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Flow field-flow fractionation-inductively coupled plasma mass spectrometry of chemical mechanical polishing slurries
Spectrochimica Acta Part B 57 (2002) 1885–1896
Flow field-flow fractionation-inductively coupled plasma mass spectrometry of chemical mechanical polishing slurries夞 Atitaya Siripinyanonda,b, Ramon M. Barnesa,b,* a University Research Institute for Analytical Chemistry, 85 North Whitney Street, Amherst, MA 01002-1869, USA Department of Chemistry, 701 Lederle Graduate Research Towers, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003-9336, USA
b
Received 20 May 2002; accepted 12 September 2002
Abstract Alumina- and silica-based chemical mechanical polishing slurries were analyzed to demonstrate the feasibility of field-flow fractionation-inductively coupled plasma mass spectrometry (FFF-ICP-MS). After FFF separation 27 Al and 29 Si were measured by ICP-MS to obtain size distributions, mean particle size, number average-, mass average-, Z average- diameters, minimum and maximum particle sizes, dominant particle size, and particle size ranges (breadth of size distribution, and polydispersity) characteristics. Five commercial alumina and 13 silica slurry samples were characterized. Broad distributions were detected and two polydispersity calculations were compared. Most silica samples and one alumina sample show monomodal normal distributions. Asymmetric distributions were observed for a few silica and most alumina slurries. The degree of deviation from normal distribution was assessed. Mean particle sizes of alumina slurries varied between 150 and 350 nm with the maximum detected particle of less than 680 nm. Silica slurries exhibited maximum particle sizes of less than approximately 400 nm with the mean particle sizes ranging from 110 to 220 nm. Trace metals (Fe, Ti and Zr) coeluted with Al, Si; whereas, Pb appeared to be present as colloidal fractions. 䊚 2002 Elsevier Science B.V. All rights reserved. Keywords: Chemical mechanical polishing; Alumina slurry; Silica slurry; Flow field-flow fractionation; Inductively coupled plasma mass spectrometry
1. Introduction Chemical mechanical polishing (CMP) slurries are used in the semiconductor industry for polish夞 This paper was presented at the 7th Rio Symposium on ´ Atomic Spectrometry, held in Florianopolis, Brazil, April 2002 and is published in the Special Issue of Spectrochimica Acta Part B, dedicated to that conference. *Corresponding author. Fax: q1-413-256-3746. E-mail address: [email protected] (R.M. Barnes).
ing integrated circuit chips. The polishing process performance depends on the choice of abrasive materials, their particle sizes, particle concentration, and slurry pH values w1x. The abrasive particle sizes and size distributions must be carefully controlled to avoid surface defects during polishing w2x. Several analytical techniques have been applied to characterize particle size of CMP slurries. These techniques include acoustic attenuation spectroscopy w3,4x, electron microscopy w5x, sin-
0584-8547/02/$ - see front matter 䊚 2002 Elsevier Science B.V. All rights reserved. PII: S 0 5 8 4 - 8 5 4 7 Ž 0 2 . 0 0 2 0 7 - 0
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gle-particle optical sensing w6x, and light scattering detection w6x, among others. Acoustic attenuation w3,4x is supposed to provide accurate particle size information, because the sample is not diluted before analysis. Nonetheless, sample dilution is needed for other detector types. Therefore, a single detection approach often is inadequate to provide reliable information, and verification techniques should be applied. For example, field-flow fractionation (FFF) has been applied for size characterization of CMP materials. In most cases, however, FFF studies have dealt with proprietary materials and therefore have not been widely reported in the literature w7x. Field-flow fractionation is a gentle size separation technique applicable to both macromolecules and particles w8x. Different external field forces are applied to analyze various sample types. These external fields give rise to an assortment of FFF sub-techniques, including gravitational, sedimentation, flow, electrical, and thermal FFF. Sedimentation and flow FFF (FlFFF) are most commonly used. The particle size of silica nanocolloids was characterized by FlFFF w5,9x. Size distributions obtained were found to be consistent with size information obtained by electron microscopy. Sedimentation FFF was applied in characterizing submicrometer size colloidal silica w10–12x. Both colloidal silica and coarse silica materials used as chromatographic supports were examined by various FFF sub-techniques w13x. Gravitational FFF also has been proven for size distribution analysis of silica particles w14–16x. The technique was considered to be a standardless method providing quantitative particle size information w13x. Inductively coupled plasma mass spectrometry (ICP-MS) has been coupled as an element-specific detector after FFF separation w17–23x. Field-flow fractionation-ICP-MS is considered an ideal technique that provides elemental composition information across size distribution. This combination was first proposed by Beckett in 1991 w24x and implemented by Taylor et al. in 1992 w17x. Several applications of FFF-ICP-MS to environmental w18–22x and biological w23x samples have been reported. However, application of FFF-ICP-MS to industrial materials, although possible, has not been documented. In this investigation CMP slur-
