WO1999001985A1 - 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 - Google Patents

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 Download PDF

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Publication number
WO1999001985A1
WO1999001985A1 PCT/US1998/013699 US9813699W WO9901985A1 WO 1999001985 A1 WO1999001985 A1 WO 1999001985A1 US 9813699 W US9813699 W US 9813699W WO 9901985 A1 WO9901985 A1 WO 9901985A1
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Prior art keywords
image
decomposition
generate
lowpass
bandpass
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PCT/US1998/013699
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English (en)
Inventor
Shih-Jong J. Lee
Larry A. Nelson
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Neopath, Inc.
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Application filed by Neopath, Inc. filed Critical Neopath, Inc.
Priority to AU82805/98A priority Critical patent/AU8280598A/en
Publication of WO1999001985A1 publication Critical patent/WO1999001985A1/fr

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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
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/956—Inspecting patterns on the surface of objects
    • 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • 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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • 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
    • G06T2207/30121—CRT, LCD or plasma display
    • 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 apparatus for image decomposition and synthesis and more particularly to a method and apparatus for the inspection of semiconductors and liquid crystal displays using multidimensional image decomposition and synthesis.
  • the substrate receives a number of inspections and material characterizations. These inspections and characterizations attempt to detect a variety of defects including process defects, scratches and contamination defects, etc. These inspections and material characterizations also attempt to detect significant process variations. Defects and the effects of significant process variations occur on a variety of scales including both the macro level scale and micro level scale. Defects also occur in different materials and on different layers on the wafer. These defects and significant process variations, of different scales, whether macro or micro, of different materials and layers, require accurate and repeatable detection and classification. In some instances this detection may require three dimensional multi-modality imaging.
  • the invention provides a semiconductor wafer or LCD inspection apparatus comprising a means for imaging the semiconductor wafer or LCD active matrix panel; an automated stage positioned to move the wafer under the means for imaging the semiconductor substrate; a substrate storage, loading and unloading means for transporting the semiconductor wafer or LCD to and from the automated stage; and a means for image processing to scan the semiconductor wafer or LCD and to perform an image decomposition, processing and synthesis for automatic inspection of the semiconductor wafer or LCD.
  • the means for image processing further comprises a means for decomposing the raw image data into a set of partial information channels, each channel reflecting predetermined aspects of the image.
  • the means for image processing further comprises a means for synthesizing the image to recover the original image . In one embodiment of the invention, the means for image processing further comprises a means for constructing an improved image.
  • the means for imaging the semiconductor wafer or LCD further comprises an automated microscope.
  • the means for imaging the semiconductor wafer or LCD further comprises a light source and a sensing device to image the area of the wafer under inspection.
  • the means for image processing further comprises a host computer.
  • the means for image processing further comprises multiple controllers.
  • the means for image processing further comprises a high speed image processing unit .
  • the invention further provides a method for semiconductor wafer inspection comprising the steps of : obtaining an image of the semiconductor substrate; performing an image decomposition to generate a decomposition data set of spatial frequency and orientation bandpass component images that represent the decomposition of the image; performing a multiple channel feature detection on the decomposition data set; performing an image synthesis on the decomposition data set to generate a synthesized image; and detecting defects in the synthesized image.
  • the step of performing an image decomposition to generate a decomposition data set of spatial frequency and orientation bandpass component images further comprises the steps of: performing a linear lowpass filtering of the image to generate a lowpass image; performing a sub-sampling on the lowpass image to generate a coarser image; and repeating the above steps a predetermined number of times to generate a predetermined number of coarser images.
  • the invention further provides the steps of : expanding the coarser image to form an interpolated image; and adding the interpolated image to the next finer resolution image to generate a bandpass image.
  • the step of expanding the coarser image further comprises the step of generating a point replicated image.
  • the step of performing a linear lowpass filtering of the image to generate a lowpass image further comprises the step of performing an isotropic lowpass decomposition.
  • the step of performing a linear lowpass filtering of the image further comprises forming lowpass filters at different directions.
  • the step of performing an image synthesis on the decomposition data set to generate a synthesized image further comprises the steps of : expanding the coarser image to form an interpolated image; and adding the interpolated image to a bandpass image to generate a next finer resolution image.
  • the invention further provides the step of performing a multiband contrast enhancement step on the lowpass and bandpass images.
