Patent Document

This application claims the priority of Provisional Application No. 60/088,567, filed Jun. 9, 1998. 
    
    
     SUMMARY OF THE INVENTION 
     This invention relates to non-destructive testing methods and apparati for determining the “cleanliness,” that is, degree of material internal purity, of metallic sputter target materials and, more particularly, non-destructive methods and apparati for determining cleanliness based on the sound propagation properties of the materials. 
     BACKGROUND OF THE INVENTION 
     Cathodic sputtering is widely used for depositing thin layers or films of materials from sputter targets onto desired substrates such as semiconductor wafers. Basically, a cathode assembly including a sputter target is placed together with an anode in a chamber filled with an inert gas, preferably argon. The desired substrate is positioned in the chamber near the anode with a receiving surface oriented normally to a path between the cathode assembly and the anode. A high voltage electric field is applied across the cathode assembly and the anode. 
     Electrons ejected from the cathode assembly ionize the inert gas. The electrical field then propels positively charged ions of the inert gas against a sputtering surface of the sputter target. Material dislodged from the sputter target by the ion bombardment traverses the chamber and deposits on the receiving surface of the substrate to form the thin layer or film. 
     One factor affecting the quality of the layer or film produced by a sputtering process is the “cleanliness” of the material from which the sputter target is made. The term “cleanliness” is widely used in the semiconductor industry, among others, to characterize high purity and ultra high purity materials. In common practice, “cleanliness” refers to the degree of material internal purity. Such impurities may be present, for example, as inclusions of impurity-rich phases surrounded by the sputter target material. Cleanliness is usually measured in units of particles per million (“ppm”) or particles per billion (“ppb”) which define a ratio between the number of contaminant atoms and the total number of atoms sampled. 
     Since the cleanliness of the material from which a sputter target is made affects the quality of layers of films produced using that target, it is obviously desirable to use relatively clean materials in fabricating sputter targets. This implies a need in the art for non-destructive techniques for selecting sputter target blanks of suitable cleanliness to produce high quality sputter targets. Known destructive test methods, such as glow discharge mass spectroscopy and LECO techniques, are not suitable for this purpose. 
     Another factor affecting the quality of the layer or film produced by a sputtering process is the presence of “flaws” in the sputter target material. As used herein, the term “flaws” refers to microscopic volumetric defects in the sputter target material, such as inclusions, pores, cavities and micro-laminations. Since flaws in a sputter target affect the quality of layers or films produced using that target, there exists a corresponding need in the art for non-destructive techniques for characterizing flaws present in sputter target materials. 
     FIG. 1 illustrates a prior art non-destructive ultrasonic “flaw” detection method for characterizing aluminum and aluminum alloy sputter target materials. The technique illustrated in FIG. 1 is similar to that suggested in Aluminium Pechiney PCT Application No. PCT/FR96/01959 for use in classifying aluminum or aluminum alloy blanks suitable for fabricating sputter targets based on the size and number of internal “decohesions” detected per unit volume of the blanks. 
     The prior art technique of FIG. 1 employed a pulse-echo method performed on a test sample  10  having a planar upper surface  12  and a parallel planar lower surface  14 . In accordance with this technique, focused ultrasonic transducer  16  irradiated a sequence of positions on the upper surface  12  of the test sample  10  with a single, short-duration, high-frequency ultrasound pulse  18  having a frequency of at least 5 MHz, and preferably 10-50 MHz. The ultrasonic transducer  16  then switched to a sensing mode and detected a series of echoes  20  induced by the ultrasound pulse  18 . 
     One factor contributing to these echoes  20  was scattering of sonic energy from the ultrasound pulse  18  by flaws  22  in the test sample  10 . By comparing the amplitudes of echoes induced in the test sample  10  with the amplitudes of echoes induced in reference samples (not shown) having compositions similar to that of the test sample  10  and blind, flat-bottomed holes of fixed depth and diameter, it was possible to detect and count flaws  22  in the test sample  10 . 
     The number of flaws detected by the technique of FIG. 1 had to be normalized in order to facilitate comparison between test samples of different size and geometry. Conventionally, the number of flaws was normalized by volume—that is, the sputter target materials were characterized in units of “flaws per cubic centimeter.” The volume associated with the echoes  20  from each irradiation of the test sample  10  was determined, in part, by estimating an effective cross-section of the pulse  18  in the test sample  10 . 