ries are examined by FlFFF-ICP-MS to demonstrate the scope of the technique with industrial micromaterials. Particle size characteristics, including size distribution, minimum and maximum particle sizes, average and mean diameters, polydispersity, and breadth of distribution, are measured for 18 CMP slurry samples. Flow FFFICP-MS can be considered as a complementary tool to existing size characterization methodologies for CMP slurries. When information about trace impurities present as dissolved fractions or large particles is sought, FlFFF-ICP-MS is the preferred tool. To demonstrate this feature Fe, Pb, Ti and Zr are detected by ICP-MS after on-line FlFFF size separation to evaluate the nature of co-existing elements with alumina and silica CMP slurries. 2. Calculation of physicochemical parameters obtained from FlFFF experiments 2.1. Data transformation from retention time to hydrodynamic diameter Flow FFF separation is brought about by the balance between a cross-flow liquid, introduced into the FFF channel perpendicular to the channel flow stream, and the diffusivity of sample particles acting against the flow field force w25x. By knowing the exact FFF channel geometry, the hydrodynamic diameter can be calculated directly from the retention times of particles separated by FFF. Therefore, this approach is considered as a standardless size characterization technique. In practice hydrodynamic diameter (dh) can be calculated directly from the retention times (tr) by using Eq. (1). dhs2kTVtr yhpw2Vc
(1)
where k is the Boltzmann’s constant y16 g cm2 sy2 Ky1), T is absolute temperature (K), V is channel flow rate (cm3 sy1), h is the carrier liquid viscosity (g cmy1 sy1), w is the FlFFF channel thickness (cm), and Vc is cross-flow rate (cm3 sy1). To convert retention time to hydrodynamic diameter without standards of known diameters to calibrate the FlFFF channel, one must carefully determine the actual channel thickness. Here, we used the breakthrough volume to calculate the void
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volume. Knowing the experimentally void volume (V 0), the channel thickness (w) is then computed wwsV 0 ybL, where b is the channel breadth and L is the channel lengthx. 2.2. Number average-, mass average-, Z averageparticle sizes, and polydispersity Three average particle diameters are defined as follows: (1) the number average particle size (dn); (2) mass average particle size (dm); and (3) Zaverage particle size (dz). The Z-average particle size is related to sedimentation equilibrium of the particles. The ICP-MS detector signal (S) at each i interval is used to calculate these average diameters with Eqs. (2)–(4). dnswy8Siz~ y wy8Si ydiz~
(2)
dmswy8Sidiz~ y wy8diz~
(3)
x
x
|
x
|
|
x
|
dzswy8Sidi2z~ y wy8Sidiz~ x
|
x
|
(4)
Polydispersity is a measure of the peak homogeneity. Two approaches are used to determine polydispersity and results are compared. With the first approach the polydispersity index (PDI) is defined as a ratio of mass average particle size (dm) to number average particle size (dn) w26x. Another polydispersity characterization is given by the percent coefficient of variation (%CV), which is defined as a percentage of the ratio of the standard deviation (s) in particle diameter to the mean diameter (dmean) w5x. The s value is calculated by taking the peak width at 60.6% maximum peak height to be 2s w5x. This assumption holds for a normal distribution profile only. In this study another approach is proposed and used to assess the s value. In view of the statistical moments presented in the Gaussian distribution function, approximately 68.3% of a Gaussian area lies between ys and qs w27x. This implies that 2s is equal to 68.3% of the total area. The relationship between cumulative area and the diameter is plotted (Fig. 1), and the diameter range that yields the centered area of 68.3% is determined. The diameter range (2s) is measured by subtracting the diameter position at 15.85% total area from the
Fig. 1. An example of a cumulative area plot (sample S2). (A) Represents minimum particle size, which is determined from the point where cumulative area starts to deviate from zero. (B) Represents the diameter where 15.85% of the total area is detected. (C) Represents mean particle size, which is the point where a half of total cumulative area is detected. (D) represents the diameter where 84.15% of the total area is detected. The difference between point D and point B is equal to 2s. E represents maximum particle size, which is determined from the saturation point of cumulative area. Values for sample S2 are as follows: As47 nm, Bs105 nm, Cs157 nm, Ds217 nm, Es365 nm, ss56.
diameter position at 84.15% total area. wNote: 100y2Xs68.3, hence Xs15.85x. To illustrate a wide range of size information that can be obtained, an example of cumulative area plot is shown in Fig. 1 for sample S2. 3. Experimental 3.1. Instrumentation An FlFFF system (Model F-1000-FO, FFFractionation LLC, Salt Lake City, UT, USA) equipped with a 10 kDa molecular weight cut-off, regenerated cellulose acetate membrane (FFFractionation LLC) was used. The FlFFF channel was 27.7 cm long, 2.0 cm wide, and 0.02 cm thick. The slurry sample was injected directly into an injector valve (Rheodyne) with a fixed loop (20 ml) attached to the FlFFF channel front end. An HPLC pump (Model 6010, Hitachi Instruments, Inc., Stoughton, MA, USA) was used to regulate the channel flow at 1 ml miny1. Another HPLC pump (Model 300, Scientific Systems, Inc., State College, PA, USA) was employed to furnish the cross-flow rate (0.6 ml miny1). A UV detector (Model L 4000, Hitachi Instruments) was set at 254 nm to monitor light attenuation of the separated slurry particles. An
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Table 1 FFF-ICP-MS operating conditions Flow FFF Model F-1000-FO FFF channel dimensions (cm) Carrier liquid Channel flow rate (ml miny1) Cross flow rate (ml miny1) Equilibration time (min) Membrane
27.7 long=2.0 wide=0.02 thick 0.1% FL-70 anionic surfactant 1.0 0.6 4.0 10 kDa MWCO poly(regenerated cellulose acetate)
ICP-MS Elan 5000a Rf generator frequency (MHz) Rf forward power (W) Torch Torch injector Spray chamber Nebulizer Nebulizer gas flow rate (l miny1) Intermediate gas flow rate (l miny1) Outer gas flow rate (l miny1) Resolution Scanning mode Measurement per peak Dwell time (ms) Isotopes monitored (myz)
