Classifications

• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B26—HAND CUTTING TOOLS; CUTTING; SEVERING
  • B26D—CUTTING; DETAILS COMMON TO MACHINES FOR PERFORATING, PUNCHING, CUTTING-OUT, STAMPING-OUT OR SEVERING
  • B26D5/00—Arrangements for operating and controlling machines or devices for cutting, cutting-out, stamping-out, punching, perforating, or severing by means other than cutting
  • B26D5/20—Arrangements for operating and controlling machines or devices for cutting, cutting-out, stamping-out, punching, perforating, or severing by means other than cutting with interrelated action between the cutting member and work feed
  • B26D5/30—Arrangements for operating and controlling machines or devices for cutting, cutting-out, stamping-out, punching, perforating, or severing by means other than cutting with interrelated action between the cutting member and work feed having the cutting member controlled by scanning a record carrier
  • B26D5/32—Arrangements for operating and controlling machines or devices for cutting, cutting-out, stamping-out, punching, perforating, or severing by means other than cutting with interrelated action between the cutting member and work feed having the cutting member controlled by scanning a record carrier with the record carrier formed by the work itself
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B28—WORKING CEMENT, CLAY, OR STONE
  • B28D—WORKING STONE OR STONE-LIKE MATERIALS
  • B28D5/00—Fine working of gems, jewels, crystals, e.g. of semiconductor material; apparatus or devices therefor
  • B28D5/04—Fine working of gems, jewels, crystals, e.g. of semiconductor material; apparatus or devices therefor by tools other than rotary type, e.g. reciprocating tools
  • B28D5/045—Fine working of gems, jewels, crystals, e.g. of semiconductor material; apparatus or devices therefor by tools other than rotary type, e.g. reciprocating tools by cutting with wires or closed-loop blades
• • 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
  • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
  • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
  • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
  • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
  • G01N21/3563—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light for analysing solids; Preparation of samples therefor
• • 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
  • G01N2021/8461—Investigating impurities in semiconductor, e.g. Silicon
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
  • G01N21/84—Systems specially adapted for particular applications
  • G01N21/88—Investigating the presence of flaws or contamination
  • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
  • G01N2021/8854—Grading and classifying of flaws
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
  • Y02P70/00—Climate change mitigation technologies in the production process for final industrial or consumer products
  • Y02P70/10—Greenhouse gas [GHG] capture, material saving, heat recovery or other energy efficient measures, e.g. motor control, characterised by manufacturing processes, e.g. for rolling metal or metal working

Definitions

• TITLE PROCESS FOR DETERMINING AT LEAST ONE CUTTING PARAMETER OF A BRICK OF A SEMICONDUCTOR MATERIAL AND DEVICE IMPLEMENTING THE PROCESS
• the technical field of the invention is that of cutting semiconductor bricks such as silicon. More particularly, the invention relates to the determination of the cutting parameters of a semiconductor brick as a function of the characteristics of the precipitates present in the brick.
• the wire used during diamond wire cutting costs approximately 50 times more than the steel wire used during "slurry" cutting. It is therefore undesirable to use it only once and a process of back and forth with the yarn is therefore used. With each round trip of about 500 m, a few meters of new thread are fed into one end of the wire web and a few meters of used thread are taken out at the other end of the wire web. Thus, to cut 2,000 wafers, it is common to use only 2 km of wire, two hundred times less than when cutting with slurry. In this case, it is well understood that when the diamonds encounter precipitates, the behavior of the wire is different.
• Diamond one of the hardest materials in the world, can cut SiC or ShISU precipitates, but the parameters used when cutting silicon do not allow it to be done under good conditions. In this case the diamonds will become dull, break or tear off during the repeated passage of the wire through the material. Also, if two or more threads are diverted from their path and come together, the diamonds on the different strands of thread will collide with each other, further accelerating the tearing phenomenon. The encounter of precipitates by diamonds therefore poses a problem.