     One drawback to the technique of FIG. 1 is that a number of factors detract from the ability of the transducer  16  to detect sonic energy scattered by the flaws  22 . This reduces the sensitivity of the technique. 
     One such factor is relative weakness of the scattered energy. A portion of the scattered energy is attenuated by the material making up the test sample  10 . Furthermore, since the flaw sizes of interest, which range from approximately 0.04 mm to 0.1 mm, are significantly less than the wavelength of ultrasound in metals (for example, the wavelength of sound in aluminum for the frequency range of 10 MHz to 50 MHz is 0.6 mm to 0.12 mm, respectively), the pulse  18  has a tendency to refract around the flaws  22 , which reduces the scattering intensity. 
     Another factor detracting from the ability of the transducer  16  to detect the sonic energy scattered by the flaws  22  is the noise generated by scattering of the pulse  18  at the boundaries between grains having different textures. In fact, the texture-related noise can be so great for high-purity aluminum having grain sizes on the order of several millimeters that small flaws within a size range of approximately 0.05 mm and less cannot be detected. Larger grain sizes reduce the signal-to-noise ratio for the sonic energy scattered by the flaws when compared to the noise induced by the grain boundaries. 
     Other factors affecting the sensitivity and resolution of the technique of FIG. 1 includes the pulse frequency, duration and waveform; the degree of beam focus and the focal spot size; the coupling conditions (that is, the efficiency with which the sonic energy travels from the transducer  16  to the test sample  10 ); and the data acquisition system parameters. 
     Another drawback to the technique of FIG. 1 is that the calculation of the “flaws per cubic centimeter” in the test sample  10  presupposes that only flaws  22  within a determinable cross-sectional area scatter sonic energy back toward the transducer  16 . In fact, the pulse  18 , due to its wave nature, does not have localized, well-determined boundaries. 
     The distribution of the energy of the pulse  18  within the test sample  10 , under simplifying assumptions, permits one to define a corridor  30  having a determinable cross-section beneath the transducer  16  in which most of the energy should be concentrated. Nevertheless, some of the energy of the pulse  18  will propagate outside this corridor  30 . As a result, the transducer may detect sonic energy scattered by relatively large flaws  22  located outside the estimated corridor  30 , thereby overestimating the density of flaws  22  in the test sample  10  and underestimating their sizes. Therefore, material cleanliness characteristics become to some degree uncertain. 
     Thus, there remains a need in the art for non-destructive techniques for characterizing sputter target materials having greater sensitivity than methods in the prior art. There also remains a need for techniques which permit the comparison of the cleanliness of different sputter target materials in a manner which is not dependent on arbitrary volumetric estimations in the form “flow per cubic unit.” One conventional imaging technique for sputter target material is C-scanning. It maps the flaws on a two-dimensional image of the material sample. Where the size of the tested object is on the order of approximately ten centimeters or greater, however, it becomes difficult to indicate the relative sizes of flaws having diameters on the order of approximately 0.04 mm to 0.1 mm to any realistic scale. When computerized imaging is used it may be impossible to indicate the relative sizes of flaws in this manner where the sizes of the flaws relative to the entire width or diameter of the sample surface are less than the relative pixel sizes of the display device. 
     Therefore there remains an additional need in the art for an imaging technique which does not require the display of flaws scaled relative to the surface area of the test sample. 
     SUMMARY OF THE INVENTION 
     These needs and others are addressed by a non-destructive method for characterizing a sputter target material comprising the steps of sequentially irradiating a test sample of the sputter target material with sonic energy at a plurality of positions on a surface of the sample; detecting echoes induced by the sonic energy; discriminating texture-related backscattering noise from the echoes to obtain modified amplitude signals; comparing the modified amplitude signals with said at least one calibration value to detect flaw data points and no-flaw data points (that is, data points in which flaws were detected, and were not detected, respectively); counting the flaw data points to determine a flaw count C F ; counting the flaw data points and the no-flaw data points to determine a total number of data points C DP ; and calculating a cleanliness factor F C =(C F /C DP )×10 6 . 