  • the invention further provides the step of performing a multiband noise coring to selectively remove high frequency components from the bandpass image .
  • the invention further provides the step of performing an edge preserving averaging on the lowpass image.
  • the invention further provides a method of image decomposition to generate a decomposition data set of spatial frequency and orientation bandpass component images further comprising the steps of : performing a linear lowpass filtering of an image to generate a lowpass image; performing a sub-sampling on the lowpass image to generate a coarser image; and repeating the above steps a predetermined number of times to generate a predetermined number of coarser images.
  • the invention further comprises the steps of: expanding the coarser image to form an interpolated image; and adding the interpolated image to the next finer resolution image to generate a bandpass image.
  • the step of expanding the coarser image further comprises the step of generating a point replicated image.
  • the step of performing a linear lowpass filtering of the image to generate a lowpass image further comprises the step of performing an isotropic lowpass decomposition.
  • the step of performing a linear lowpass filtering of the image further comprises forming lowpass filters at different directions.
  • the step of performing an image synthesis on the decomposition data set to generate a synthesized image in one embodiment of the invention, the step of performing an image synthesis on the decomposition data set to generate a synthesized image.
  • the step of performing an image synthesis on the decomposition data set to generate a synthesized image further comprises the steps of : expanding the coarser image to form an interpolated image; and adding the interpolated image to the bandpass image to generate a next finer resolution image.
  • Figures 1A and IB show the method and apparatus of the invention for semiconductor wafer and LCD inspection using multidimensional image decomposition and synthesis .
  • Figure 2 shows the method of the invention to generate the lowpass and bandpass decomposition of raw image data .
  • Figure 3 shows the spatial frequency/morphological pattern spectra plot of the decompositions in three-dimensions.
  • Figure 4 shows the lowpass filters at different directions .
  • Figure 5 shows the directional decompositions derived from the lowpass decomposition.
  • Figure 6 shows the coarse to fine synthesis process of the invention.
  • Figure 7 shows the method of the invention to perform multiband local contrast enhancement.
  • FIG 8 which shows the method of the invention to perform multiband noise coring.
  • Figure 9 shows a representation of a semiconductor wafer substrate having replicated die patterns containing detailed image structure in the presence of a defect having scale and directional characteristics caused by a process defect.
  • DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT Refer now to Figures 1A and IB which show the method and apparatus of the invention for semiconductor wafer or LCD inspection using multidimensional image decomposition and synthesis.
  • the automatic wafer inspection system 10 comprises a wafer storage and loading module 18 and a wafer storage and unloading module 20, where both can be combined into one module, to transport a wafer 15 to and from an automated stage 16.
  • the automated stage 16 moves the wafer 15 under a microscope 17 for scanning.
  • the invention further provides an illumination source 14 and a sensing device 12 to image the area of the wafer 15 under inspection.
  • the illumination source 14 may further comprise a visible light source, an ultra-violet light source, an infrared light source or an electron beam source.
  • the sensing device 12 is appropriately configured to receive and image from the illumination source 14.
  • a processing unit 22 comprises a host computer 21, multiple controllers 23, a high speed image processing unit 25 and a power supply 27.
  • the sensing device sends a raw image 24 to processing unit 22 through connection 19.
  • the processing unit 22 controls the scanning of the wafer 15 and performs the image decomposition and synthesis processing for automatic inspection.
  • the host computer 21 operates to facilitate the information extraction process by implementing the decomposition and synthesis method in software.
  • the invention decomposes the raw image data 24 into a set of partial information channels, each channel reflects certain aspects, a modality, of the information.
  • the partial information can thus be processed independently or cooperatively and synthesized to recover the original image as recovered image 24A or to construct an improved image 33. In this manner, useful information can be retained and irrelevant information can be rejected effectively.
  • FIG. IB shows the method of the invention to perform automatic wafer defect classification.
  • the method starts with the acquisition of a wafer image 24 to provide the raw image data 24 of the wafer 15.
  • the process then performs an image decomposition in step 26 on the raw image data 24 to generate a decomposition data set of spatial frequency and orientation bandpass component images that represent the decomposition of the raw image data 24.
  • the method of the invention then performs multiple channel feature detection and enhancement on the decomposition data set in step 28.