40 1000 Fassel type Ceramic alumina Ryton䉸 Scott double pass Gem-tip cross flow 0.98 0.96 14.5 1"0.1 at 10% peak maximum Peak hop transient signal 1 100 27 Al, 29Si, 48Ti, 57Fe, 90Zr, 208Pb
ICP-MS (SciexyElan 5000a, Perkin-Elmer Instruments, Shelton, CT) was used as an element detector sequentially after the UV absorption detector. Owing to the similarity of the FlFFF channel and ICP-MS sample flow rates typically used for analysis, an ICP-MS cross-flow nebulizer was connected directly to the UV detector outlet with a 50-cm length of poly(tetrafluoroethylene) tubing (PTFE, 0.3 mm i.d.). The experimental operating parameters are summarized in Table 1. Peaks were evaluated using PeakFit娃 (SPSS, Chicago, IL, USA). 3.2. Slurry samples Commercially available CMP slurry samples containing particles with hydrodynamic diameters of less than 600 nm were studied. These sometimes include proprietary mixtures of buffers, salts, surfactants, or other additives. Slurry samples (as received) were shaken vigorously, subsampled, and an aliquot was diluted with deionized water to a final concentration of approximately 0.1% solid (alumina slurries), or approximately 1–2% solid (silica slurries) before injection into the FlFFF
channel. The slurry samples and their percent solid contents present in the ‘as-received’ samples are listed in Table 2. 3.3. Carrier liquids Two carrier liquids were tested. A 30 mM Tris buffer was prepared by dissolving approximately 3.6 g of Tris(hydroxymethyl aminomethane) (Aldrich Chemical Company, Inc., Milwaukee, WI, catalog number T8, 760-2) in 1000 ml deionized water and adjusted to pH 7 with concentrated nitric acid. A 0.1% FL-70 anionic surfactant was prepared by diluting 1 ml of concentrated FL-70 (Fisher Scientific Co., Pittsburgh, PA, catalog number SF 105-1) to a final volume of 1000 ml with deionized water. Fresh carrier liquids were prepared daily. 3.4. Data treatment Raw fractograms were translated into size distribution profiles by applying an Excel (Microsoft䉸 Excel 2002, Redmond, WA) spreadsheet. PeakFit娃 (SPSS, Chicago, IL, USA) was
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Table 2 List of samples % Solid Alumina slurry A1 A2 A3 A4 A5
8.0 3.6 2.3 4.0 10.2
Silica slurry S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13
5.5 11.0 13.0 11.4 26.1 19.0 30.2 25.9 54.7 14.8 1.9 2.0 5.9
used to evaluate peaks, adjust baselines, and plot cumulative area. 4. Results and discussion 4.1. Choice of carrier liquid The carrier liquid must be compatible with the membrane and disperse slurry particles, and it
Fig. 3. Fractograms of silica slurry (sample S13). (a) (dark line) is a silicon fractogram, for which the 29 Si count rate axis (on the left) is used. (b) (gray line) is an UV-based fractogram, for which the absorption at 254 nm axis (on the right) is used. A shift in retention times is observed with the UV detector. Peak diameters are 156 nm and 180 nm from silicon and absorption fractograms, respectively. Note: an unresolved fraction (*) was detected with a UV detector.
should not interact with particles or change particle size characteristics. An FL-70 anionic surfactant has been used previously as a particle dispersing reagent and carrier liquid for FlFFF w5,13x. Tris buffer (pH 7) and FL-70 surfactant were compared as carrier liquids for a silica slurry with known particle size (sample S5, Fig. 2). The Tris buffer caused delayed elution and larger peak particle diameter than expected, which might be a consequence of particle agglomeration. The FL-70 gives an accurate peak diameter and is the preferred carrier liquid used throughout this work. 4.2. Fractograms of alumina and silica slurries
Fig. 2. Silicon fractograms of silica slurry (sample S5). (a) (dark line) is a fractogram of silica slurry when 0.1% FL-70 was used as carrier liquid. (b) (gray line) is a fractogram of silica slurry when 30 mM Tris was used as carrier liquid. Diameters at peak are 143 nm and 198 nm for FL-70 and Tris carrier liquids, respectively. The reported value by an independent laboratory for sample S5 is 143 nm. Void volume peak appears at 60 s.
The UV absorption- and Si-based fractograms obtained for a silica slurry (sample S13) are shown in Fig. 3. In the absorption fractogram a small, unresolved fraction was observed before the entire size distribution (*, Fig. 3). This fraction could be due to organic additives present in the slurry solution, although this hypothesis requires verification. Furthermore, a slight shift of the UV fractogram peak to larger particle size compared to the Si fractogram was observed. A similar shift was reported by Taylor et al. for Al2O3 particles w17x. They explained that the light scattering intensity of smaller particles in this size range was less on a per mass basis relative to larger particles, and this could possibly be the reason for the observed
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4.3. Particle size characteristics Particle size at peak maximum (dp) is used to identify the dominant particle sizes of the slurry samples investigated. To measure a breadth of size distributions, particle size ranges at half maximum height (Dd0.5) from distribution profiles are calculated. The measured dp and Dd0.5 values for alumina and silica CMP slurries are summarized in Table 3. To obtain the mean (dmean), minimum (dmin), and maximum (dmax) particle sizes present in a slurry sample, the diameter distribution profile is converted into a cumulative area plot as shown in Fig. 1. Mean, minimum, and maximum particle sizes values are determined from this cumulative plot. The values of dmean, dmin and dmax, are
Fig. 4. Size distributions of alumina slurry samples. (A) Aluminum fractogram of sample A2 (peak diameter at 396 nm). (B) Aluminum fractograms of alumina slurries. The dark line (a) shows size distribution of sample A4 (peak diameter at 148 nm). The gray line (b) shows size distribution of sample A3 (two peak diameters at 251 and 394 nm).