     Unlike the prior art method described earlier, the method of the present invention provides a characterization of the sputter target material which is not dependent on theoretical estimates of the cross-sectional area occupied by the sonic energy during its flight through the test sample. Since the cleanliness factor is normalized by the number of data points rather than by estimated volume, there is less risk of overestimating the number of flaws, or underestimating their sizes, than in the prior art method of FIG.  1 . 
     Although the cleanliness factor provides a useful characterization of the sputter target material, more information can be provided by means of a histogram. More specifically, the sputter target may be characterized by defining a plurality of amplitude bands; measuring said modified amplitude signals to determine modified amplitude signal magnitudes; comparing said modified amplitude signal magnitudes with said plurality of amplitude bands to form subsets of said modified amplitude signals; counting said subsets of modified amplitude signals to determine a plurality of modified amplitude signal counts, each modified amplitude signal count of said plurality of amplitude signal counts corresponding to one of said amplitude bands of said plurality of amplitude bands; and constructing a histogram relating said modified signals counts to said plurality of amplitude bands. Since the histogram does not attempt to directly map the locations of flaws along the surface of the sputter target material, it does not suffer from the scaling problems inherent in prior art mapping techniques. 
     Most preferably, the test sample is compressed along one dimension, such as by rolling or forging, and then irradiated by sonic energy propagating transversely (that is, obliquely or, better yet, normally) to that dimension. This has the effect of flattening and widening any flaws in the material. The widening of the flaws, in turn, increases the intensity of the sonic energy scattered by the flaws and reduces the likelihood that the sonic energy will refract around the flaws. 
     These methods for characterizing sputter target materials may be used in processes for manufacturing sputter targets. As noted earlier, the cleanliness of a sputter target is one factor determining the quality of the layers or films produced by the target. By shaping only those sputter target blanks having cleanliness factors or histograms meeting certain reference criteria to form sputter targets, and rejecting blanks not meeting those criteria, one improves the likelihood that the sputter targets so manufactured will produce high quality layers or films. 
     Therefore, it is one object of the invention to provide non-destructive methods for characterizing sputter target materials. Other objects of the invention will be apparent from the follow description the accompanying drawings, and the appended claims. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a schematic view illustrating of prior art method of ultrasonic texture analysis; 
     FIG. 2 is a schematic view illustrating an especially preferred method of ultrasonic cleanliness characterization in accordance with the invention; 
     FIG. 3 is a schematic view of a test apparatus for carrying out the method of FIG. 2; 
     FIG. 4 is a histogram characterizing a relatively “clean” (“cleanliness factor” 183 flaw counts per million) Al-0.5 wt % Cu material in accordance with an especially preferred form of the invention; and 
     FIG. 5 is a histogram characterizing a less “clean” (“cleanliness factor” 1,714 flaw counts per million) Al-0.5 wt % Cu material in accordance with the especially preferred form of the invention. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     FIG. 2 illustrates an especially preferred method for characterizing the cleanliness of sputter target material. In accordance with this method, a cylindrical sample  50  of the sputter target material (which preferably comprises metal or a metal alloy) is compressed or worked to produce a disc-shaped test sample  52  having a planar upper surface  54  and a planar lower surface  56  approximately parallel to the upper surface  54 . Thereafter, a focused ultrasonic transducer  60  is positioned near the upper surface  54 . The transducer  60  irradiates the upper surface  54  of the test sample  52  with a single, short-duration, megahertz frequency range ultrasonic pulse  62 . The transducer  60  subsequently detects an echo  64  induced in the test sample  52  by the pulse  62 . The transducer  60  converts the echo into an electrical signal (not shown), which is processed for use in characterizing the test sample  52 . 
     More specifically, the sample  50  first is compressed along a dimension  70  to form the disc-shaped test sample  52 . Preferably, the sample  50  is compressed by forging or rolling of the sample  50 , followed by diamond cutting to prepare the planar surfaces  54  and  56 . The reduction in the dimension  70  may be anywhere between 0% to 100%. The compression of the sample  50  flattens and widens any flaws  72 , so as to increase their surface area normal to the dimension  70 . 
     As illustrated in FIG. 3, the test sample  52  then is immersed in deionized water (not shown) in a conventional immersion tank  80 . The transducer  60  is mounted on a mechanical X-Y scanner  82  in electrical communication with a controller  84  such as a PC controller. The controller  84  is programmed in a conventional manner to induce the mechanical X-Y scanning unit  82  to move the transducer  60  in a raster-like stepwise motion across the upper surface  54  of the test sample  52 . 