  • the invention then performs image synthesis in step 30 on information from the multiple channel feature detection and enhancement of the decomposition data set 35.
  • the invention performs final defect detection.
  • Image defects 37 are provided in step 34 from the final defect detection step 32.
  • the representation used by the invention retains spatial localization while maintaining the localization in the spatial-frequency spectrum pattern and spatial orientation domains.
  • the invention decomposes the raw image data 24 into a set of spatial frequency and orientation bandpass component images, represented by decomposition data set 35.
  • Individual samples of a component image represent image pattern information that is appropriately localized, while the bandpassed image as a whole represents information about a particular fineness of detail or scale.
  • Particular semiconductor fabrication processes have characteristic defects. For example, spin depositions in semiconductor processes may have streaking. Or processes that are sensitive to contamination by particulates may contain small scale imperfections in the presence of larger scale structure and small features within that structure.
  • images can be represented efficiently to allow access to the structural characteristics of the defects which are characteristic to the image processing problems and their associated processing structures.
  • image processors can have feature calculations that have higher signal to noise and require less processing time when the image data is decomposed from a conventional representation and recomposed into structures that are appropriate to the defects which need to be detected.
  • Figure 9 shows a representation of a semiconductor wafer substrate having replicated die patterns containing detailed image structure in the presence of a defect having scale and directional characteristics caused by a process defect.
  • the image of the wafer is decomposed directionally and spectrally into a family of macro images.
  • the defect 912 is a streak viewed in the context of circuit detail and large scale wafer structure.
  • images 901 through 904 show directional filters that if applied to the image, produce filtered images 905 through 908, respectively.
  • the defect properties are easily separable using conventional image processing techniques.
  • the same input macro image is alternatively spectrally decomposed into low pass image 909, bandpass image 910, and high pass image 911.
  • the large nature of the defect in contrast to the small scale features of the wafer design are apparent in a large object shown in image 909.
  • the signal strength represents the strength of the features of the defect .
  • Defect feature values come through intact for the most part after the orientation filtering process.
  • Features calculated for the remaining portions of the image are reduced in value. This increases the ability of the invention to detect defects in the presence of other intended structures. Certain characteristics of the basic wafer image are also apparent.
  • the basic image information has reduced contrast in the residual image compared to the residual contrast of the defect shown in filtered images 909, 905 or alternatively the defect may be rejected by the filtering the result of which is shown in image 910, 911, 900, 907 and 908. In both cases a separation has occurred.
  • the invention separates defects from other image information. This separation reduces the difficulty of image processing to detect a defect by selection of the decomposition, filtering, and synthesis operations on the input image information. The combination of the features of these images further strengthens feature strength for defect detection.
  • the invention provides for the organization of image information to facilitate rapid and enhanced image processing algorithms. Following the arrangement of image data into a set of partial information channels, each channel of which represents predetermined aspects of the raw image data, the image processing apparatus acts to detect and classify defects on the imaged substrate.
  • Defects are detected by conventional application of morphological operations to segment objects, and to characterize those objects by calculation of features selected to differentiate between objects and artifacts.
  • the calculated feature values for each object are the input to conventional classifiers such as Fisher Tree classifiers or neural network classifiers .
  • the output of the classifier may serve to simply detect a defect or alternatively to classify that defect within a set of possibilities.
  • the construction and operation of these classifiers may be influenced by the features available from decomposed images which would otherwise not be available from the original image, or be so difficult and time consuming to compute as to be impractical to obtain from the original image.
  • the invention views objects of different types at different scales but at the same time preserves the spatial relations between objects so that object characteristics can be related easily between decomposed images .
  • An object represented by a combination of multiscale data can be analyzed by selectively correlating these data.
  • the multiscale representation provides a unique way to associate local information with global context by correlation between data of different scales. Thus, local features can be enhanced differently depending upon their global context encoded in the coarse representations .
  • the decomposition and synthesis image representation and analysis method of the invention are related to the mask free primitive characterization modules of the US patent application entitled “Method and Apparatus for Mask free Semiconductor Wafer Inspection” by Lee, et al . which is incorporated in its entirety by reference thereto . While the invention is described with image data in three dimensions, those skilled in the art will recognize that the methods of the invention can be equally applicable to higher dimensional image data or two dimensional data as well.