• a first aspect of the invention relates to a method for determining at least one cutting parameter C of a brick of a semiconductor material comprising: a step of determining the density and the density. size of the precipitates present in the brick; from at least one predetermined threshold, a step of dividing the brick into several zones, each zone being associated with a class chosen from at least two classes; depending on the proportion of areas associated with each class, a step of determining at least one cut parameter C.
• the cutting parameter (s) take into account the specificities of each brick in terms of precipitates. The risk of wire breakage during cutting is thus greatly reduced.
• the method according to a first aspect of the invention may have one or more additional characteristics among the following, considered individually or in any technically possible combination.
• the threshold or thresholds defining the classes are chosen as a function of criteria comprising the consumption of wire, the cutting time, the sawing yield, the quality of the slices (or wafers in English) and / or the risk of wire breakage.
• the density of the precipitates is calculated based on the perimeter or the area of said precipitates.
• the cutting parameter (s) C are chosen from at least one of the following parameters: the table speed, the wire consumption per slice, the wire speed, and / or the orientation of the brick.
• the step of determining the density and / or the size of the precipitates comprises: a sub-step of imaging the brick using a range of wavelengths for which the material composing said brick is transparent; a sub-step of processing the image obtained so as to extract the density and / or the size of the precipitates.
• the brick imaging substep comprises acquiring at least one face of the brick, preferably all four faces of the brick.
• the image processing sub-step is preceded by an image processing test sub-step.
• the step of dividing the brick into several zones is carried out from two thresholds defining three classes: a first class corresponding to an easy cut, a second class corresponding to a moderately difficult cut, and a third class corresponding to a very difficult cut.
• each class can be associated with a height, the total height of all the classes corresponding to the total height associated with the brick, the cutting parameter (s) being determined as a function of the value of the heights associated with each class.
• a second aspect of the invention relates to a cutting method comprising a step of determining at least one cutting parameter using a method according to a first aspect of the invention, the parameter or parameters. thus determined being implemented during cutting.
• a third aspect of the invention relates to a device comprising the means for implementing a method according to a first aspect of the invention.
• Figure 1 shows a flowchart of a method according to a first aspect of the invention
• Figure 4 shows an infrared image of a silicon brick.
• Figure 5 shows a device for imaging in the infrared one or more faces of a semiconductor brick.
• Figure 6 shows the fast Fourier transform of an infrared image of a silicon brick.
• Figure 7 illustrates the reduction of the vertical halftone by the application of a multiband cut filter.
• Figure 8 shows an image of a silicon brick before (left) and after (right) reduction of the vertical screen.
• Figure 9 shows a step of projecting an infrared image of a silicon brick.
• Figure 10 shows a spread infrared image of a silicon brick.
• Figure 11 shows a normalized infrared image of a silicon brick.
• Figure 12 compares the projection of an initial image to the projection of the same normalized image.
• Figure 13 illustrates the identification of rectification traces on an infrared image of a silicon brick.
• Figure 14 schematically illustrates the deformations to be applied to the image.
• Figure 15 shows an infrared image of a silicon brick after deformation.
• Figure 16 compares two infrared images of a silicon brick before
• Figure 17 illustrates a division between an image and this same “blurred” image making it possible to bring out the precipitates.
• Figure 18 illustrates a thresholding operation
• Figure 20 shows patterns for performing tests on image processing methods.
• Figure 21 shows a brick divided into several zones, each zone being associated with a class.
• Figure 22 illustrates the evolution of the deflection of the cutting line during a reference cut.
• Figure 23 illustrates the analysis of precipitates for a silicon brick to be cut.
• Figure 24 illustrates the evolution of the deflection of the cutting line during a cut for which at least one of the parameters has been calculated using a method according to a first aspect of the invention.
• a first aspect of the invention relates to a method 100 for determining at least one cutting parameter C of a brick of a semiconductor material, for example a silicon or carbide brick. silicon.