shift in the absorption fractogram. The UV detector response is due to both light absorption and Mie theory. This shift supports a value of alternative detector types we.g. light scattering detector or element specific detectors like inductively coupled plasma atomic emission spectrometry (ICP-AES) or ICP-MSx to obtain accurate particle size characterization. Fractograms of diluted alumina and silica slurry samples are transformed into diameter distributions by applying Eq. (1). Size distributions of selected slurry samples are illustrated in Figs. 4 and 5. Only one (sample A4) of five alumina slurries showed a normal distribution. In contrast, most silica slurries (except samples S4 and S6) yielded normal distributions. An anomalous distribution can indicate multiple groups of particle sizes, and multiple peaks appear when particles of different sizes are resolved. Broad size distributions are observed for all samples studied. These fractograms are quantified to obtain particle size information.
Fig. 5. Size distributions of silica slurry samples. (A) Silicon fractograms of silica slurries. The dark line (a) shows size distribution of sample S2 (peak diameter at 146 nm). The gray line (B) shows size distribution of sample S9 (peak diameter at 217 nm). (b) Silicon fractogram of sample S4 (peak diameters at 100 and 202 nm). (C) Silicon fractogram of sample S6 (peak diameters at 80 and 207 nm).
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Table 3 Particle size information dp (nm)
Dd0.5 (nm)
dmean (nm)
dmin (nm)
dmax (nm)
dn (nm)
dm (nm)
dz (nm)
dnydp
Alumina slurry A1 A2a A3b A4c A5*
395 396 251, 394 148 260, 500
214 76 263 131 399
348 348 292 158 304
28 103 98 42 80
601 488 467 350 680
274 298 263 150 n.d.
338 327 291 155 n.d.
375 350 316 170 n.d.
0.69 0.75 0.67 1.01 n.d.
Silica slurry S1 S2d S3 S4e S5f S6g S7 S8 S9h S10 S11 S12 S13i
149 146 169 100, 202 140 80, 207 191 158 217 146 143 169 156
109 136 151 93 133 258 202 160 141 134 159 149 144
161 157 177 117 151 189 199 175 221 161 177 175 165
40 47 33 32 38 27 32 33 75 34 38 37 37
360 365 385 295 380 400 403 402 381 355 400 367 350
149 142 156 107 136 141 168 153 208 143 158 156 148
169 162 180 125 157 189 204 183 224 167 190 179 170
188 181 201 145 177 226 233 210 239 189 220 200 190
1.00 0.97 0.92 1.07 0.97 0.68 0.88 0.97 0.96 0.98 1.10 0.92 0.95
n.d., not determined. Noisy signal was detected for sample A5*. This might be due to additives that are used for stabilizing slurry sample. a Fig. 4A. b Fig. 4Bb, Fig. 7, and Fig. 8. c Fig. 4Ba. d Fig. 1, and Fig. 5Aa. e Fig. 5B. f Fig. 2, and Fig. 5Aa. g Fig. 5C, and Fig. 9. h Fig. 5Ab, and Fig. 6. i Fig. 3.
summarized in Table 3. From Eqs. (2)–(4), the average diameters (dn, dm and dz) were calculated for all samples. These results also are summarized in Table 3. Among the 18 samples, dmean and dmax were determined by an independent laboratory for two silica slurries (S1 and S5). The dmean and dmax values of 139 and 337 nm, and 143 and 337 nm were reported for S1 and S5 samples, respectively. Our dmean and dmax values agree closely with these data (Table 3). For samples that exhibit normal size distributions, dm and dz values are shifted to the larger diameter size relative to dmean, because larger particles are more heavily weighted than smaller particles. In contrast, dn values are shifted toward
smaller diameter size compared to dmean, because no mass factor is involved in the dn calculation. The differences in the average particle diameters indicate the importance of identifying the type of size information being reported, particularly when dealing with highly polydisperse materials. To emphasize these differences, four size distribution profiles showing area distribution (diameter distribution that is directly translated from raw fractogram), number distribution, mass distribution, and Z (sedimentation) distribution are illustrated in Figs. 6 and 7. The normal size distributions of a silica slurry (sample S9) are presented in Fig. 6. Peak maximum shifts to the larger diameter sizes in the following order: number distribution-area
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pendent methods (e.g. light scattering, SdFFF) will be used to verify size distributions and the contribution of steric retention for slurries containing large particles. 4.4. Polydispersity (PDI and %CV)
Fig. 6. Normal size distributions of silica slurry (sample S9). Arrows indicate peak maximum. (A) Number distribution (dark line, a) and mass distribution (gray line, b). (B) Area distribution (dark line, a) and Z distribution (gray line, b).
