Crystal distortions modify the propagation of X-rays in single crystal materials, and X-ray topography can be used to record these modiﬁcations on a ﬁlm thus providing images of the distributions and nature of defects, dislocations, strains, precipitates, etc. in semiconductors. Small variations of contrast, which often need to be analysed can be rendered invisible. Furthermore, artefacts in the ﬁlms must be removed. This study examines the use of advanced image analysis techniques applied to a selection of X-ray topographs in section transmission mode: (i) the automated counting of oxygen-related precipitates and (ii) the enhancement of Pendell¨osung fringes. The technique also succeeds in removing unwanted features in the original x-ray topographs such as vertical streaking due to collimating slit phase contrast and strain features near the surface due to the presence of integrated circuit process strains

Epelboin

G. Murphy

McNally

Meieran

Ming

P. F. Whelan

P. J. McNally

Partanen

Pilyard

R. Simon

T. Tuomi

Tuomi

Vincent

Crossref

The use of neighbourhood intensity comparisons, morphological gradients and Fourier analysis for automated precipitate counting & Pendellösung fringe analysis in X-ray topography

Murphy, G.

Whelan, Paul F.

McNally, Patrick J.

Tuomi, T.

Name not available

Eur. Phys. J. Appl. Phys. 27, 443–446 (2004)DOI: 10.1051/epjap:2004087 THE EUROPEANPHYSICAL JOURNALAPPLIED PHYSICSThe use of neighbourhood intensity comparisons, morphologicalgradients and Fourier analysis for automated precipitate counting& Pendello¨sung fringe analysis in X-ray topographyG. Murphy1, P. F. Whelan1, P. J. McNally2,a, T. Tuomi3, and R. Simon41 Vision Systems Group, Research Institute for Networks & Communications Engineering (RINCE), Dublin City University,Dublin 9, Ireland2 Microelectronics Group, RINCE, Dublin City University, Dublin 9, Ireland3 Optoelectronics Laboratory, Helsinki University of Technology, PO Box 3500, 02015 TKK, Espoo, Finland4 Institute fu¨r Synchrotronstrahlung (ISS), Forschungszentrum Karlsruhe GmbH, Postfach 3640, D-76021 Karlsruhe, GermanyReceived: 30 June 2003 / Accepted: 31 December 2003 – © EDP SciencesAbstract. Crystal distortions modify the propagation of X-rays in single crystal materials, and X-raytopography can be used to record these modiﬁcations on a ﬁlm thus providing images of the distributionsand nature of defects, dislocations, strains, precipitates, etc. in semiconductors. Small variations of contrast,which often need to be analysed can be rendered invisible. Furthermore, artefacts in the ﬁlms must beremoved. This study examines the use of advanced image analysis techniques applied to a selection of X-raytopographs in section transmission mode: (i) the automated counting of oxygen-related precipitates and(ii) the enhancement of Pendello¨sung fringes. The technique also succeeds in removing unwanted featuresin the original x-ray topographs such as vertical streaking due to collimating slit phase contrast and strainfeatures near the surface due to the presence of integrated circuit process strains.PACS. 07.05.Pj Image processing – 61.72.Ff Direct observation of dislocations and other defects (etchpits, decoration, electron microscopy, x-ray topography, etc.) – 81.05.-t Speciﬁc materials: fabrication,treatment, testing and analysis1 IntroductionX-ray topography (XRT) is a non-destructive technique,which can provide detailed information on defect distri-butions in crystals and is mainly used for the study ofdislocations, planar defects, stacking faults, domain wallsin ferromagnetic materials, growth defects or large precip-itates [1–3]. Analysis of the topographs is often diﬃcultsince many intricate features appear in a single image.The topographs often present small variations of contrastand areas corresponding to very high or very low densitiesof grey can be quite invisible. Artefacts are created dueto ﬁlm damage, phase contrast eﬀects due to dust on col-limating slits, scratches on samples and recording ﬁlms,etc. [4]. This paper examines two diﬀerent imaging prob-lems related to two diﬀerent types of topograph: (A) theidentiﬁcation, segmentation and counting of oxide-relatedprecipitates which appear as dark blobs of varying inten-sity and shape in an image and (B) enhancing and high-lighting Pendello¨sung fringes, which appear as black andwhite horizontal lines, while removing unwanted verticaland diagonal streaks and artefacts.a e-mail: patrick.mcnally@rince.ie2 ExperimentalThe measurements were performed at the HASYLAB amDESY Synchrotron, Hamburg, Germany as described else-where [5,6] and at the ANKA Synchrotron, ISS, Karl-sruhe, Germany. In section topography, which was usedin this study, the beam was limited by a horizontal slithaving a width of 15 µm, and the (001) silicon