WO2008110159A2 - Procédé et dispositif permettant de déterminer une rupture dans une matière cristalline - Google Patents

Procédé et dispositif permettant de déterminer une rupture dans une matière cristalline Download PDF

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Publication number
WO2008110159A2
WO2008110159A2 PCT/DE2008/000439 DE2008000439W WO2008110159A2 WO 2008110159 A2 WO2008110159 A2 WO 2008110159A2 DE 2008000439 W DE2008000439 W DE 2008000439W WO 2008110159 A2 WO2008110159 A2 WO 2008110159A2
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WO
WIPO (PCT)
Prior art keywords
image
spatial
transmitted light
fracture
frequency information
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PCT/DE2008/000439
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German (de)
English (en)
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WO2008110159A3 (fr
Inventor
Marc Hemsendorf
Christian Probst
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Gp Solar Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
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Publication date
Application filed by Gp Solar Gmbh filed Critical Gp Solar Gmbh
Priority to DE112008001330T priority Critical patent/DE112008001330A5/de
Publication of WO2008110159A2 publication Critical patent/WO2008110159A2/fr
Publication of WO2008110159A3 publication Critical patent/WO2008110159A3/fr

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Classifications

    • 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
    • G01N21/9505—Wafer internal defects, e.g. microcracks
    • 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
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/958—Inspecting transparent materials or objects, e.g. windscreens
    • 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