distribution-weight distribution-Z distribution. The broad asymmetrical (bimodal) size distributions of an alumina slurry (sample A3) is shown. Upon close inspection of the area distribution, two peak position maxima were observed. The first peak (smaller diameter size) is more pronounced than the second peak (larger diameter size) in the number distribution. The second peak, compared to the first one, becomes more distinct when the mass average- and the Z average-size distributions are plotted. Mixed mode retention (i.e. normal and steric modes) might occur in this separation leading to the anomalous distribution. Steric inversion is a phenomenon in which larger size particles elute earlier than smaller particles. When particles are retained in the steric mode, a fronting peak shape often is observed. For example, the area distribution (Fig. 7Ba) could result from steric inversion that would lead to inaccurate particle size information if ignored. To avoid steric effects the sample was filtered to remove particles )600 nm before sample injection. However, the CMP slurry particle size distribution might be altered during sample pretreatment, and in future studies inde-
Separation of nanoparticles by FlFFF generates concentration profiles that are broadened by the particle polydispersity. Small diameter particles elute in the fronting edge of the profile, whereas the large diameter sizes elute in the tailing edge of the profile. This results in a broad peak, which is the sum of a continuum of unresolved peaks w28x. The peak broadening contribution from polydispersity can be characterized by FlFFF. In this study two definitions of polydispersity (PDI and %CV) were tested, and the polydispersity values are summarized in Table 4. For the %CV definition of polydispersity, s is calculated two ways. One is a proposed method, which assesses the s value by finding the centered particle size range that yields 68.3% of the total area. The other method, which determines the s value by measuring the peak width at 60.6% maximum peak height w5x, is used for comparison only.
Fig. 7. Non-normal size distributions of alumina slurry (sample A3). Arrows indicate peak maximum. (A) Number distribution (dark line, a) and mass distribution (gray line, b). (B) Area distribution (dark line, a) and Z distribution (gray line, b).
A. Siripinyanond, R.M. Barnes / Spectrochimica Acta Part B 57 (2002) 1885–1896 Table 4 Polydispersity of CMP slurries PDI (dmydn)
Polydispersity (%CV68.3)a
Polydispersity (%CV60.6)b
Alumina slurry A1 A2 A3 A4 A5
1.24 1.10 1.11 1.03 n.d.
32.5 26.6 32.7 31.3 n.d.
24.4 11.2 44.0 32.3 n.d.
Silica slurry S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13
1.13 1.14 1.16 1.17 1.15 1.34 1.21 1.20 1.08 1.17 1.20 1.15 1.15
34.5 35.7 35.9 42.7 37.4 48.7 41.7 41.7 26.9 37.6 44.6 35.1 36.4
28.0 36.3 38.1 33.3 38.7 59.8 44.0 38.9 26.9 34.2 34.5 37.1 37.9
n.d., not determined. Noisy signal was detected for sample S5. a Calculated by finding the centered particle size range that yields 68.3% of the total area. b Calculated by measuring the peak width at 60.6% maximum peak height.
A dissimilarity of polydispersity values calculated by these two methods (PDI and %CV) is observed. However, the values show similar trend for silica slurry samples. Upon examining PDI values, samples can be classified into three groups: highly polydisperse (dm ydn)1.3, or %CV)45); moderately polydisperse (1.1-dm ydn-1.3, or 30-%CV-45); and slightly polydisperse (dm y dn-1.1, or %CV-30). The polydispersity index from dm ydn values increases in the following sample order: S6 )S7 ;S8 ;S11 ;S4 ;S10 ;S3 ;S5 ;S12 ;S13 ;S2 ;S1 )S9. Similarly, the polydispersity calculated by using the %CV definition (both approaches) increases in almost the same order. Switching in the polydispersity order was observed for a few moderately polydisperse samples. The difference in polydispersity values indicates that the definition used in the calculation should be strictly specified. Since
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the similar trends are detected by both indicators (dw ydn, and %CV), either polydispersity definition can be used. In the A2 aluminum sample fractogram (Fig. 4A), an asymmetric distribution is observed. This bimodal distribution suggests the presence of more than one group of particles in the sample. The polydispersity value should be high when more than one species is present. However, the values calculated by all definitions for non-normal size distribution particles seem to be lower than expected. The polydispersity (%CV60.6), in which s is calculated by measuring the peak width at 60.6% maximum peak height, is prone to significantly more error compared to %CV68.3, in which s is calculated by 68.3% of the total area. Therefore, polydispersity calculated for abnormal distributions are suspect. 4.5. Deviation distribution
from
monomodal
normal
For a Gaussian size distribution the calculated number average particle diameter (dn) should agree well with the detected particle size at peak (dp). The ratio between number average particle size (dn) and particle size at peak (dp), as summarized in Table 3, is proposed here as a rapid quantitative measure of deviation from normal distribution. Other accurate ways exist to perform statistical test for normal distribution. Here, the dn ydp is used as a quick measure, because the values are readily available. A ratio of 1 indicates a symmetrical normal distribution profile, whereas a ratio deviating from 1 suggests a presence of distribution asymmetry. The dn ydp values of most silica slurries are close to unity indicating monomodal normal distribution profiles. Silica sample S6 exhibited a ratio of approximately 0.7. Among the five alumina slurry samples, only sample A4 yielded a ratio close to 1. The ratios of approximately 0.7 were calculated for other alumina samples indicating deviation from normal distribution. This suggests that two or more groups of particle sizes coexist in these slurries. To evaluate this hypothesis, size distribution peaks of one alumina slurry (sample A3) were deconvolved using the PeakFit娃 program. Three groups of particle sizes
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and Zr was observed with Si in some silica slurries (Fig. 9). Most CMP slurries are used for polishing the surfaces of integrated chips and other types of semiconductor products, and therefore the concentrations of trace impurities in the CMP slurries must be carefully controlled. Obtaining information indicating whether those impurities are present as dissolved fractions or associated with the slurry particles is useful. Since these elements are present in trace levels, an ‘as-received’ slurry sample was injected directly into the FlFFF channel without dilution or pretreatment. As a consequence, sample overloading may cause peak distortion and inaccurate particle size distributions. Nonetheless, this experiment was undertaken to examine only the presence of co-existing elements with silicon, and not to characterize their exact particle sizes. An example of trace elements fractograms of a silica slurry is illustrated in Fig. 9. Titanium, Al, Fe, Zr and Si exhibit similar fractograms. Early elution of Pb relatively to other elements suggests the formation of lead-nanocolloids in the slurry solution. Lead is, therefore, not considered to be a Fig. 8. An example of peak deconvolution (sample A3). (A) Size distribution of an alumina slurry (peak diameters at 251 and 394 nm). (B) Three deconvolved Gaussian peaks of sample A3. The (a) line shows the peak maximum at 226 nm (dns221, dms239, dzs258 nm, dmydns1.09). The (b) line shows the peak maximum at 305 nm (dns303, dms309, dzs 314 nm, dmydns1.02). The (c) line shows the peak maximum at 387 nm (dns386, dms389, dzs392 nm, dmydns1.01). (C) Summation of peaks a, b, and c (B). This results in a convoluted peak that resembles the original size distribution (A).