wafer sur-face was at an angle of 20° with respect to the incidentbeam. The images were then generated from the ﬁlmsby using a CCD camera in conjunction with an opticalmicroscope and these images are saved in ‘tif ’ format.Analysis of the images was carried out in NeatVision(www.neatvision.com) which is a Java-based computervision package developed by the Vision Systems Group atDCU.3 Automated counting of images ofoxygen-related precipitates in sectiontransmission topographs of a silicon waferSection transmission topography results in non-destructively acquired images of a “slice” of a444 The European Physical Journal Applied Physicssemiconductor wafer, in the case of this study, heat-treated Czochralski-grown Si wafers. The wafers are(100) oriented p-type Si wafers, with a resistivity of8–15 Ohm-cm. They were subjected to a 1160 °C annealin a N2 ambient in order to simulate typical thermalannealing conditions in an IC process, which has beendescribed previously [5]. When the Si wafer is subjectedto thermal cycling during production, the presence ofoxygen impurities in the wafer results in the nucleationof large oxygen-related precipitates. These appear asblack “blobs” in the section transmission topographs,and their presence may be related to inferior electricalperformance [6]. An original unprocessed section topo-graph (ST) of a Si wafer is shown in Figure 1. The spatialFig. 1. Typical 220 section topograph showing precipitates ina Si wafer. The projection of the diﬀraction vector g in theplane of the recording ﬁlm is indicated.resolution in X-ray topography is 1–2 µm and the spatialresolution on the ﬁlm is approx. 1 µm. A 256 (8-bit)grey-scale intensity range was used to manipulate data inthe NeatVision environment. One of the main diﬃcultiesin counting precipitates lies in the fact that they vary insize, shape and intensity (the precipitate images can varyin size from 1–50 µm, though one must count them allwithin a typical topograph).The problem of counting the precipitates in a topo-graph is resolved into three main parts, namely, identiﬁca-tion, separation and ﬁnally counting. A selected ∼ 50 µm×50 µm region of the original section topograph is shownin Figure 2(a). The use of regional maxima [7] and vari-ous neighbourhood patterns are two methods, which wereassessed to identify the precipitates. Also, overlapping pre-cipitate images must be separated. This was achievedthrough using a combination of morphological gradientgeneration and conditional dilation. This process is nowoutlined.Due to the illumination gradient inherent in the CCDcapture process, simply identifying the precipitates (suchas those illustrated in Figure 2(a)) using a global thresh-old does not work. This produces results where there aresigniﬁcantly more precipitates at the edges of the imagesthan in the centre. There are a number of methods to re-move illumination gradients such using as black or whiteFig. 2. Automated image analysis of the precipitate images.(a) Section from a topograph showing the precipitates. (b)Identiﬁed precipitates (in white) overlaid on the original image.(c) Mask image: thresholded version of image (b). (d) Mor-phological gradient of image (c). (e) Marker image: result ofsubtracting image (d) from image (b). (f) Conditional dilationof the marker image using the binary mask. (g) Subtraction ofthe conditionally grown image from the mask image.“top hats” [7–10]. Using this methodology, some of theprecipitate information can be lost as it can be identiﬁedincorrectly as noise.For the identiﬁcation of the precipitates a numberof diﬀerent neighbourhood patterns were compared. Ineach case the intensity of the central pixel was foundand compared to the average of the outer pixels in theneighbourhood. If a suﬃcient diﬀerence exists between thetwo values then that pixel was copied to the output im-age as it formed either a part of a blob or an entire blob.All other pixels were transformed to white in the outputG. Murphy et al.: The use of neighbourhood intensity comparisons. . . 445image (Figure 2(b)). The resulting image was thresholded(Figure 2(c)) and is referred to as the “mask” image forconditional dilation. The thresholding operation performstwo functions: it creates a binary image where the precipi-tates are represented as white blobs on a dark backgroundin preparation for counting and also by performing a log-ical “OR”-ing of this image with the original image, thelocations of the precipitates are highlighted.The edge pixels were used rather than the completeneighbourhood due to the variable but small size of theprecipitates. This is not an issue as the number of precip-itates is small compared to the height and width of theimage. It was found that the 7× 7 square neighbourhoodpattern containing 24 boundary pixels gave the best