     The presently preferred transducer  60  is sold by Panametrics USA under the designation V 319. This is a high resolution piezoelectric transducer having a fixed focalization distance. At a center frequency of approximately 15 MHz with a 7.2 MHz (−6 dB) bandwidth, the transducer produces a pulse  62  having a focal distance of approximately 51 mm and a focal spot 12.5 mm in diameter. 
     Most preferably, the upper surface  54  of the sample  52  has a width or diameter on the order of approximately 7.5 inches (approximately 19 cm). Data acquisition steps of approximately 0.8 mm in both the “x”-direction and the “y”-directions permit the detection of 0.1 mm flat bottom holes at a detection level of −6 dB without exposure area overlap. One thereby irradiates approximately 50,000-500,000 test points on the upper surface  54 . 
     Most preferably, the transducer  60  is oriented so that the pulse  62  propagates through the deionized water (not shown) in the immersion tank  80  and strikes the test sample  52  approximately normally to the upper surface  54 . Furthermore, the transducer  60  is preferably spaced from the upper surface  54  such that the pulse  62  is focused on a zone  86  (FIG. 2) of the test sample  52  between approximately 3 mm and 6.2 mm below the upper surface  54 . The pulse  62  interacts with the sample  52  to induce echoes  64 , which then propagate back through the deionized water (not shown) to the transducer  60  approximately 60 μsec after the pulse is sent. 
     To increase the signal-to-noise ratio, the zone  86  (FIG. 2) in which the pulse  62  is focused should be located near the upper surface  54 . The waveform and duration of the pulse  62  should be chosen keeping in mind that very short pulses experience dispersion which smooths the pulse and makes the detection of small flaws more difficult. Therefore, it is preferred that the pulse  62  be a “Gaussian” wave packet including several cycles of oscillations. 
     An especially preferred echo acquisition system includes a low noise gated preamplifier  90 ; a low noise linear amplifier  92  with a set of calibrated attenuators with a signal (from 0.1 mm flat bottom hole)-to-noise (texture) ratio of 6 dB; and a 12-bit (2.44 mV/bit) analog-to-digital converter  94 . When sufficient time has elapsed for the echoes to arrive at the transducer  60 , the controller  84  switches the transducer  60  from a transmitting mode to a gated electronic receiving mode. The echoes  64  are received by the transducer  60  and converted into an RF electric amplitude signal (not shown). The amplitude signal is amplified by the preamplifier  90  and by the low noise linear amplifier  92  to produce a modified amplitude signal. The attenuators (not shown) associated with the low noise linear amplifier  92  attenuate a portion of the texture-related noise. The modified amplitude signal then is digitized by the analog-to-digital converter  94  before moving on to the controller  84 . The analog-to-digital conversion is performed so as to preserve amplitude information from the analog modified amplitude signal. 
     Flaws of given sizes are detected by comparing the digitized modified amplitude signals obtained from the sample  52  with reference values derived from tests conducted on reference samples (not shown) having compositions similar to that of the test sample  10  and blind flat-bottomed holes of fixed depth and diameter. 
     The especially-preferred PC controller  84  includes a microprocessor  100  programmed to control the data acquisition process. An especially preferred software package used in connection with the data acquisition system is available from Structural Diagnostics, Inc. under the designation SDI-5311 Winscan 4. 
     The microprocessor  100  is also programmed to calculate the cleanliness factor characterizing the material of the samples  50 ,  52 . More precisely, it is programmed to discriminate texture-related backscattering noise and to distinguish “flaw data points,” that is, digitized modified amplitude signals received from the analog-to-digital converter  94  representing amplitudes which, after comparison with the calibrations values, indicate the presence of flaws. One especially preferred method for discriminating texture related noise is to reject echoes having amplitudes less than an echo amplitude threshold. The microprocessor  100  maintains a count of the flaw data points detected during the testing of a test sample  52  to determine a flaw count “C F .” 
     The microprocessor  100  also is programmed to distinguish “no-flaw data points,” that is, digitized modified amplitude signals representing amplitudes which, after comparison with the calibration values, indicate the absence of flaws. 