Definitions

  • the invention relates to a method and an apparatus for determining a fracture in crystalline material.
  • micro-cracks Breaks in wafers are usually in the form of micro-cracks in a partially extremely fine form. In photovoltaics, microcracks influence the efficiency of the future solar cell. In addition, microcracks cause the problem that wafers break easily in places of microcracks and microcracks therefore interfere with production.
  • micro-cracks can be so small that they can be hidden in the wafer without protruding to the surface. Such micro-cracks often have no surface structure and are therefore not recognizable on the surfaces of the wafer. Another difficulty is that microcracks and grain boundaries of polycrystalline semiconductor materials, especially of polycrystalline silicon, look so similar that they are generally difficult to distinguish.
  • the invention utilizes anomaly of the optical transmission of semiconductors
  • Microcracks Within the wafer, reflections occur at the break. A transmitted light image of a crack therefore appears dull black. In this way, even microcracks not visible from the outside can be detected.
  • the invention is based on the further consideration that by recording frequency information from the transmitted light image, characteristics of local brightness fluctuations in the transmitted light image are recorded in great detail
  • f (x, y) is the brightness of the transmitted light image
  • f (k x / ky) of the brightness corresponding to the spatial function f (x, y) of the brightness thus has high frequencies in the presence of a microcrack.
  • the brightness transition of grain boundaries in the transmitted light image is less sharp, causing the grain boundaries in the frequency spectrum of the brightness distribution to lower frequencies. From the presence of high-frequency components in the frequency spectrum of the brightness distribution can therefore be concluded on the presence of microcracks. By evaluating the frequency information can thus be distinguished between microcracks and grain structures in crystalline semiconductor material.
  • the spatial function f (x, y) of the brightness may be discrete, e.g. as individual measurements of pixels of a camera, or continuously, e.g. in the form of an algebraic function. It can be present directly in two dimensions or as the sum of several one-dimensional functions f (x).
  • the frequency information is obtained from the location function of the brightness and therefore dependent on the location function. Through them, the brightness change in the location function is characterized.
  • the frequency information may be available explicitly, for example in the form of coefficients, or determined implicitly by an evaluation of the location function, e.g. through frequency-selective steps, and also be used directly without concrete formation of an explicitly present function as such. For example, an application of a frequency filter and the further use of the frequency-filtered data is an evaluation of frequency information.
  • the method is applicable to any transmittable crystalline material, in particular semiconductor material, for which there is a transmission window in an imaging method in transmitted light in an arbitrary wavelength range.
  • the semiconductor material is silicon, while For example, monocrystalline silicon, in particular polycrystalline silicon.
  • the closing is based on a break from an evaluation of high-frequency components with elimination of low-frequency components. This can be done by filters or mathematical steps. Particularly effectively, low frequency components can be eliminated by forming a differential image, in particular by subtracting an image without a e.g. filtered high frequency information from a picture with the high frequency information.
  • the frequency information is manipulated, from the manipulated frequency information, a spatial image is reconstructed and calculated from the difference between an otherwise or not manipulated image, e.g. the transmitted light image, and reconstructed spatial image is closed to the presence of a break, possibly using further evaluation steps.
  • an otherwise or not manipulated image e.g. the transmitted light image
  • reconstructed spatial image is closed to the presence of a break, possibly using further evaluation steps.
  • the spatial image can be present as a data set which is evaluated. It is not necessary that the location space image is displayed as a visible image, for example on a screen.
  • the spatial image may, for example, be a data set of the brightness as a function of two spatial directions. It is also conceivable that the spatial image is present as a plurality of data sets, each of which reflect the brightness as a function of spatial direction.
  • the difference between the transmitted image and the reconstructed spatial image is determined on the basis of a difference image, in particular using methods of image processing.
  • the effects of manipulating the frequency information can be particularly sensitive in this way be reliably detected.
  • broadband interference signals in the difference image are suppressed on the basis of a morphological filter.
  • a morphological filter can be understood as a mathematical operation in which elements of a two-dimensional set of numbers are linked together by an operator. Depending on the filter, the choice of input elements may be different, for example, it may be the nearest neighbors of a point, and the operator may be different.
  • a well-known example is an erosion filter in which the minimum is determined from each immediate neighborhood, and then the corresponding neighborhood-defining point can be minimized.
  • a H-maxima filter is used as the morphological filter.
  • An H maxima filter filters local maxima by filtering out isolated extrema. This is particularly effective in fractures, as fractions are characterized by a connected sequence of local maxima.