exist as shown in Fig. 8. This suggests that the anomalous size distribution of sample A3 consists of three individual normal size distributions. The dp values from the deconvoluted peaks (230 and 381 nm) are smaller than the observed dp values (251 and 394 nm) from the original size distribution. The dn ydp values for these three deconvolved peaks are 0.98, 0.99 and 1.0, indicating the Gaussian nature of the peaks. 4.6. Co-elution of trace metals By using ICP-MS as a simultaneous, elementspecific FFF detector, co-elution of Al, Fe, Ti, Pb
Fig. 9. Ion fractograms of silica slurry (sample S6) showing co-elution of trace elements with silica particles. (A) Titanium (dark line, a), Pb (gray line, b), and Fe (q, c) fractograms. (B) Aluminum (dark line, a), and Zr (gray line, b) fractograms. Total quantitative determination of element concentrations shows the presence of Pb at ;5 mg, Fe, Ti and Zr at ;20 mg, and Al at ;100 mg per 1 g of dried solid sample.
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silica particle constituent. However, because Al, Fe, Ti or Zr co-eluted with the silica particles, they most likely are constituents of the particles. Further investigation should be undertaken to identify the physical forms of these elements in the silica slurry. Chemical pretreatment by acid leaching or acid digestion is suggested. Quantitative recovery from acid leaching should be obtained if the elements were present as weakly adsorbed forms. Poor recovery is expected if the elements were strongly adsorbed or constituents of solid particles.
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any materials. Polishing efficiency of CMP slurry may depend on the particle mass as well. Upon close examination of average diameters values, dm and dmean are quite similar. In all cases, differences of dm from dmean values are less than 7%. Although co-elution of major and trace elements can be examined, no attempt was made to distinguish inorganic particles from organic macromolecule additives in this study. The presence of lead-nanocolloids deserves further investigation. The application of FFF-ICP-MS to other commercial CMP slurries, (e.g. ceria slurry) also should be investigated.
5. Conclusion Acknowledgments A novel application of FlFFF-ICP-MS to CMP slurries illustrates the capability of the technique for industrial micromaterials. Since UV spectrophotometry is subjected to error caused by Mie scattering, ICP-MS detection provides more reliable particle size distribution information. Several useful particle characteristics can be extracted from a single fractogram dataset. Two definitions of polydispersity are compared to determine peak homogeneity. Our preference is to use the dm ydn value, because the degree of deviation from monodispersity (dm ydn ideally is equal to 1) can be evaluated immediately. To obtain accurate dm ydn or %CV values, the band broadening contribution from the laminar flow profile should be corrected. This can be accomplished by running a standard monodisperse particle. The dm ydn or %CV values are then calculated and subtracted from the dm ydn or %CV values of unknown samples w5x. Although other investigators have made this correction, it is ignored here, because band broadening contributes only slightly to samples that are highly polydisperse. Since the polydispersity values from nonnormal distribution (mainly those calculated from %CV definition) can be misleading, we suggest that a criterion of dn ydp ranging between 0.8 and 1.2 be met. When the dn ydp lies outside this range, the size distribution pattern should be carefully inspected. Peak deconvolution may be applied before the particle size information is acquired. Considering the average particle sizes, we recommend that the mass average diameter (dm) be reported since mass is an important property of
Thanks are due to Dr Eva Reitznerova´ for providing the values of percent solid and elements concentrations in the slurry samples. A.S. wishes to acknowledge the Royal Thai government for her studentship funded through the Ministry of University Affairs, Thailand. This work was supported by ICP Information Newsletter, Inc. (Hadley, MA, USA). References w1x B.J. Palla, D.O. Shah, Stabilization of high ionic strength slurries using the synergistic effects of a mixed surfactant system, J. Colloid Interface Sci. 223 (2000) 102–111. w2x G.B. Basim, J.J. Adler, U. Mahajan, R.K. Singh, B.M. Moudgil, Effect of particle size of chemical mechanical polishing slurries for enhances polishing with minimal defects, J. Electrochem. Soc. 147 (2000) 3523–3528. w3x A.S. Dukhin, P.J. Goetz, Characterization of chemical polishing materials (monomodal and bimodal) by means of acoustic spectroscopy, Colloids Surf. 158 (1999) 343–354. w4x A.S. Dukhin, P.J. Goetz, Acoustic and electroacoustic spectroscopy characterizing concentrated dispersions emulsions, Adv. Colloid Interface Sci. 92 (2001) 73–132. w5x S.K. Ratanathanawongs, J.C. Giddings, Rapid size characterization of chromatographic silicas by flow fieldflow fractionation, Chromatographia 38 (1994) 545–554. w6x D. Nicoli, K. Hasapidis, P. O’Hagen, G. Pokrajac, B. Schade, Particle size analysis of colloidal suspensions by SPOS compared to DLS: a sensitive indicator of quality and stability, Am. Lab. 33 (2001) 32–39.