• cutting parameter C is meant any parameter linked to the cutting consumable or to the process and making it possible to modulate the characteristics (or the quality) of the products resulting from the cutting (silicon or SiC wafer).
• the cutting parameter (s) are chosen from at least one of the following parameters: the table speed, the wire speed, the wire consumption per slice and / or the orientation of the brick.
• the term “orientation of the cut” is understood to mean the choice of the face of the brick on which the cut will begin.
• First step 1 E1 determination of the density and / or the size of the precipitates present in the brick
• the method 100 comprises a first step 1 E1 for determining the density and / or the size of the precipitates present in the brick.
• precipitate is meant an area of light intensity different from that of the rest of the infrared image.
• density of precipitates means the number of precipitates per unit area or per unit of volume.
• size of the precipitates is understood to mean the average size of the precipitates per unit area or per unit of volume. In one embodiment, the density of the precipitates is calculated based on the perimeter or area of said precipitates.
• step 1 E1 of determining the density and / or the size of the precipitates is carried out using an imaging technique. More particularly, step 1 E 1 of determining the density and / or size of the precipitates comprises: a sub-step 1 E11 of imaging the brick using a range of wavelengths for in which the material making up said brick is transparent (that is to say a transmittance at least greater than or equal to 20%, preferably greater than or equal to 50%); a sub-step 1 E12 of processing the image obtained so as to extract the density and / or the size of the precipitates.
• the brick imaging sub-step 1 E11 comprises the acquisition of at least one face of the brick, preferably the acquisition of the four faces of the brick (in other words, four clichés of the brick are made, each cliché being associated with a face).
• the face of the brick is understood to mean a face capable of being sawn during the cutting procedure.
• the term face here excludes the two faces constituting the two ends of the brick.
• the photographs (or images) can for example be obtained by means of a device integrated within a cropping island, such as an infrared characterization bench.
• the range of wavelengths used for the acquisition of the image (s) depends on the material for which one seeks to determine the density and / or the size of the precipitates present in this material. In the following, for purposes of illustration, an example will be given in which the material is silicon, but the teachings provided by this example can easily be adapted to any other material without difficulty.
• the image processing sub-step 1 E12 is preceded by a sub-step for testing the image processing.
• a sub-step for testing the image processing it may be advantageous to test the treatment method used before implementing it in order to determine the cutting parameters.
• the test to be performed depends on the treatment method used. In general, such a test can be carried out by simulating an alternately black and white vertical screen, by simulating a heterogeneity of the lighting, by inducing strong traces of rectification on a brick serving as a test or even using test patterns. An example of a test using patterns will be provided in the remainder of the description.
• the width of the silicon band gap is 1.1 eV.
• silicon is absorbent in the visible, but transparent in the infrared (for example, silicon is considered transparent to IR when its transmittance exceeds 30%).
• Figure 2 illustrates the transmittance of silicon for different thicknesses as a function of wavelength. This transmittance has a plateau which extends from 1.4 ⁇ m to 6 ⁇ m and whose value is approximately equal to 54% regardless of the thickness considered. A magnification of this characteristic for visible wavelengths is shown in Figure 3. In this Figure 3, the influence of the thickness to be passed through on the transmittance is clearly visible. The dotted curve illustrates the evolution of this transmittance for a thickness of around 150 mm commonly used. It emerges from these FIGS. 2 and 3 that, in the case of silicon, the most suitable wavelength range for analysis by transparency imaging is between 1.4 ⁇ m and 6 ⁇ m, the range over which the transmittance is. highest and does not depend on thickness.
• the silicon carbide has a fairly wide absorption band of 11 ⁇ m and the silicon nitride has an intense absorption located between 10 ⁇ m and 11 pm.
• Silicon oxynitrides are characterized by a band absorption starting at 7.5 ⁇ m and extending up to 16.5 ⁇ m. In other words, these absorptions are not located in the transparency band of silicon. So the mechanism at the origin of their observation in the images by transmission of the bricks in the range of wavelengths chosen is not necessarily linked to these absorptions characteristic of the nature of these precipitates.