  • H-maxima filters are described, for example, in P. Soille, Morphological Image Analysis, Springer Verlag, Berlin, 1999.
  • the manipulation takes place by suppressing a frequency range characteristic of the fracture.
  • uncharacteristic frequencies in particular all other frequencies, can be made to disappear during the subtraction, so that the frequency information of the fraction or a position information resulting therefrom can be recognized particularly well, especially in a difference image.
  • the suppression can be done by reducing or eliminating the frequency range characteristic of the rupture. Both technical and mathematical particularly simple suppression can be done by a low-pass filtering.
  • the low-pass filtering can be performed by systematically eliminating frequency information, for example, by systematically reducing a resolution of the frequency information.
  • the suppression is carried out by a targeted elimination of individual frequency coefficients from a discrete location-frequency transformation.
  • the suppression can be very targeted to one or more specific frequency ranges.
  • the method according to the invention is advantageously further developed in that the frequency information is determined by means of a wavelet transformation.
  • a method which is particularly suitable for data processing is available, in particular when using a discrete wavelet transformation.
  • a wavelet or its mathematical function is connected to the location function and thus the frequency information is obtained.
  • a Daubechies-2 wavelet it is advantageous to use a Daubechies-2 wavelet as a wavelet.
  • other wavelets are also suitable, in particular those which allow a good reconstruction, in particular a perfect reconstruction.
  • a further embodiment of the invention provides that with the aid of the frequency information, a spatial image is created, for example a difference image, from which the presence of a break is concluded with the aid of image processing.
  • a spatial image is created, for example a difference image, from which the presence of a break is concluded with the aid of image processing.
  • the presence of a fracture can be done easily and automatically by image processing.
  • Characteristic information about the fracture eg the size and position of a fracture, can be reliably determined when the spatial view is reduced with an environment filter.
  • the ambient filter can be, for example, a brightness or grayscale filter so that a fraction is completely or partially black and the rest of the environment is displayed white or vice versa. Data obtained in this way can be used particularly easily to characterize the fracture.
  • a determination of a position of the fracture is carried out from the spatial image with the aid of a cluster algorithm.
  • Small-scale noise which can not be eliminated, for example, despite an environmental filter, can be separated from an image of a fracture and the fracture can thus be reliably detected.
  • a determination of a size of the fraction is carried out from the spatial image with the aid of a cluster algorithm.
  • the method according to the invention can be integrated particularly well into a production of products, in particular of semiconductor wafers, if the transmitted light image of the material is obtained with the aid of a flashlamp.
  • the transmitted light image can be created during a movement of the material without, for example, a conveyor belt for transporting the material would have to be stopped.
  • the flashlamp should work in the wavelength-transmissive range for the material and may be, for example, a xenon lamp or an LED lamp.
  • the invention is directed to an apparatus for determining a fracture in crystalline material.
  • the device comprises an image recording device for recording a transmitted light image of the material and a processing means which is prepared to obtain frequency information of the spatial function from a spatial function of the brightness of the transmitted light image and the presence of a fracture based on the presence of fractions characteristic frequencies to output a control signal.
  • the control signal may be used to discard the broken product from a process.
  • the process means is intended to carry out one or more method steps as described above.
  • FIG. 1 shows a schematic representation of a device for detecting fractures in crystalline material
  • FIG. 3 shows an illustration of an etched solar cell wafer with a fracture
  • FIG. 5 shows a flow chart of a method for detecting a fracture.
  • FIG. 1 shows a device 2 for determining a fracture in crystalline material, in FIG. 1 a wafer 4 made of polycrystalline silicon.
  • the device 2 comprises a camera 6, which is signal-connected to a processing means 8.
  • the process means 8 is used to evaluate signals from the camera 6 and to control the method for determining the breakage.
  • the device 2 comprises a light source 10 and a diffusion plate 12 between the light source ⁇ 10 and the wafer 4.
  • a means of transport 14, the transport speeds is also speed the process means 8 is controlled, serves to move the wafer 4 on the light source 10 over during a manufacturing process.
  • Silicon as a semiconductor is opaque in the visible wavelength range. Only in the near infrared range, above about 950 nm, the transmission increases and remains at a high level until about 6 ⁇ m.
  • a transmission spectrum of silicon is shown in FIG. Plotted is the transmission in percent at an average wafer, as it is used for photovoltaics, depending on the wave number ⁇ .
  • the light source 10 For the light source 10, this means that it must have a relevant spectral component in the transmission range, advantageously in the near infrared.
  • its sensor expediently a CCD sensor, is also sufficiently sensitive in the transmission range, in particular in the near infrared.
