METHOD FOR PRODUCING AND CLASSIFYING POLYCRYSTALLINE SILICON

Abstract

A method for producing and classifying polycrystalline silicon. The method includes producing polycrystalline silicon rod within a reaction space of a gas phase deposition reactor by introducing a reaction gas, which in addition to hydrogen contains silane and/or at least one halosilane. Once produced, the polycrystalline silicon rod is extracted from the reactor, and at least one two-dimensional and/or three-dimensional image is generated of at least one partial region of the polycrystalline silicon rod or of at least one silicon chunk created. At least one analysis region is selected per generated image and at least two surface-structure indices per analysis region are generated by using image processing methods, each of which is generated using a different image processing method. The surface-structure indices are combined to form a morphology index.

Claims

1-13. (canceled)

14. A method for producing and classifying polycrystalline silicon, comprising: producing a polycrystalline silicon rod by introducing a reaction gas, which in addition to hydrogen contains silane and/or at least one halosilane, into a reaction space of a gas phase deposition reactor, wherein the reaction space contains at least one heated filament rod upon which silicon is deposited to form the polycrystalline silicon rod; extracting the polycrystalline silicon rod from the reactor; optionally comminuting the polycrystalline silicon rod to obtain silicon chunks; generating at least one two-dimensional and/or three-dimensional image of at least one partial region of the polycrystalline silicon rod or of at least one silicon chunk, and selecting at least one analysis region per generated image; generating at least two surface-structure indices per analysis region by using image processing methods, each surface-structure index being generated using a different image processing method; combining the surface-structure indices to form a morphology index; and wherein the image processing methods are selected from the group comprising determination of a grey-level co-occurrence matrix, use of a rank filter, identification of depressions relative to a convex envelope and determination of the width of a laser line, and wherein the polycrystalline silicon rods or the silicon chunks are classified depending on the morphology index and are sent to different further-processing steps.

15. The method of claim 14, wherein the two-dimensional images are generated under dome lighting.

16. The method of claim 14, wherein at least two, preferably at least three, particularly preferably at least four, two-dimensional images are generated, each from a different viewing angle.

17. The method of claim 14, wherein at least two, preferably at least three, particularly preferably at least four, two-dimensional images are generated, each under a different illumination.

18. The method of claim 14, wherein the three-dimensional images are generated using a laser as light source.

19. The method of claim 14, wherein the three-dimensional images are generated by scattering a laser point and/or by a laser line on a surface of the chunks that is being evaluated.

20. The method of claim 14, wherein the three-dimensional images are generated by using laser triangulation and/or stripe-light projection.

21. The method of claim 14, wherein the polycrystalline silicon rod or the silicon chunks are sent to the generation of the two-dimensional or three-dimensional images via a conveyor belt.

22. The method of claim 14, wherein the rank filter is a median filter.

23. The method of claim 14, wherein the surface-structure indices are combined to form the morphology index by using a linear combination, a support vector machine, regressions or artificial neural networks.

24. The method of claim 14, wherein the further-processing steps are selected from the group comprising comminution, packaging, sorting, sampling for quality assurance and combinations of these.

Description

[0065] FIG. 1 shows an arrangement for morphology determination after deposition

[0066] FIG. 2 shows the segmentation of a polysilicon chunk

[0067] FIG. 3 schematically shows the determination of a surface-structure index on the basis of GLCM

[0068] FIG. 4 graphically shows the distribution of the GLCM-based surface-structure indices for different polysilicon types

[0069] FIG. 5 schematically shows the determination of a surface-structure index on the basis of the identification of depressions

[0070] FIG. 6 graphically shows the distribution of the GLCM-based surface-structure indices for different polysilicon types

[0071] FIG. 7 shows the distribution of the morphology index for different polysilicon types

[0072] FIG. 1 shows an arrangement 10, comprising a conveyor belt 12 the advancing direction of which is denoted by two arrows. On the conveyor belt 12 are situated separated polysilicon chunks 20 which are to be classified on the basis of their morphology. Dome lighting 14, which comprises a plurality of cameras 18 and light sources 16, is arranged above the conveyor belt 12. The cameras 18 and light sources 16 are coupled to software and can each be controlled individually. For example, homogeneous light conditions can thus be generated with the light sources 16. However, light incidence from a particular direction can also be produced. For determination of the morphology, one or else more of the chunks 20 are now moved under the dome lighting 14 and 2D images of the chunk or chunks 20 are generated according to the selected imaging setup. The images are preferably generated continuously, that is to say without halting the conveyor belt 12. Using the software, the surface-structure indices are determined from the generated images and are then combined to form a morphology index which is then used for the classification. By way of example, a sorting installation can be arranged at the end of the conveyor belt 12. In principle, a silicon rod can also be moved along its longitudinal axis under the dome lighting 14 on the conveyor belt 12.

EXAMPLE

[0073] Polysilicon rods of three different quality types were produced in a gas phase deposition reactor.

[0074] Type 1 is a very compact polysilicon which is destined in particular for the production of semiconductors. There are generally hardly any differences in terms of the morphology between the surface and the interior of the rod.

[0075] Type 2 has an intermediate compactness and is used in particular for cost-optimized, robust semiconductor applications and demanding solar applications using monocrystalline silicon.

[0076] Type 3 has a high proportion of popcorn. It has a relatively fissured surface and a high porosity. It is used in particular for the production of multicrystalline silicon for solar applications.