  • Figure 2 which shows the method of the invention to the image decomposition step 26 described in Figure IB. In step 26 the invention generates lowpass and bandpass decomposition of image 24.
  • the image decomposition data set 35 are data structures designed to isolate wafer image 24 features at different scales and orientations and to support efficient scaled neighborhood operations through reduced image representation.
  • the image decomposition data set 35 comprises a lowpass decomposition stage, a bandpass decomposition stage, and a directional decomposition stage.
  • the lowpass decomposition consists of a sequence of copies of the original image in which both sample density and resolution are decreased in regular or irregular steps. These reduced resolution levels of the decomposition are themselves obtained through a highly efficient iterative algorithm.
  • the bottom, or zero-th level of the lowpass decomposition, L 0 , decomposition data image 36 is equal to the original image, the raw image data 24.
  • This decomposition data image 36 is linearly or morphologically lowpass filtered 53 and down-sampled 55, in one example embodiment by a factor of two in each dimension, to obtain the next decomposition level, L x , decomposition data image 38.
  • L is then filtered 57 in the same way and down-sampled 59 to obtain L 2 , decomposition data image 40. Further repetitions of the filter 61 and down-sample 63 steps generate the remaining lowpass decomposition levels 42.
  • L 0 I;
  • the lowpass filter can be a linear convolution filter or nonlinear morphological filters such as the well known dilation, erosion, opening, closing, and other operations.
  • the lowpass decomposition is equivalent to filtering the original image with a set of equivalent linear or nonlinear neighborhood functions. The equivalent functions increase in width in proportion to down-sample factor with each level.
  • the equivalent filters act as lowpass filters with the band limit reduced correspondingly by one octave with each level.
  • the linear lowpass decomposition using Gaussian convolution is equivalent to the well known Gaussian pyramid data structure.
  • the bandpass decomposition, decomposition data images 44, 46 and 48 can be generated by subtracting each lowpass decomposition level from the next lower level in the decomposition using subtractors 37, 39 and 41. Because these levels differ in their sample density, the invention interpolates, using expansions 45, 47 and 49, new sample values between those in a given level before that level is subtracted from the next lower level.
  • the interpolation can be achieved by point replication followed by a linear lowpass filtering.
  • the levels of the bandpass decomposition, B, decomposition data images 44, 46 and 48 can thus be specified in terms of the lowpass decomposition levels as follows :
  • B 1 A - L(EXP(L 1+1 ) ) , where L(.) is a multidimensional linear lowpass filter and EXP ( . ) is a multidimensional data replication.
  • Figure 3 shows the spatial frequency/morphological pattern spectra plot 59 of the decompositions in three-dimensions.
  • the bandpass decomposition step 26 decomposes the image into different bands in spatial frequency or morphological pattern scale.
  • the linear bandpass images, decomposition data images 44, 46, 48, 50, as with the Fourier transform, represent pattern components that are restricted in the spatial-frequency domain. But unlike the Fourier transform, the images are also restricted to local volumes in the spatial domain.
  • the filter F is the normalized additive combinations of F 1 , F 2 , ... F 8 .
  • different directional components can be generated along with an isotropic lowpass image which is an average of all the directional components.
  • a lowpass decomposition in direction p at level p i, L ⁇ is generated by
  • L l 7 can be derived from L f 's:
  • the filter F is the normalized additive combinations of F 1 , F 2 , ... F 8 representing lowpass filters 54, 56, 58, 60, 62, 64, 66, 68 and 70, at different directions.
  • the isotropic lowpass image can then be used to generate the next level of decompositions including different directional components at that level.
  • the directional bandpass images can be generated at each level in the same way the isotropic bandpass images are generated.
  • B l is generated by subtracting L(EXP(L t P +1 ))
  • image transformation or wavelet basis functions in image coding applications can be used as the generating kernels for the directional components of the invention.
  • image coding applications include the Harr transform, the Quadrature mirror filters and others.
  • FIG. 5 shows the directional decompositions derived from the lowpass decomposition ⁇ .
  • the lowpass decomposition image 110 is low pass filtered by filters 72, 74, 76, 78, 80, 82, 84 and 86 to generate the directional decompositions 90, 92, 94, 96, 98, 102, 104 and 106.
  • the combination of the directional decompositions generate the bandpass decomposition 108 and the low pass decomposition 88.