  • he should have a sufficiently large spatial resolution.
  • the ideal solution was a spatial resolution of 50 ⁇ m to 100 ⁇ m.
  • a camera S For receiving a transmitted light image of the wafer 4, which has a size of 15 cm x 15 cm, a camera S with a resolution of about 4 megapixels is well suited.
  • a xenon flash lamp has proved to be advantageous. It has a spectrum of about 300 nm to 2000 nm and can be operated with a flash time of about 0.1 msec by short-time overload operation of a halogen lamp. Also possible is an LED lighting, for example, with main activity around 950 nm and a flash time of 1 msec by short-term overload operation of the LEDs.
  • the device 2 is particularly suitable for integration into production lines, since a recording in the movement can be made by this short exposure time. Insofar as this is not necessary, it is also possible to work with permanent lighting.
  • Particularly suitable - especially in the search for fractures in silicon - is a camera 6, which is sensitive to electromagnetic radiation in the wavelength range between 1000 nm and 2000 nm.
  • the high permeability of the silicon in this range makes it possible to dispense with an overload operation of the lighting.
  • a particularly high dynamics of the camera 6 is not required, since there have the lighting and the wafer at a transmittance of about 50% about the same brightness.
  • FIG. 3 shows, in a very schematic representation, a section of a transmitted light image 16 of the wafer 4 with a break 18.
  • grain boundaries 20 of the grain structures in the polycrystalline silicon of the wafer 4 are shown schematically.
  • the evaluation comprises the decomposition of the transmitted light image 16 with a 2D wavelet filter into its high-pass and low-pass components.
  • the 2D wavelet filter is realized by a two-dimensional discrete wavelet transformation with which the transmitted light image or its data record is decomposed stepwise into its respective high-pass and low-pass components.
  • the high-pass component is discarded and then a spatial image is reconstructed from the low-pass component.
  • the reconstructed spatial image is subtracted from the original transmitted light image 16.
  • the result is a difference image.
  • the gray values of the difference image are reduced so far with the aid of an ambient filter that the central area of the fracture is barely visible.
  • FIG. 4 shows an evaluation image 38. Image residues of the grain boundaries 20 are suppressed by the environmental filter, so that a central area of the fracture 18 remains visible by means of sharp spots. With the help of a cluster
  • Algorithm can now determine the position and size of the fraction 18. If a break 18 has been found with the aid of this method, a signal can be forwarded to a sorting installation in order to exclude the defective wafer 4 from further processing.
  • the transmitted light image 16 is first obtained and converted by the camera 6 into a data record 22, which is forwarded to the processing means 8.
  • the process means is equipped with a program which forms a matrix of n 0 xm 0 floating-point numbers, which the transmitted light image 16 reproduces.
  • the processing means 8 performs a filtering process in which a high-pass component 24 and a low-pass component 26 are created from the data record 22.
  • the high-pass fraction 24 is discarded and the low-pass portion 26 is again filtered by the same filtering algorithm, so that from the low-pass portion 26 is a high-pass fraction 28 and a low-pass • portion 30 is formed.
  • the decomposition of the transmitted light image 16 or its data record 22 in the filtering process is carried out with the aid of the two-dimensional wave
  • the two-dimensional wavelet transformation can first be carried out one-by-one line by line, and then, for example, by line-wise compilation into two-dimensionality come.
  • equation (1) on the left side of the equation is the discrete wavelet ⁇ j (k (t) and on the right side ⁇ stands for the continuous wavelet according to equation (Ia), which is discretized by the parenthesized relationship can be described here as:
  • f (t) is the continuous function to be examined, which depends on the independent variable t. It does not matter whether t stands for time or for a spatial coordinate x or y, ⁇ * indicates the shape of the wavelet, ie the shape of the window with which the function to be examined is scanned. It has proved particularly suitable for this method a wavelet whose meanwhile is known under the name "Daubechies-2-Wavelet".
  • ⁇ is the time of the window or the position of the window over the function to be examined f (t).
  • s is the size of the window, that is about the width of the wavelet.
  • the function f (t) to be scanned ie an arbitrary signal, for example the brightness distribution of an image line of the transmitted light image as a function of the spatial coordinate within this line, can again be represented by the discrete wavelet transformation as.
  • ⁇ (j, k) generally describes so-called filter components, into which the arbitrary signal f (t) can be decomposed.
  • the signal f (t) can also be called a scaling function:
  • This scaling function can be used to form a subband filter chain. Since a wavelet can also be described by its scaling function, the following applies:
  • the ⁇ j _i (k) describe the low-pass component 26 and the ⁇ j -i (k) the high-pass component 24 of the first stage, defined by the type of wavelet:
  • the signal f (t) to be examined can be reconstructed from the high-pass component 24 and the low-pass component 26.
  • the two portions 24, 26 each provide a frequency information of the location function, in the above example, the brightness distribution within a picture line. Sharp transitions 1 from light to dark or from dark to light deliver high frequencies and smoother transitions lower frequencies.
