indicate the X-axis and the Y-axis, and the intensity of the difference signal is expressed in shades on the plane generated by the X-axis and the Y-axis.
  • the number 614 is the large difference caused by the existence of the defect, and the thinner spot can be seen in the area of the number 615, which corresponds to the situation after the noise signal reduction filter is used.
  • the number 605 when the noise signal reduction filter is not used is distinguished from the number 615 when the noise signal reduction filter is used, so that the difference in the status can be clearly seen.
  • the SNR Signal to Noise Ratio
  • the SNR is 15.0dB.
  • the detection target area is divided into a plurality of sub-areas, and it is judged whether the information indicating the difference intensity information contained in each sub-area is the information related to the peak of the difference or the information related to the noise signal.
  • One method Use only the sub-regions related to the noise signal to calculate the power spectrum of the noise signal, and use its power spectrum to form a noise signal reduction filter.
  • the noise reduction area used for the estimation of the power spectrum of the noise signal that is, to divide the detection target image formed by the detection target area into M seamless Connect the sub-regions, and select the N regions below M to participate in the estimation of the noise power spectrum.
  • the N regions participate in the selection of the noise power spectrum, and the average value of the sum of the squares of the difference intensities of each subregion can be taken.
  • the average value is less than a predetermined value, it is determined as a subregion composed of noise signal components.
  • the reference number 700 in FIG. 7 is a schematic diagram showing the difference information corresponding to the position of the detected object.
  • a peak based on the difference of the defect as shown by the number 701 appears, and the number 702 and the number 703 are peaks based on the difference of other defects, but the intensity of the latter two peaks is relatively small.
  • the peak value of the defect difference is plotted in a three-dimensional space, where the coordinate axis is composed of the X axis (reference numeral 706), the Y axis (reference numeral 707), and the intensity of the difference (reference numeral 708).
  • the X-Y plane is divided into virtual sub-areas (reference number 704).
  • the number of virtual sub-regions (also called cells) is set to 6 ⁇ 6, which is composed of sub-regions related to the difference peak and sub-regions (reference number 705) mainly composed of noise signal components.
  • the virtual sub-region does not have to be rectangular, and can be any shape. However, it is desirable that the virtual sub-regions cannot overlap each other, and they cover the entire detected object region without gaps.
  • a classification method use statistical properties related to the strength of the differences in each subregion to make judgments. For example, taking the average value of the sum of the squares of the difference intensity of each sub-region, and when the average value is less than a predetermined value, it is determined as a sub-region composed of noise signal components. This determination method is only used as an example, and other determination methods are also possible, which are not limited here.
  • FIG. 8 is a flowchart of a method for calculating a power spectrum of a noise signal in this embodiment.
  • the method for calculating the power spectrum of the noise signal includes the following steps:
  • the detected object area is divided into a plurality of sub-areas by, for example, a virtual grid method.
  • the shape of the sub-region does not need to be rectangular, and may be any shape.
  • step 802 it is necessary to first determine whether the focused sub-region contains a defect signal.
  • the judgment step can use the method based on statistical properties described above.
  • step 803 it is necessary to first determine whether a difference peak (defect) is included. If it is included, skip step 804. If it is not included, in step 804, evaluate and improve the power spectrum Pn(u,v) of the noise signal.
  • step 805 the next sub-region is extracted, and in step 806, it is determined whether the processing of all the sub-regions has ended. If it is not over, step 802 to step 806 are repeated.
  • the evaluation and improvement of the power spectrum related to the noise signal are sequentially implemented in step 804.
  • the same processing flow can be used to perform After scanning the entire area, after completing the list of sub-areas related to the noise signal, calculate the noise power spectrum Pn(u,v).
  • the wafer inspection apparatus includes a detection target image generation unit and a data processing unit, the detection target image generation unit is used to obtain a plurality of detection target regions or detection target images across wafer regions, and the data processing unit It is used for processing and determining the defect extraction of the detection target image; its data processing unit includes a preprocessing module and a noise reduction module, and the preprocessing module includes a noise reduction area determination submodule and a noise signal extraction submodule.