  • the bandpass and directional decomposition is a complete image representation; the step used to construct the decomposition may be reversed to synthesize the original image exactly.
  • B 1 can be recovered fully by averaging p over the directional decomposition 5, 's decomposition data images 120, 122 and 124.
  • L 1+1 , data images 114, 116 and 118, are interpolated, using expansions 130, 132 and 138, and added to B 1 using summations 126, 125, and 136. This procedure can be repeated to recover L ⁇ , L 1 _ 2 , and so on until the original image is recovered.
  • An image can be sufficiently represented by its bandpass decomposition B 0 through B N-1 and the top lowpass image L N .
  • bandpass decomposition may place the data in a more compact form so that the data can be stored, processed and transmitted more efficiently.
  • the bandpass decomposition has a little more sample elements
  • the values of these samples tend to be near zero, and therefore can be stored with a small number of bits.
  • Further data compression can be obtained through quantitization: the number of distinct values taken by samples is reduced by binning the existing values. This results in some degradation when the image is recovered, but if quantitization bins are carefully chosen, the degradation can be reduced to an acceptable level.
  • Another nice feature of the bandpass decomposition is that the coarse resolution images, image data 118 and 116 for examples, are recovered first in the synthesis process. This invention provides a controlled synthesis scheme. Volumes of interest can be identified first at coarse resolution and are synthesized subsequently at finer resolution as required in an analysis process.
  • the data outside the volumes of interest are sufficiently represented by its coarse resolution data and need not be synthesized further. Thus, only the data in the volumes of interest need to be progressively transmitted up to the finest resolution. In this way, a controlled resolution strategy is set up which uses the lowest resolution sufficient for the application and restricts processing of highest resolution image to volumes of interest.
  • This methodology is in line with how human perception works. Humans view the world through sequences of fixations. They gather information selectively and the vast majority of scene information is ignored.
  • the method and apparatus of the invention performs feature detection in step 28.
  • Bandpass and directional decompositions based on different filtering schemes can isolate critical components of the wafer circuit patterns at different scales and orientations so that they are more accessible for analysis.
  • the bandpass decomposition components include a highpass image and several bandpass images derived from taking the difference of images filtered by different sized equivalent Gaussian filters. These bands highlight image edge information at different scales.
  • differences of Gaussian filters are approximations to the well known Laplacian of Gaussian filters.
  • the zero-crossing points of the Gaussian based bandpass decomposition correspond to the edges of different scales detected by the Laplacian of Gaussian edge detectors.
  • a temporal bandpass filter is applied prior to an image decomposition, a Gaussian decomposition can be used for motion detection. In motion detection, each decomposition band represents motions of different velocities from objects of different sizes.
  • the decomposition and synthesis image representation and analysis disclosed in this invention can support the mask free primitive characterization modules disclosed in copending United States patent application entitled "Method and Apparatus for Mask free Semiconductor Wafer Inspection” by Lee, et al . which is incorporated by reference hereto.
  • nonlinear filters such as morphological dilation, erosion, opening, and closing, etc. can be used for image decompositions.
  • the bandpass decompositions based on different morphologic filters can detect wafer defect patterns of different kinds. Dilation decompositions detect dark edges of different scales; erosion decomposition detects bright edges of multiple scales; opening decomposition detects bright corners of multiple scales; and closing decomposition detects dark corners of multiple scales.
  • the directional decompositions further isolate the specific image features at different spatial directions .
  • a modified image can be constructed by the synthesis of the modified bandpass and directional components.
  • the modification can be performed to enhance features at desirable bands and directions and to reject undesirable artifacts or to exclude certain undesirable information.
  • highpass filtered images of different scales can be generated by selectively excluding low frequency components of the images. This can be accomplished by a selective synthesis strategy. Highpass images of the smallest scale can be generated from B 0 :
  • H' B 0 + L(EXP(B X ) ) , which can be considered as an image recovery process starting from Bi rather than h x . Since and
  • L 2 D(F(D(F(I) )))
  • B- . L- . - L (EXP (D (F (D (F (I) ) ) ) ) )
  • a lowpassed image can be synthesized by excluding B 0 band.
  • any combinations of L N and Bi's can be used in the selective synthesis process.