• Figure 4 illustrates the image obtained by IR imaging in the case of a silicon brick.
• the precipitates black marks on a white background
• This processing is carried out during sub-step 1 E12 of processing the image obtained mentioned above.
• FIG. 5 An example of a device allowing the imaging of the brick is illustrated in FIG. 5.
• a silicon brick is imaged by illuminating it by a light source and by measuring the intensity of the transmitted light. by the brick using an infrared camera. More specifically, the device comprises five parts.
• the first part comprises a light source allowing spatially homogeneous and stable illumination of the sample. Since imaging is done in the infrared, the light source used must emit in this wavelength range. In addition, the light intensity produced must be sufficient to be able to pass through the sample.
• the light source could include a halogen neon having a total light intensity of 1500 W (emitted in An str), preferably adjustable using a potentiometer. In such a device, of the 1,500 W available, only a fraction is used, this fraction depending on the opening angle of the diaphragm and the gap of the material imaged, here silicon.
• the light source may include infrared LEDs (Light Emitting Diode or LED).
• the second part comprises two diffusers mounted near the light source, their role being to ensure the homogeneity of the illumination of the sample.
• These diffusers are made of frosted glass.
• the third part comprises a diaphragm linked to the two upstream diffusers allowing illumination of the sample by a horizontal light beam.
• the diaphragm comprises a slit of approximately 5 mm in width.
• the fourth part comprises the brick to be imaged, the latter being placed on a carriage allowing the vertical translation of the brick in front of the light beam.
• this carriage also allows the rotation of the brick in front of the beam.
• three successive 90 ° rotations can be programmed for the automatic acquisition of four images corresponding to the four faces of the brick.
• the fifth part comprises an infrared linear camera and allows the line by line acquisition of the image by infrared transmission of the brick.
• the sensor could be a CCD InGaAs (gallium indium arsenide) camera of 1024 pixels marketed by Hamamatsu ® . This camera makes it possible to work in the infrared range, its bandwidth covering the range of wavelengths from 1.0 ⁇ m to 1.6 ⁇ m.
• the purpose of this image processing method is to reduce or correct any traces of surface rectification; to attenuate the lighting heterogeneities linked to the light source; to attenuate or correct the vertical halftone associated with the linear infrared camera; to carry out a flattening of the images (suppression of the background noise); to perform a thresholding (isolation of precipitates); and perform a precipitate count (location).
• the image processing can for example be divided into two phases: a first phase of correction of the artefacts and a second phase of analysis.
• morphological This is only an example, however, and image analysis may be performed in a different way.
• the effects due to the vertical screen, to illumination heterogeneities and to traces of rectifications will in particular be corrected.
• a flattening, thresholding and counting of precipitates will be carried out in particular.
• the count of precipitates that is to say the determination of the size and density of the precipitates, will subsequently make it possible to set one or more thresholds corresponding to the difficulty of cutting.
• a first artefact to be deleted is the vertical screen which is in the form of a vertical pattern repeating in space as illustrated in FIG. 4.
• the processing of this vertical screen comprises a frequency analysis of the image by Fourier transform.
• M and N of the image are not necessarily powers of 2
• an intermediate image / ' such that:
• a multi-band cut filter can be applied to the signal, the cut frequency bands, with a width of 20 pixels, being selected so as to eliminate the fundamental mode and its harmonics. .
• Figure 8 shows an image before (left) and after (right) application of the multiband cut filter.
• I (a, b) is the intensity at the intersection of column a and row b. Then, the projection thus obtained is normalized:
• Figure 9 shows the normalized projection as a function of the index of the column.
• the values of the normalized projection are distributed in an interval of 0.2 to 1.
• the edges correspond to the lower values probably due to a lack of illumination.
• Vertical bands of heightened light intensity in the infrared image result in values close to unity in the normalized projection.