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w7x B.N. Barman, in: M.E. Schimpf, K. Caldwell, J.C. Giddings (Eds.), Field-Flow Fractionation Handbook, Wiley, New York, NY, 2000, pp. 373–382. w8x M.N.J. Myers, Overview of field-flow fractionation, J. Microcolumn Sep. 9 (1997) 151–162. w9x J.C. Giddings, G.C. Lin, M.N. Myers, Fractionation and size analysis of colloidal silica by flow field-flow fractionation, J. Colloid Interface Sci. 65 (1978) 67–78. w10x J.J. Kirkland, W.W. Yau, W.A. Doerner, J.W. Grant, Sedimentation field-flow fractionation of macromolecules and colloids, Anal. Chem. 52 (1980) 1944–1954. w11x J.J. Kirkland, C.H. Dilks Jr., W.W. Yau, Sedimentation field-flow fractionation at high force fields, J. Chromatogr. 255 (1983) 255–271. w12x C.R. Yonker, H.K. Jones, D.M. Robertson, Nonaqueous sedimentation field-flow fractionation, Anal. Chem. 59 (1987) 2573–2579. w13x J.C. Giddings, S.K. Ratanathanawongs, B.N. Barman, M.H. Moon, G. Liu, B.L. Tjelta, M.E. Hansen, in: H.E. Bergna (Ed.), The Colloid Chemistry of Silica, Advances in Chemistry Series, 234, American Chemical Society, Washington, DC, 1994, pp. 309–340. w14x P. Reschiglian, D. Melucci, G. Torsi, A. Zattoni, Standardless method for quantitative particle-size distribution studies by gravitational field-flow fractionation: application to silica particles, Chromatographia 51 (2000) 87–94. w15x J. Pazourek, J. Chmelik, Characterization of chromatographic silica gel support particles by gravitational fieldflow fractionation, J. Microcolumn Sep. 9 (1997) 611–617. w16x P. Reschiglian, D. Melucci, G. Torsi, Experimental study of the retention of silica particles in gravitational fieldflow fractionation: effects of the mobile phase composition, J. Chromatogr. A 740 (1996) 245–252. w17x H.E. Taylor, J.R. Garbarino, D.M. Murphy, R. Beckett, Inductively coupled plasma-mass spectrometry as an element-specific detector for field-flow fractionation particle separation, Anal. Chem. 64 (1992) 2036–2041. w18x D.M. Murphy, J.R. Garbarino, H.E. Taylor, B.T. Hart, R. Beckett, Determination of size and element composition distributions of complex colloids by sedimentation
w19x
w20x
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w27x w28x
field-flow fractionation-inductively coupled plasmamass spectrometry, J. Chromatogr. 642 (1993) 459–467. J.F. Ranville, D.J. Chittleborough, F. Shanks, R.J.S. Morrison, T. Harris, F. Doss, R. Beckett, Development of sedimentation field-flow fractionation- inductively coupled plasma mass spectrometry for the characterization of environmental colloids, Anal. Chim. Acta 381 (1999) 315–329. ¨ B. Lyven, ´ C. Haraldsson, W. Sirinawin, M. Hassellov, Determination of continuous size and trace element distribution of colloidal materials in natural water by on-line coupling of flow field-flow fractionation with ICP-MS, Anal. Chem. 71 (1999) 3497–3502. B. Chen, C.A. Shand, R. Beckett, Determination of total and EDTA extractable metal distributions in the colloidal fraction of contaminated soils using SdFFF-ICPHRMS, J. Environ. Monit. 3 (2001) 7–14. D. Amarasiriwardena, A. Siripinyanond, R.M. Barnes, Trace elemental distribution in soil and compost-derived humic acid molecular fractions and colloidal organic matter in municipal wastewater by flow field-flow fractionation- inductively coupled plasma mass spectrometry (flow FFF-ICP-MS), J. Anal. At. Spectrom. 16 (2001) 978–986. A. Siripinyanond, R.M. Barnes, Flow field-flow fractionation-inductively coupled plasma mass spectrometer and metal speciation in proteins: a feasibility study, J. Anal. At. Spectrom. 14 (1999) 1527–1531. R. Beckett, Field-flow fractionation-ICP-MS: a powerful new analytical tool for characterizing macromolecules and particles, At. Spectrosc. 12 (1991) 228–232. S.K. Ratanathanawongs-Williams, in: M.E. Schimpf, K. Caldwell, J.C. Giddings (Eds.), Field-Flow Fractionation Handbook, Wiley, New York, NY, 2000, pp. 257–277. M.E. Schimpf, in: M.E. Schimpf, K. Caldwell, J.C. Giddings (Eds.), Field-Flow Fractionation Handbook, Wiley, New York, NY, 2000, pp. 239–256. J.C. Giddings, Unified Separation Science, Wiley, New York, NY, 1991, pp. 90–92. J.M. Davis, in: M.E. Schimpf, K. Caldwell, J.C. Giddings (Eds.), Field-Flow Fractionation Handbook, Wiley, New York, NY, 2000, pp. 49–70.