  • the selections can be down to different directional components to synthesize only the features at the desirable orientations. This provides a general platform to derive and present information of different emphases from a given wafer image with certain circuit pattern features .
  • bandpass channels can be modified prior to the synthesis. This provides a richer and more powerful platform for wafer defect feature detection, enhancement, and regular circuit pattern and variation artifact rejection.
  • the method and apparatus of the invention performs an independent enhancement then synthesis.
  • the modification can be done independently at each band before synthesis is applied. In this way, information can be modified differently at different scales .
  • the human visual system processes spatial-frequency information independently of the information in adjacent bands.
  • the independent enhancement then synthesis steps can be performed by multiband local contrast enhancement, multiband noise coring, or edge preserved averaging.
  • FIG. 7 shows a method of the invention to perform multiband local contrast enhancement .
  • Contrast enhancement can be performed independently at each scale by correlating between the lowpass image, lowpass decomposition image data 142, 144 and 146, and the bandpass image, bandpass decomposition image data 148, 150 and 152, at each decomposition level.
  • the enhancement in the bandpass features is adaptively adjusted based on the lowpass images.
  • the adjustment weights 154, 156 and 158 can be encoded as lookup tables with bandpass and lowpass images as the input and the enhanced image as the output, contrast enhanced bandpass decomposition image data 160, 162 and 164.
  • the contrast enhanced bandpass decomposition can be used to synthesize a reconstructed contrast enhanced image 168.
  • FIG 8 shows the method of the invention to perform multiband noise coring.
  • the noise coring operation of the invention selectively removes high frequency components from an image. Noise is suppressed by setting the bandpass or highpass image data value to zero when the noise value is below a predefined threshold. The predefined threshold is adjusted based on the decomposed image band.
  • the noise suppressed bandpass images 188, 190 and 192 are then added with the lowpass image 170 as described in Figure 6 to construct a noise reduced image 194.
  • too much low-amplitude edge information may be removed along with the true noise, which causes edge transitions to look blurred and ragged.
  • the invention decomposes the images and performs noise coring at each band independently and then synthesizes the image following the method shown in Figure 8.
  • the noise reduced image 194 is analogous to the reconstructed original image 112.
  • the invention removes less image information while still reducing the equivalent amount of noise.
  • the invention also performs edge preserved averaging.
  • a multiresolution representation of the original image can be rather useful in several applications.
  • the lowpass decomposition can be used for this purpose, the edges are blurred at lower resolution because of the lowpass filtering effect.
  • the invention constructs a multiresolution representation of the original image 24 based on an edge preserved blurring method. To accomplish this, the invention uses a directional decomposition method. In the directional decomposition, a p directional lowpass decomposition set L l at level p i and a directional bandpass decomposition set B l _ 1
  • the edge preserved lowpass image at level i, L ' x can then be generated by the following rule:
  • the method of the invention tends to preserve edges when averaging is performed.
  • the invention also performs between band correlation then synthesis .
  • bandpass decomposition provides both spatial and spatial frequency localization, spatial neighborhood operators can be applied to different spatial frequency bands for detection and enhancement.
  • An even more useful method is to perform operations between bandpass channels of the same image to detect and strengthen feature characteristics of a process .
  • different embodiments of the invention provide a number of between band correlations then synthesis methods.
  • the correlation strategies include a weakening strategy and an enhancement strategy.
  • Information can be removed from a wafer image by a correlation between different bandpass decomposition bands.
  • a weakening strategy constrains the data intensity by taking the minimum with its adjacent bands.
  • the adjacent bands can be reprocessed by neighborhood operations such as morphological dilations before correlation.
  • B 1+1 to correlate B 1 the method of the invention expands B x+1 and dilates the result by an appropriate structuring element before performing the minimum operation with B x :
  • B' x MIN(B X , dilate (EXP (B 1+1 ) ) ) .
  • the correlation of B 1 can also be performed using the adjacent higher frequency bands B ⁇ as follows:
  • B' x MIN(B X/ dilate (D(B X . X ))).
  • the weakening operations can be applied iteratively from the coarse (fine) resolutions to the fine (coarse) resolutions.
  • Image features can be enhanced by band correlation in a similar fashion.
  • an enhancement strategy increases the data intensities by a constrained dilation operation. The constraint is imposed by correlating its dilated image with the data from its adjacent bands by the following rule:
  • B' x MAX(B X , MIN (dilate (B x ) , EXP (B 1+1 ) ) ) .