• a so-called spreading operation is performed in which an image of the same size as the original image is constructed from the normalized projection obtained previously.
• the intensity values of this normalized projection are repeated and redistributed between 0 and 255 (for an image having an 8-bit depth) on each line of the image.
• This image is represented by the so-called spread image matrix, an illustration of which can be seen in FIG. 10.
• the processing of the rectification traces comprises the selection by a user of a rectification trace. As illustrated in FIG. 13, this selection can for example be carried out by entering the position of at least three points, preferably five points, on the rectification trace selected previously. From these points, it is possible to determine a second degree polynomial associated with said trace, for example by a least squares method.
• the selection operation by an operator can be automated.
• This automation can for example be done using a method using the Hough transform to extract the equation of the different parabolas in the image. For this, each point of the image is represented in a three-dimensional space, one for each parameter a, b and c to be identified. The maxima in this new parameterized space will then constitute the precipitates.
• this method requires a three-dimensional analysis which is algorithmically heavier than the rest of the processing proposed so far.
• this automation can be carried out using a method consisting of repeating the treatment presented above by anticipating the different radii of grinding wheels that can be used for grinding the bricks. Selection by the operator then becomes superfluous.
• the main problem with this method is that, even knowing a priori the radius of the grinding wheel, the inclination of the grinding wheel relative to the face of the brick is managed by the user and is therefore not constant. However, this inclination defines the shape of the contact of the grinding wheel with the brick which is strictly speaking a parabola. So for the same wheel, we can find traces of grinding of different apparent diameter from one brick to another.
• the processing of the rectification traces comprises the deformation of the image, the deformation to be applied to the image being chosen from the equation of the polynomial so as to transform the curve representative of the polynomial in a horizontal line.
• This deformation is illustrated in Figure 14 for a down-type parabola and an up-type parabola.
• the solid line curve corresponds to the curve representative of the polynomial, the arrows corresponding to the deformations to be made to obtain the horizontal line in broken line.
• the result of this deformation is illustrated in figure 15.
• the traces of rectifications appear on the image. as straight lines so as to form a pattern similar to the vertical raster described above.
• FIG. 16 illustrates the processing of the raster by showing an image before processing (on the left) and an image after processing (on the right).
• This analysis consists of a statistical analysis of the processed image making it possible to quantitatively characterize the presence of the precipitates. More particularly, it is a question of automating their localization in the image by keeping the information of position of the pixels which compose them. This analysis is essentially based on operations commonly used in image processing and therefore available in libraries of mathematical software functions such as Matlab ® .
• this analysis firstly comprises a flattening of the image with the aim of eliminating the background level of the images.
• this flattening comprises the application of a Gaussian low-pass filter to the image to be processed so as to obtain a blurred image.
• the image to be processed is then divided member by member by the blurred image.
• an image can be represented by a matrix; dividing two member-to-member images therefore amounts to dividing each matrix element of the first image by the corresponding matrix element of the second image (the orientation and resolution are preserved when applying the Gaussian filter).
• zones of homogeneous intensities are saturated (white zones) while the precipitates to be located in the brick are found in the dark parts of the image corresponding to the zones of lower intensity in the image d. 'origin (stronger infrared attenuation).
• the morphological analysis also comprises a thresholding operation making it possible to obtain a binary image (that is to say at two levels) so as to automatically list each precipitate. More specifically, the objective of thresholding is to isolate the precipitates from other elements. of flat lay image. In an exemplary embodiment, this thresholding is performed using the OTSU algorithm. This algorithm is based on an analysis of the histogram of the gray levels of the flattened image and makes it possible to determine an optimal gray level threshold to separate two populations of pixels: one will correspond to the pixels belonging to a precipitate called “population of precipitates” below, the other at the pixels of the bottom of the image. An illustration of the thresholding as just described is given in FIG. 18. At the end of the thresholding step, the image is binarized into white or black pixels depending on whether they belong to the population of precipitates or. no.