re-sults.A major diﬃculty lies in identifying the precipitatesthat tend to touch and overlap (an arrow points outonce case of overlapping blobs in Figure 2(b)). Precipitateimages should ideally be circular due to the sphericallyisotropic stress distribution surrounding them [11]. Henceany elongated images are most likely due to overlappingprecipitate images and these precipitates need to be sepa-rated. A morphological gradient [10] outlines the overlap-ping images and a very small structuring element (2 × 2array) is required due to the small size of the precipi-tate images (Figure 2(c)). When the morphological gra-dient is found (Figure 2(d), note the boundary aroundthe two overlapped blobs), it is subtracted from the orig-inal image. This leaves a marker for each of the over-lapped blobs and also for any of the larger blobs, thisimage is referred to as the “marker” image (Figure 2(e)).These markers are counted and the value stored. Usingthe original image as a mask, the marker image is grownby conditional dilation (Figure 2(f)) using the mask imagegenerated previously (Figure 2(c)) to condition the growthprocess. The resultant image contains the precipitates,which the morphological gradient has highlighted. This issubtracted from the original image (as these have alreadybeen counted and segmented) resulting in the image il-lustrated in Figure 2(g). The remaining non-overlappingprecipitates are then counted. Both counts are then addedto give the total number of precipitates in the image.A dilation count method was used, as it counts all theidentiﬁed blobs with 100% accuracy regardless of orien-tation and shape. The algorithm is as follows: (a) rasterscan the image until a white pixel is found, (b) incrementcounter, (c) copy white pixel to a black holding image ofthe same size as the original, (d) use the holding image as amarker and the original image as a mask and grow the blobby conditional dilation (10 iterations), (e) subtract thisimage from the ﬁrst image (thus removing the blob fromthe image), (f) keep repeating the ﬁve steps above untilthe raster scan of the image is completed. This methodwill accurately count all 4-connected precipitate imagesregardless of shape, size of orientation. A complete to-pograph with approximately ﬁfteen hundred precipitatestakes of the order of 1 minute on a standard personalcomputer. Experimentation has shown that the dilationFig. 3. Images after processing and images of the removednoise. (a) Original unprocessed 220 section transmission to-pograph image of a Si wafer displaying Pendello¨sung fringes,oxygen-related precipitates and vertical streaks; (b) after theinverse Fourier transform was performed on an image whichhas had a 5-pixel wide strip pass ﬁlter applied; (c) the noisecomprising of uneven illumination and streaks which has beenremoved; (d) after the inverse Fourier transform was performedon an image which has had a 1-pixel wide strip pass ﬁlter ap-plied; (e) the noise comprising of uneven illumination and di-agonal streaks which has been removed.count method matches the actual count for a wide rangeof diﬀerent regions within our test topograph’s dataset.4 Enhancement of images of Pendello¨sungfringes (PFs) in section transmissiontopographs of nearly perfect siliconAn evaluation of the relative crystalline perfection ofthe Si substrate is one of the crucial factors impactingthe reliability of completed ICs. This evaluation can becarried out using X-ray topographic techniques, wherea particular emphasis can be placed on the appearance(high crystalline quality) or disappearance/perturbationsof Pendello¨sung fringes (strained or low quality) in Si sub-strates [13–17]. An example of the Pendello¨sung fringe(PF) phenomenon is shown in Figure 3(a), which is a220 X-ray transmission section topograph of a section ofa silicon wafer. Other unwanted features include verticalstreaks due to the eﬀect of the phase contrast impact ofdust, or imperfections in the collimating slits. Backsidedamage can lead to vertical and horizontal streaks and isoften due to scratching and mechanical damage of the back446 The European Physical Journal Applied Physicsof the wafer. These must be removed. Oxygen related pre-cipitates are also observed as the small black/white blobsthroughout the topograph image. Another problem is un-even illumination of the image.Due to the periodic nature of the information in theimages, and in light of previous research, it appears thatFourier analysis could be an eﬀective method for solv-ing this problem. However, the low pass spatial ﬁlteringmethodology of [4] cannot be directly used for PF en-hancement. Firstly, the brightness gradient must be re-moved. The authors used a mathematical morphologicalwhite “top hat” (image minus opening with a structur-ing element), and 11 × 11 and 21 × 21 sized structuringelements (SE) [7–10]. It was found that opening with an11 × 11 SE identiﬁes the PFs as a background and triesto remove them. However, while the resultant images aredarker, the lighting gradient is removed and the imagesare more uniform. Removing the brightness gradient witha white top hat using a 21× 21 SE gives the best results.The PFs are separated from the image noise by theapplication of the Fourier transform [7–9]. Examining themagnitude image, after applying the Fourier transform,both as an image and as a thresholded image did not showany obvious peaks in the spectrum relating to the verti-cal and/or any diagonal streaks. However, the requiredinformation contained in the PFs exist in the horizontalplane of the original image. Thus in frequency space, allthe information of interest exists in the vertical plane. Inorder to isolate the information in the vertical plane, astrip pass ﬁlter was used. This ﬁlter only passes informa-tion contained in the vertical axis out to a certain numberof pixels on either side. The impact of using the Fouriertransform with a strip pass filter [7–9] to eﬃciently high-light the PFs is shown in Figures 3(b)–(e). It can be seenin Figure 3(d) that when the ﬁlter is one pixel wide, infor-mation at the left and right edges of the original image islost. This is most easily observed in Figure 3(e) which isan image of what has been removed from the image, i.e.the brightness gradient, which was removed with a whitetop hat and the diagonal or vertical streaks and backsidedamage. Excessive information tends to be removed, andthis is particularly acute near the left- and right-hand up-per surfaces. This tends to lead to less deﬁnition of thePFs near the upper and lower surfaces of the topograph.However when a strip pass ﬁlter of ﬁve pixels wide is usedthen the information at the edges is preserved; see Fig-ures 3(b)–(c) for comparison. Successively adding the re-sultant image to the original image emphasises the PFsand de-emphasises the noise in the image. This acts asa quick check that the horizontal black and white linesfound are indeed the Pendello¨sung fringes.5 Conclusions5.1 Precipitate countingThe results have shown that electronic image process-ing and identiﬁcation of oxygen-related precipitates isachieved using a seven by seven-square neighbourhood.The overlapping precipitates are correctly separated out us-ing a morphological gradient. The precipitates have beenaccurately counted regardless of orientation, shape or sizeby a counting algorithm based on mathematical morpho-logical methods.5.2 Pendello¨sung fringe enhancementRemoving the brightness of the gradient of the image mustbe the ﬁrst pre-processing step. This is performed viamathematical morphological methods. The Pendello¨sungfringes are then separated from the unwanted streaks ornoise in the image by strip pass filtering the magnitudeimage in the Fourier domain. It was found that using aﬁlter with a width of ﬁve pixels produced the best results.This work was supported by the IHP-Contract HPRI-CT-1999-00040/2001-00140 of the European Commission. The authorsthank C. Paulmann at beamline F-1, HASYLAB, Germany.References1. A. R. Lang, Diﬀraction and Imaging Techniques inMaterial Science, edited by S. Amelinckx, R. Gevers,J. Van Landuyt (Amsterdam: North-Holland, 1978), p. 6232. T. Tuomi T, K. Naukkarinen, P. Rabe, Phys. Stat. Sol. A25, 93 (1974)3. A. Authier, Diﬀraction and Imaging Techniques inMaterial Science, edited by S. Amelinckx, R. Gevers,J. Van Landuyt (Amsterdam: North-Holland, 1978), p. 7154. M. Pilyard, Y. Epelboin, A. Soyer, J. Appl. Cryst. 28, 279(1995)5. P. J. McNally, J. W. Curley, M. Bolt, A. Reader, T. Tuomi,R. Rantama¨ki, A. N. Danilewsky, I. DeWolf, J. Mater. Sci.:Mater. Electron. 10, 351 (1999)6. T. Tuomi, M. Tuominen, E. Prieur, J. Partanen,J. Lahtinen, J. Laakkonen, J. Electrochem. Soc. 142, 1699(1995)7. P. F. Whelan, D. Molloy, Machine Vision Algorithms inJava, Techniques and Implementation (Springer-Verlag,2001)8. P. Soille, Morphological Image Analysis: Principles andApplications (Springer-Verlag, 1999)9. J. C. Russ, The image processing handbook (CRC Press,1992)10. L. Vincent, IEEE Trans. Image Proc. 2, 176 (1993)11. L. Ming, Z. H. Mai, S. F. Cui, Acta Cryst. A50, 725 (1994)12. S. Beucher, F. Meyer, Image Processing, edited byDougherty (Marcel Dekker Inc., 1993), pp. 433–48313. P. J. McNally, A. N. Danilewsky, J. W. Curley, A. Reader,R. Rantama¨ki, T. Tuomi, M. Bolt, M. Taskinen,Microelectron. Eng. 45, 47 (1999)14. E. S. Meieran, I. L. Blech, J. Appl. Phys. 36, 3162 (1965)15. Y. Epelboin, J. Appl. Phys. 64, 109 (1988)16. D. K. Bowen, B. K. Tanner, High Resolution X-RayDiﬀractometry and Topography (Taylor & Francis,London, UK, 1998)17. J. Partanen, T. Tuomi, J. X-Ray Sci. Technol. 2, 165(1990)