     The microprocessor  100  also determines a total number of data points “C DP ,” that is, the sum of the flaw count C F  and the number of no-flaw data points. Although the total number of data points could be determined by maintaining counts of the flaw data points and the no-flaw data points, it is preferably determined by counting the total number of positions “C 1 ” along the upper surface  54  at which the test sample  52  is irradiated by the transducer  60  and subtracting the number of digitized RF signals “C N ” which the data acquisition circuitry was unable due to noise or other causes, to identify as either flaw data points or no-flaw data points. (Alternatively, the “noise count” C N  may be described as the number of positions along the upper surface  54  at which neither a flaw data point nor a no-flaw data point is detected.) 
     Having determined the flaw count C F  and the total number of data points C DP , the microprocessor is programmed to calculate the cleanliness factor F C =(C F /C DP )×10 6  to characterize the material comprising the samples  50 ,  52 . Unlike the prior art “flaws per cubic centimeter,” the magnitude of the cleanliness factor is not dependent on any estimate of pulse cross-sectional area Since the cleanliness factor is normalized by the dimensionless coefficient C DP ×10 −6  rather than by volume, it is more closely related to ppm and ppb units than are units of “flaws per cubic centimeter.” 
     The preparation of a suitable program for determining the cleanliness factor in accordance with the invention as disclosed herein is within the ordinary skill in the art and requires no undue experimentation. 
     Another way in which to characterize the material comprising the samples  50 ,  52  is by determining the size distribution of flaws in the test sample  52 . More specifically, one may characterize the cleanliness of the sample  52  by defining amplitude bands or ranges; comparing the amplitudes represented by the digitized modified amplitude signal magnitudes with the amplitude bands to form subsets of the modified amplitude signals; counting these subsets of modified amplitude signals to determine a modified amplitude signal counts for each amplitude band; and constructing a histogram relating the modified signal counts to said plurality of amplitude bands. Since the amplitudes represented by the digitized modified amplitude signals are related to the sizes of flaws detected in the sample  52 , the histogram provides an indication of the flaw size distribution in the sample  52 . 
     Turning now to FIGS. 4 and 5, there may be seen histograms characterizing two Al-0.5 wt % Cu alloy sputter target materials having orthorhombic textures and grain sizes in the range of 0.08 mm to 0.12 mm. The material of FIG. 4 was “cleaner” than that of FIG. 5; the material of FIG. 4 had a cleanliness factor of 183, while the material of FIG. 5 had a cleanliness factor signal of 1,714. The zone of flaw monitoring was located within a gate of 1 microsecond duration with a gate delay of 0.9 microseconds. 
     The abscissa  100  of the histogram of FIG. 4 represents amplitude normalized as a percentage of the echo amplitude induced in a reference sample having a 0.1 mm blind, flat-bottomed hole. The ordinate  102  in FIG. 4 represents the modified signal counts for each amplitude, expressed on a logarithmic scale. The echo amplitude threshold for the flaw counting was set to 48% since, as established experimentally, the texture-related echo amplitude did not exceed 45% for all aluminum alloys tested. The abscissa  104  and ordinate  106  of the histogram of FIG. 5 were scaled similarly. 
     The histograms of FIGS. 4 and 5 represent an improvement over prior art imaging techniques in that the distribution of flaw sizes may be represented without having to represent flaw sizes relative to the surface area of the test sample (not shown). 
     The preparation of a suitable program for plotting histograms such as those shown in FIGS. 4 and 5 in accordance with the invention as disclosed herein is within the ordinary skill in the art and requires no undue experimentation. 
     Either the cleanliness factor or histograms such as those shown in FIGS. 4 and 5 may be used in a process for manufacturing sputter targets. As noted earlier, the cleanliness of a sputter target is one factor determining the quality of the layers or films produced by the target. By shaping only those sputter target blanks having cleanliness factors less than reference cleanliness factors, or having histograms with selected columns or areas less than reference values, to form sputter targets, and rejecting blanks not meeting those criteria, one improves the likelihood that the sputter targets so manufactured will produce high quality layers or films. 
     While the method herein described, and the form of apparatus for carrying this method into effect, constitutes a preferred embodiment of this invention, it is to be understood that the invention is not limited to this precise method and form of apparatus, and that changes may be made in either without departing from the scope of the invention, which is defined in the appended claims.

Technology Category: 3