  • this frequency information can be manipulated, for example by the function f (t) to be examined being reconstructed solely from the low-pass component 26, and the high-pass component 24 being rejected, ie set to zero. This results in a manipulated spatial image in which a part of the frequency information is missing. With a suitable choice of wavelet
  • the transformation according to equation (7) can be carried out in further stages so that components 28, 30, for example of the second stage, are formed. Also a third, fourth or further stages are conceivable. In the example shown in FIG. 5, in which only the low-pass component 30 is used to reconstruct the manipulated spatial image, only the lowest quarter of the frequencies is used for the reconstruction. Depending on the choice of the shares 24-30 and the different stages, the frequency information can also be manipulated differently and almost any frequency coefficients can be used or hidden.
  • the low-pass component 30 For examining the wafer 4, it makes sense to use only the low-pass component 30. In this low-pass component, the absolute brightness of the transmitted light image 16 from the wafer 4 is quite well contained, so that a difference between the transmitted light image 16 and the manipulated spatial image 32 effectively suppresses the absolute brightness of the image. Since with the thickness of the wafer 4 also its transmission varies, the algorithm used should be independent of the absolute, local brightness. This is achieved by the use of the low-pass component 30. By discarding the high-pass components 24, 28, the manipulated spatial image 32 is reconstructed, omitting the frequency band significant for the fraction 18. struiert. Only the grain boundary structure of the wafer 4 or of its transmitted light image 16 is reconstructed. By forming a differential image 34, the grain boundary structure can be largely eliminated. The difference image 34 thus contains little or no grain boundary structure. What remains is some noise and - if available - the break 18.
  • the wavelet filter can be regarded as a generalized bandpass filter.
  • the filtered band should be characteristic of the signal emanating from the break 18.
  • the difference image 34 there are also remaining portions of broadband noise, e.g. Noise or impurities, etc. Typically, however, these components are scattered and / or weak. With a suitable morphological filter, these components can be suppressed and it is possible to create a cleaned spatial image 36, which differs from the evaluation image 38 shown in FIG. 4 by gray scale levels. With regard to transmitted light examinations of Wazer 4, a H-maxima transformation is particularly suitable as a morphological filter.
  • the process means 8 can perform the filtering process and the formation of the difference image as follows: First, for example, a square submatrix I 2 with the size n 2 xn 2 is formed, where n 2 is the largest potential of 2, which ⁇ min (n o , m o ) is.
  • the matrix I 2 determines the image section on which the analysis is performed.
  • the matrix I 2 is decomposed by means of the two-fold, two-dimensional , wavelet wavelet transformation on the basis of the Daubechies-2 wavelet.
  • the low-pass component 30 from this Decomposition is saved.
  • a matrix A 2 with the edge length n 2 xn 2 is reconstructed with the aid of the discrete wavelet transformation, eg according to equation 6.
  • the spatial image 36 can be further processed, for example by being digitized with a suitable threshold, ie only completely white or black pixels are present.
  • a suitable threshold ie only completely white or black pixels are present.
  • Such an evaluation image 38 is shown in FIG.
  • An image of the wafer 4 without break 18 is then completely black or white, depending on the digitization.
  • All white or black pixels of the fraction 18 in the evaluation image 38 can now be combined into clusters, so that each fraction 18 is represented by a cluster.
  • the clusters can now be evaluated for size and location, as indicated schematically in FIG. 5 by the step 40 of cluster formation and cluster evaluation. As a result, the number of breaks 18 as well as their size and location will be present.
  • each decomposition into a wavelet basis ⁇ (j, k) or portions 24-30 represents a frequency band for the image signal f (t).
  • the band region of the crystal structure changes.
  • the burst signal in the frequency domain is particularly broad band, it is always possible to scale to a band range suitable for separating the grain boundary structure from the signature of the fraction 18.
  • the process offers some advantages. First, only the Transmission used. It can therefore satisfy a single image capture. It is not necessary to record both sides of the wafer 4. Even microcracks with no surface structure can be detected. In addition, when using a sufficiently strong flashlight source with a spectral approach in the infrared, a wafer 4 can be imaged in the movement and thus tested. In practice this means a particularly simple integration into an existing production line.
  • the method is largely independent of the surface structure of the wafer. For example, raw and etched wafers 4 can be tested. Furthermore, the grain structure of polycrystalline silicon is masked out by a suitable filter and the difference formation. The method can therefore be applied to both monocrystalline and polycrystalline silicon. In addition, the method is largely independent of the thickness of the wafer 4. It operates locally and therefore also tolerates variations in thickness within the wafer 4.
  • Short term Fourier transformation can also determine the location of the break 18 in the wafer 4.
  • the Fourier transformation can be used more generally and the frequency spectrum of the transmitted light image 16 can be examined for frequencies characteristic of the break 18. If these are present, the wafer 4 can be eliminated, for example, from the further production process without further investigation.