  • the enhancement strategy picks up the components in band B x+1 having intensity values higher than the intensity value in band B x .
  • the enhancement can be done by a maximum operation or by adding a fraction of the enhancement component to band B x .
  • the enhancement correlation can be performed using the adjacent higher frequency band B i _ 1 as well.
  • the enhancement operations can be applied iteratively from the coarse (fine) resolutions to the fine (coarse) resolutions.
  • weakening and enhancement operations can be applied iteratively before the image synthesis step 30 is performed to selectively detect and enhance desirable features and patterns in the image and suppress the undesirable information.
  • image synthesis step 30 is performed to selectively detect and enhance desirable features and patterns in the image and suppress the undesirable information.

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  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)

Abstract

La présente invention concerne un système d'inspection de plaquettes automatique (10) comprenant un module (18) de stockage et de chargement de plaquettes et un module (20) de stockage et de déchargement de plaquettes, lequel système transporte une plaquette (15) vers et depuis une platine automatisée (16) qui place la plaquette (15) sous un microscope (17) afin qu'elle soit examinée par balayage. Un capteur (12) forme l'image de la zone de la plaquette (15) qui est examinée. Une unité de traitement (22) comprenant un ordinateur hôte (21), plusieurs contrôleurs (23), une unité de traitement d'image à grande vitesse (25) et une alimentation (27) commande le balayage de la plaquette et effectue la décomposition, la synthèse et le traitement de l'image (fig.2) nécessaires à l'inspection automatique. Un ordinateur (21) décompose les données image brutes (26) en un ensemble de voies porteuses d'informations partielles en relations spatiales de commande (35). Chaque voie reflète des aspects prédéterminés des informations. Les informations partielles peuvent ainsi être traitées par des techniques de traitement d'image, de façon indépendante ou coopérative. Il est possible de synthétiser (fig.7) les images d'information partielle afin de récupérer l'image originale ou d'obtenir une image améliorée permettant l'inspection automatique. Le système acquiert une image (24), effectue une décomposition de l'image (26) afin de générer un ensemble de données de décomposition (35) des images composantes de fréquence spatiale et d'orientation passe-bande représentant la décomposition de l'image entrée (26), il effectue la détection et l'amélioration des caractéristiques des différentes voies sur l'ensemble de données de décomposition (28), et procède à la synthèse d'image (30) et à la détection des défauts (32).
PCT/US1998/013699 1997-07-03 1998-07-01 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 WO1999001985A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU82805/98A AU8280598A (en) 1997-07-03 1998-07-01 Method and apparatus for semiconductor wafer and lcd inspection using multidimensional image decomposition and synthesis

Applications Claiming Priority (2)

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US88811697A 1997-07-03 1997-07-03
US08/888,116 1997-07-03

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WO1999001985A1 true WO1999001985A1 (fr) 1999-01-14

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WO2008110159A2 (fr) * 2007-03-15 2008-09-18 Gp Solar Gmbh Procédé et dispositif permettant de déterminer une rupture dans une matière cristalline
WO2008152020A1 (fr) * 2007-06-12 2008-12-18 Icos Vision Systems Nv Procédé pour une inspection d'un substrat à semi-conducteur
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CN113075113A (zh) * 2014-12-09 2021-07-06 伯克利之光生命科技公司 微流体装置中微物体的自动检测和重新定位

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7835695B2 (en) 1999-08-06 2010-11-16 Qualcomm Incorporated Method and apparatus for determining the closed loop power control set point in a wireless packet data communication system
WO2008110159A2 (fr) * 2007-03-15 2008-09-18 Gp Solar Gmbh Procédé et dispositif permettant de déterminer une rupture dans une matière cristalline
WO2008110159A3 (fr) * 2007-03-15 2008-11-20 Gp Solar Gmbh Procédé et dispositif permettant de déterminer une rupture dans une matière cristalline
WO2008152020A1 (fr) * 2007-06-12 2008-12-18 Icos Vision Systems Nv Procédé pour une inspection d'un substrat à semi-conducteur
CN113075113A (zh) * 2014-12-09 2021-07-06 伯克利之光生命科技公司 微流体装置中微物体的自动检测和重新定位

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