[0077] A rod of each type was comminuted in each case and the morphology index was determined for each of the chunks using dome lighting as illustrated in FIG. 1. After the comminution, the chunks were first separated on a conveyor belt and moved under dome lighting at a constant speed (advancing rate). The dome lighting was equipped with six area scan cameras at different positions. The 2D images were generated simultaneously from a plurality of viewing angles. A total of six images were recorded per chunk. In the evaluation described hereinafter, for reasons of clarity only one image per chunk (viewing angle perpendicular to the surface of the conveyor belt from above) was subjected to an evaluation, that is to say the morphology index was determined. In total, 4103 chunks of type 1, 9871 of type 2 and 6918 of type 3 polysilicon were examined.

[0078] An analysis region was defined for each image by segmentation. FIG. 2 shows by way of example segmentation on the basis of a type-3 polysilicon chunk for the generation of an analysis region. The segmented region, that is to say the analysis region, is illustrated on the right-hand side in FIG. 2.

[0079] The segmentation of the chunk is carried out by the following steps: (1) Applying a filter (blur) to the entire image region, in order to smooth hard edges.

[0080] (2) Applying a further filter (Sobel filter, direction-independent) for calculating the brightness differences.

[0081] (3) Segmenting the chunk from the outside inwards by identifying the region having a brightness difference greater than a defined threshold. This involves iteratively discarding regions having too low a brightness difference starting from the outside until only the relevant region (cf. FIG. 2, right-hand side) still remains as analysis region.

[0082] From this analysis region were generated a first surface-structure index by determination of the grey-level co-occurrence matrix (GLCM values) and a second surface-structure index by identification and assessment of depressions.

[0083] The scheme for calculation of the GLCM values is illustrated in FIG. 3.

[0084] The GLCM (grey-level co-occurrence matrix) is determined by counting combinations of greyscale values. An entry is made in the GLCM for each pixel in the analysis region, where i is the greyscale value of the pixel itself and j is the greyscale value of the pixel in the vicinity. Since a pixel in a typical 2D image has 8 neighbouring pixels, it is usual to determine the GLCM for all directions and to take the average of these. It is also possible not to use immediate neighbouring values, but to use the neighbouring value at a distance of n pixels. The immediate neighbours were used in the example. Division by the sum total of the matrix entries is then typically performed. The values then correspond to a probability p for the particular greyscale value combination.

[0085] Consideration of the contrast (equation (I)): For this purpose, high contrasts (i.e. large differences in the greyscale values) are provided with a high weighting. The term |i−j|.sup.2 from equation (I) is then large when the values are as remote as possible from the main diagonals. These are the values at which i and j are maximally different, that is to say the greyscale values are maximally different.

[0086] Consideration of the homogeneity (equation (II)): Here there is division by the term 1+|i−j|. Values close to the main diagonals are therefore weighted more heavily. As a result, regions having very similar greyscale value ranges are given a higher value in this index. Two surface-structure indices are thus obtained in principle by the equations (I) and (II).

[0087] It can be recognized from the graphical evaluation shown in FIG. 4 of the GLCM indices for the three different polysilicon types that the values obtained for homogeneity and contrast are opposed. The distribution of the indices for the individual polysilicon types is illustrated in the histograms. The values on the X axis correspond to the values for the respective index. The density concerns the relative frequencies for the occurrence of the particular value.

[0088] The generation of the second surface-structure index on the bases of the identification and assessment of depressions is schematically shown in FIG. 5, with on the one hand the number of holes per area and on the other hand the hole size as an average greyscale value gradient at the edge of the hole having been determined. A median filter is used to present the depressions relative to their surroundings. This makes it possible to subsequently find and mark the regions having a value less than a defined threshold and a defined minimum size in pixels (cf. the rectangles of different sizes).

[0089] The evaluation for the second surface-structure indices is illustrated in FIG. 6. Here, the hole regions in the analysis region are counted and output relative to the pixel area. For type 1 (very compact), only very few holes are present, that is to say the index has a value close to zero. Somewhat more holes are present for type 2. Type 3 (fissured) has a recognizable distribution of holes (cf. FIG. 6, bottom). For an assessment of the holes, the hole size is considered as an average gradient at the edge of the hole (greyscale value drop-off), the values being scaled. For type 1, this is lower since the holes present are less deep and pronounced and therefore do not appear as dark. For type 2 and type 3, the hole regions are more strongly pronounced (steeper and thus darker), and as a result the value for the index rises.

[0090] The surface-structure indices determined are combined with each other (combined by calculation) in a final step in order to obtain a morphology index which can be used as a basis for subjecting the relevant polysilicon chunk for example to a sorting (i.e. classification). This combination is effected by means of a linear combination using the following equation

y.sub.j=Σ.sub.i=1.sup.Na.sub.i*(x.sub.j,i−b.sub.i),

where [0091] x.sub.j,i=ith index of the jth chunk [0092] a.sub.i=gradient for the ith index [0093] b.sub.i=base value for the ith index [0094] y.sub.j=morphology value of the jth chunk.

[0095] The result of the linear combination is shown in FIG. 7 using the histogram. The resulting distributions differ significantly, and accordingly the three different polysilicon types are distinguishable from each other. The combination of a plurality of indices makes the method more robust and more independent of individual outliers.