• the information concerning the precipitates present in the brick can be extracted in different forms to assist the user, for example in the selection of the zones to be eliminated by trimming.
• the extraction of certain information can in particular be facilitated by labeling the pixels of the population of precipitates. This labeling consists of giving an identical number to the pixels of a given precipitate. Membership of the precipitate considered is determined by a criterion of connectivity between pixels based on the presence of common faces or common vertices. The choice of a connectivity criterion requiring at least one common face between two pixels is more robust for counting precipitates.
• the mapping of a quantity representative of the density of the precipitates is constructed. This magnitude can for example be obtained from the total number of pixels constituting the outlines of the precipitates or else from the total number of pixels belonging to the precipitates in a unit area of the image of the brick.
• Two types of representation of the information extracted are given for example in figure 19. On the left of figure 19 is illustrated a map of the density of the precipitates obtained from the contours of precipitates with a color scale which corresponds to a number of precipitates. per unit area. It is superimposed on the image resulting from the thresholding and makes it possible to visually identify areas of high density.
• FIG. 19 To the right of figure 19 is illustrated a classification of portions of the brick into three groups, anticipating the difficulty of cutting, based on predefined density thresholds.
• the green areas are those that will easily cut, the orange areas are of uncertain difficulty, and the red areas are unsuitable for cutting.
• the treatment method is tested using printed patterns, for example the three patterns in FIG. 20. More particularly, pattern a) makes it possible to test the sensitivity of the treatment method. relative to the density of the precipitates. Pattern b) allows the consistency of the processing along the Z axis (i.e. the ordinate) to be tested. Finally, pattern c) makes it possible to check the consistency of the processing along the X axis (that is to say the abscissa).
• Second step 1 E2 division of the brick into several zones
• the method according to a first aspect of the invention also comprises, from at least one predetermined threshold, a step 1 E2 of dividing the brick into several zones, each zone being associated with a class chosen from at least two classes.
• the predetermined threshold (s) in question can be determined from a transparent image of the brick to be cut (see figure 19) or else using any other technique making it possible to determine the density and / or size of the bricks. rushed.
• the threshold or thresholds defining the classes are chosen as a function of criteria comprising the consumption of wire, the cutting time, the sawing yield, the quality of the slices or the risk of wire breakage.
• the predetermined threshold or thresholds are defined by learning.
• the value of the predetermined threshold (s) can be optimized on the basis of experience feedback from cuts under standard brick conditions previously characterized in terms of density and / or size of precipitates.
• the predetermined threshold is a threshold for the density and / or size of the precipitates.
• step 1 E2 of dividing the brick into several zones is carried out on the basis of two thresholds defining three classes: a first class corresponding to easy cutting; a second class corresponding to a moderately difficult cut; and a third class corresponding to a very difficult cut. This division allows in particular to easily visualize whether a brick will be difficult to cut or not. It also allows the use of a simple method for determining the cutting parameters while keeping the number of variables low (this aspect will be detailed below).
• each area is limited by the size of the pixels in the image or images used to determine the density and / or size of the precipitates.
• these thresholds are defined by default in% of the maximum surface density of precipitates, the density of precipitates being calculated from the number of pixels constituting the contour of the precipitates.
• the first threshold is set at 0.5% and the second threshold is set at 1%.
• the smallest area corresponds to a strip of one pixel in height extending over the entire width of the face of the brick in question.
• the membership of a band to a class is determined by taking into account all the pixels of the band according to the width. There can therefore not be a zone of a first class occupying only part of the width of the face of the brick, the other part being occupied by a zone of a second class. This is explained in particular by the fact that, during cutting, the wire is in contact with the brick over its entire width and that it is the presence of precipitates over this entire width that determines the difficulty of cutting.
• the use of height to assess the proportion of each area associated with each class is only an example and other ways of doing things, for example based on the area occupied by each area, can be envisaged.
• the method according to a first aspect of the invention finally comprises, depending on the proportion of areas associated with each class, a step 1 E3 of determining at least one cut parameter C.