  • the power spectrum estimation stator module is used to estimate the full signal power spectrum P according to the detection target image i i s+n(u,v), the noise power spectrum estimation stator module obtains the noise power spectrum P Ni n(u,v) of the detection target image of the Ni parts, making it the noise power spectrum P i n (u,v);
  • the difference signal power spectrum estimation stator module estimates the difference signal power spectrum P i s( according to the noise power spectrum P i n(u,v) and the full signal power spectrum P i s+n(u,v) u,v);
  • the noise signal extraction submodule obtains the average value of the range of i
  • the power spectrum improvement calculation of the noise signal is performed in the plurality of detection target regions or across the wafer, so that the accuracy can be improved.
  • FIG. 9 is a schematic diagram of an example of image processing using morphology.
  • the distinction between the small element signals 906 to 909 that are considered to be noise signals and the detection target image element signals 902 to 905 to be extracted is a comparison. Easy, but distinguishing meaningful defects from the element signal requires complex processing.
  • the wafer inspection apparatus of the present invention further includes a defect extraction module and a restoration module. That is, after the detection target image is obtained, the defect extraction module extracts the detection target image elements in the detection target image based on the morphological image processing technology, and removes the noise image of the detection target image. If the idea of the present invention is used to process only the noise image of the detection target image, the selection of the filter is relatively simple, and the accuracy of the filtering effect is improved. Moreover, after the noise image of the detection target image is obtained, the restoration module adds the detection target image elements in the detection target image to the detection target image after the noise information in the detection target image is removed, and it can be directly formed The detection target image after noise is removed.
  • FIG. 9 is a schematic diagram of an example of image processing using morphology.
  • the reference numeral 900 in FIG. 9 details an example of filtering the noise signal composed of fine difference defects and extracting an image of a predetermined size or more.
  • Numeral 901 is an input image, which includes element signals 906 to 909 of the fine difference defect of the noise signal and the detection target image element signals 902 to 905 to be extracted.
  • Reference numeral 910 denotes image processing composed of a combination of morphological calculations, that is, morphological processing.
  • the structural elements used in the morphological image processing with reference number 910 are shown by reference number 911.
  • the output image 920 obtained by the morphological processing shown in this example is an image in which the fine image elements of the noise signal have been removed.
  • the morphological processing used in this example can use the Erosion calculus method.
  • the shape and size of the structural element 911 can be selected according to the characteristics of the image structural element of the fine difference defect of the noise signal to be removed.
  • the pattern structure elements 922 to 925 obtained when the Erosion calculation is used are deformed according to the input relationship between the pattern structure elements 902 to 905. Since the deformation based on the Erosion calculation is different, in order to obtain the same pattern elements as those of 902 to 905, further pattern calculations are required.
  • the filtering of the noise signal based on the morphological processing shown here is an example, and it is a technique that can be applied to a variety of complicated processing such as pattern structure extraction and shape extraction.
  • Fig. 10 shows an example of parameterized morphological structural elements.
  • an example of parameterization of the structural elements of a circle and a rectangle is shown in 1000, and an example of parameterization of a circle is shown in 1010.
  • the circle 1011 is a model with the radius R1012 as a parameter.
  • the radius R is set to 1
  • the instantiated example 1013 is the circle shown by 1014.
  • the illustrated example 1015 is a circle shown by 1016.
  • an example of a structural element that parameterizes a rectangle is shown in 1020.
  • the detection accuracy changes through the selection of the morphological processing program and the selection of the detection accuracy parameters. Therefore, the best choice needs to be made.
  • the evaluation of detection accuracy is realized by a computer program, the selection of parameters and processing programs can be changed, so that the best program and parameter selection can be explored.
  • the best choice can be made through human judgment.

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