The use of neighbourhood intensity comparisons, morphological gradients and Fourier analysis for automated precipitate counting & Pendell¨osung fringe analysis in X-ray topography

Tuomi, Tiinamaija

Simon, R.

Irish Universities

Crystal distortions modify the propagation of X-rays in single 
crystal materials, and X-ray topography can be used to record 
these modifications on a film thus providing images of the 
distributions and nature of defects, dislocations, strains, 
precipitates, etc. in semiconductors. Small variations of contrast, 
which often need to be analysed can be rendered invisible. 
Furthermore, artefacts in the films must be removed. This study 
examines the use of advanced image analysis techniques applied to 
a selection of X-ray topographs in section transmission mode: (i) 
the automated counting of oxygen-related precipitates and (ii) the 
enhancement of Pendellösung fringes. The technique also succeeds 
in removing unwanted features in the original x-ray topographs such 
as vertical streaking due to collimating slit phase contrast and 
strain features near the surface due to the presence of integrated 
circuit process strains

EDP Sciences OAI-PMH repository (1.2.0)

The use of neighbourhood intensity comparisons, 
morphological gradients and Fourier analysis for automated 
precipitate counting & Pendellösung fringe analysis in 
X-ray topography

Whelan, P. F.

McNally, P. J.

DESY

DESY Publication Database

DCU Online Research Access Service

http://bib-pubdb1.desy.de/search?p=id:%22PUBDB-2017-05624%22

The use of neighbourhood intensity comparisons, morphological gradients and Fourier analysis for automated precipitate counting & Pendell¨osung fringe analysis in X-ray topography

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