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Abstract

La présente invention concerne un procédé permettant de déterminer une rupture (18) dans une matière cristalline. Selon cette invention, une fonction de lieu de la luminosité d'une image transparente (16) est obtenue à partir d'une image transparente (16) de la matière et une information de fréquence de la fonction de lieu est obtenue à partir de celle-ci. On déduit ensuite la présence d'une rupture (18) si des fréquences caractéristiques de ruptures (18) sont présentes. Il est ainsi possible de détecter dans une plaquette des ruptures (18) qui ne sont pas visibles de l'extérieur et de différencier de manière fiable les joints des grains (20) des ruptures (18).
PCT/DE2008/000439 2007-03-15 2008-03-15 Procédé et dispositif permettant de déterminer une rupture dans une matière cristalline WO2008110159A2 (fr)

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DE112008001330T DE112008001330A5 (de) 2007-03-15 2008-03-15 Verfahren und Vorrichtung zum Bestimmen eines Bruchs in kristallinem Material

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DE102007012525 2007-03-15
DE102007012525.0 2007-03-15

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WO2008110159A3 WO2008110159A3 (fr) 2008-11-20

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Cited By (1)

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Publication number Priority date Publication date Assignee Title
CN108878307A (zh) * 2017-05-11 2018-11-23 北京北方华创微电子装备有限公司 晶片检测系统和晶片检测方法

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EP0309758A2 (fr) * 1987-09-23 1989-04-05 Isotopen-Technik Dr. Sauerwein Gmbh Procédé et dispositif pour constater et évaluer des fissures superficielles sur des pièces à usiner
WO1999001985A1 (fr) * 1997-07-03 1999-01-14 Neopath, Inc. Procede et appareil destines a l'inspection de plaquettes a semiconducteurs et de dispositifs afficheurs a cristaux liquides et faisant appel a la decomposition et a la synthese d'images multidimensionnelles
WO2002040970A1 (fr) * 2000-11-15 2002-05-23 Real Time Metrology, Inc. Procédé et dispositif optique d'examen d'objets plans de grande superficie
US20060278831A1 (en) * 2005-06-14 2006-12-14 Mitsubishi Denki Kabushiki Kaisha Infrared inspection apparatus, infrared inspecting method and manufacturing method of semiconductor wafer
EP1801569A2 (fr) * 2005-12-23 2007-06-27 Basler Aktiengesellschaft Procédé et dispositif destinés à la reconnaissance de fissures dans des tranches de silicium

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Publication number Priority date Publication date Assignee Title
EP0309758A2 (fr) * 1987-09-23 1989-04-05 Isotopen-Technik Dr. Sauerwein Gmbh Procédé et dispositif pour constater et évaluer des fissures superficielles sur des pièces à usiner
WO1999001985A1 (fr) * 1997-07-03 1999-01-14 Neopath, Inc. Procede et appareil destines a l'inspection de plaquettes a semiconducteurs et de dispositifs afficheurs a cristaux liquides et faisant appel a la decomposition et a la synthese d'images multidimensionnelles
WO2002040970A1 (fr) * 2000-11-15 2002-05-23 Real Time Metrology, Inc. Procédé et dispositif optique d'examen d'objets plans de grande superficie
US20060278831A1 (en) * 2005-06-14 2006-12-14 Mitsubishi Denki Kabushiki Kaisha Infrared inspection apparatus, infrared inspecting method and manufacturing method of semiconductor wafer
EP1801569A2 (fr) * 2005-12-23 2007-06-27 Basler Aktiengesellschaft Procédé et dispositif destinés à la reconnaissance de fissures dans des tranches de silicium

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Title
ZIKUAN CHEN ET AL: "Subband correlation of Daubechies wavelet representations" OPTICAL ENGINEERING, SOC. OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS. BELLINGHAM, Bd. 40, Nr. 3, 1. März 2001 (2001-03-01), Seiten 362-371, XP002458776 ISSN: 0091-3286 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108878307A (zh) * 2017-05-11 2018-11-23 北京北方华创微电子装备有限公司 晶片检测系统和晶片检测方法

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