• each class can be associated with a height, the total height of all the classes corresponding to a total height associated with the brick, the cutting parameter (s) being determined as a function of the value of the heights associated with each class.
• the cutting parameter C is determined using the following formula:
• hi is the height associated with the class i with ie ⁇ 2,3 ⁇
• the constants C t are linked by the following relationships:
• the value of the cut parameter C x associated with the first class is calculated as a function of one or more characteristics of the desired cut, for example a value of TTV (Total Thickness Variation or total variation of thickness ) not to exceed.
• TTV Total Thickness Variation or total variation of thickness
• the material is silicon
• the cutting parameter C x associated with the first class is the wire speed and if the desired cutting characteristic is for the TTV, then the value of the parameter of cut C will be equal to the wire speed making it possible to obtain the desired TTV in the case of a monocrystalline silicon brick (or else mono-like® without precipitate).
• this value will serve as a reference in order to establish the other two constants C 2 and C 3 . It is interesting to note that this reference value is not absolute but depends on the quality of the desired cut (through the desired cutting characteristics). For example, if we want to obtain a given TTV, we calculate the value of C x for the TTV in question then, knowing the values of k 2 and k 3 , the value of the constants C 2 and C 3 corresponding to this TTV can be calculated. This procedure makes it possible to separate the influence of the material on the cut (which is taken into account using the constants k 2 and k 3 ) from that of the characteristics of the desired cut on the value of the cut parameter (s). which is taken into account through the parameter C x .
• the preceding expression of the cutting parameter C can be generalized by:
• the wire consumption is the cutting parameter C to be determined.
• the value of C x is established as a function of a reference brick, ideally a monocrystalline silicon brick or even a mono-like® brick free of precipitate (indeed, it is possible to verify that a mono-like® without precipitate can be cut with parameters identical to those used for cutting monocrystalline silicon for the same cut quality).
• the whole of the reference brick is therefore associated with the first class corresponding to an easy cut.
• the cutting recipe for obtaining slices with the desired characteristics consumes 1 m / slice, with a cutting time of 160 minutes for an ASAHI wire 80 m ⁇ ti.
• the desired characteristics may for example relate to the TTV which varies here between 3.6 ⁇ m and 7.7 ⁇ m, knowing that the specifications of the manufacturers of the wafers are TTV ⁇ 30 ⁇ m.
• the behavior of the son during cutting is that desired: the value of the deflection corresponds to a low risk of breakage of the wire.
• the cutting parameter C x associated with the first class is therefore chosen to be equal to 1 m / slice.
• the values of k 2 and k 3 are established by reference tests making it possible to associate the infrared images and the cutting behavior.
• the value of k 2 is between 1.5 and 4.5
• the value of k 3 is between 3.5 and 6.5 and such that k 3 > k 2 .
• the brick to be cut was analyzed using the steps described above and the result of this analysis is illustrated in FIG. 23.
• the brick to be cut has been divided into five zones divided into three classes. Of this brick, 20% of the brick is in the hard to cut class, 5% of the brick is in the medium easy to cut class, and 75% of the brick is in the easy to cut class.
• the cutting parameter (here the amount of wire per slice) can therefore be calculated using the formula introduced previously and recalled here:
• the final cutting parameter C is equal to 2.15 m / slice.
• the dimensional characteristics of the slices obtained using this cutting parameter are listed in Table 2 and demonstrate the very good behavior of this recipe on a mono-like brick containing precipitates.
• the TTV varies between 5.2 pm and 8.5 pm, and as shown in figure 23, the good behavior of the wire when cutting, in particular the value of the deflection and the wrap are also close to those of the cut. reference.
• This exemplary embodiment makes it possible to understand how the cutting characteristics obtained with a wafer without precipitate can be reproduced with a wafer comprising precipitates by calculating at least one cutting parameter using a method according to a first aspect of the invention.

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• 2019
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