Flow field-flow fractionation-inductively coupled plasma mass spectrometry of chemical mechanical polishing slurries夞 Atitaya Siripinyanonda,b, Ramon M. Barnesa,b,* a University Research Institute for Analytical Chemistry, 85 North Whitney Street, Amherst, MA 01002-1869, USA Department of Chemistry, 701 Lederle Graduate Research Towers, University of Massachusetts, 710 North Pleasant Street, Amherst, MA 01003-9336, USA
b
Received 20 May 2002; accepted 12 September 2002
Abstract Alumina- and silica-based chemical mechanical polishing slurries were analyzed to demonstrate the feasibility of field-flow fractionation-inductively coupled plasma mass spectrometry (FFF-ICP-MS). After FFF separation 27 Al and 29 Si were measured by ICP-MS to obtain size distributions, mean particle size, number average-, mass average-, Z average- diameters, minimum and maximum particle sizes, dominant particle size, and particle size ranges (breadth of size distribution, and polydispersity) characteristics. Five commercial alumina and 13 silica slurry samples were characterized. Broad distributions were detected and two polydispersity calculations were compared. Most silica samples and one alumina sample show monomodal normal distributions. Asymmetric distributions were observed for a few silica and most alumina slurries. The degree of deviation from normal distribution was assessed. Mean particle sizes of alumina slurries varied between 150 and 350 nm with the maximum detected particle of less than 680 nm. Silica slurries exhibited maximum particle sizes of less than approximately 400 nm with the mean particle sizes ranging from 110 to 220 nm. Trace metals (Fe, Ti and Zr) coeluted with Al, Si; whereas, Pb appeared to be present as colloidal fractions. 䊚 2002 Elsevier Science B.V. All rights reserved. Keywords: Chemical mechanical polishing; Alumina slurry; Silica slurry; Flow field-flow fractionation; Inductively coupled plasma mass spectrometry
1. Introduction Chemical mechanical polishing (CMP) slurries are used in the semiconductor industry for polish夞 This paper was presented at the 7th Rio Symposium on ´ Atomic Spectrometry, held in Florianopolis, Brazil, April 2002 and is published in the Special Issue of Spectrochimica Acta Part B, dedicated to that conference. *Corresponding author. Fax: q1-413-256-3746. E-mail address: [email protected] (R.M. Barnes).
ing integrated circuit chips. The polishing process performance depends on the choice of abrasive materials, their particle sizes, particle concentration, and slurry pH values w1x. The abrasive particle sizes and size distributions must be carefully controlled to avoid surface defects during polishing w2x. Several analytical techniques have been applied to characterize particle size of CMP slurries. These techniques include acoustic attenuation spectroscopy w3,4x, electron microscopy w5x, sin-
0584-8547/02/$ - see front matter 䊚 2002 Elsevier Science B.V. All rights reserved. PII: S 0 5 8 4 - 8 5 4 7 Ž 0 2 . 0 0 2 0 7 - 0
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gle-particle optical sensing w6x, and light scattering detection w6x, among others. Acoustic attenuation w3,4x is supposed to provide accurate particle size information, because the sample is not diluted before analysis. Nonetheless, sample dilution is needed for other detector types. Therefore, a single detection approach often is inadequate to provide reliable information, and verification techniques should be applied. For example, field-flow fractionation (FFF) has been applied for size characterization of CMP materials. In most cases, however, FFF studies have dealt with proprietary materials and therefore have not been widely reported in the literature w7x. Field-flow fractionation is a gentle size separation technique applicable to both macromolecules and particles w8x. Different external field forces are applied to analyze various sample types. These external fields give rise to an assortment of FFF sub-techniques, including gravitational, sedimentation, flow, electrical, and thermal FFF. Sedimentation and flow FFF (FlFFF) are most commonly used. The particle size of silica nanocolloids was characterized by FlFFF w5,9x. Size distributions obtained were found to be consistent with size information obtained by electron microscopy. Sedimentation FFF was applied in characterizing submicrometer size colloidal silica w10–12x. Both colloidal silica and coarse silica materials used as chromatographic supports were examined by various FFF sub-techniques w13x. Gravitational FFF also has been proven for size distribution analysis of silica particles w14–16x. The technique was considered to be a standardless method providing quantitative particle size information w13x. Inductively coupled plasma mass spectrometry (ICP-MS) has been coupled as an element-specific detector after FFF separation w17–23x. Field-flow fractionation-ICP-MS is considered an ideal technique that provides elemental composition information across size distribution. This combination was first proposed by Beckett in 1991 w24x and implemented by Taylor et al. in 1992 w17x. Several applications of FFF-ICP-MS to environmental w18–22x and biological w23x samples have been reported. However, application of FFF-ICP-MS to industrial materials, although possible, has not been documented. In this investigation CMP slur-
ries are examined by FlFFF-ICP-MS to demonstrate the scope of the technique with industrial micromaterials. Particle size characteristics, including size distribution, minimum and maximum particle sizes, average and mean diameters, polydispersity, and breadth of distribution, are measured for 18 CMP slurry samples. Flow FFFICP-MS can be considered as a complementary tool to existing size characterization methodologies for CMP slurries. When information about trace impurities present as dissolved fractions or large particles is sought, FlFFF-ICP-MS is the preferred tool. To demonstrate this feature Fe, Pb, Ti and Zr are detected by ICP-MS after on-line FlFFF size separation to evaluate the nature of co-existing elements with alumina and silica CMP slurries. 2. Calculation of physicochemical parameters obtained from FlFFF experiments 2.1. Data transformation from retention time to hydrodynamic diameter Flow FFF separation is brought about by the balance between a cross-flow liquid, introduced into the FFF channel perpendicular to the channel flow stream, and the diffusivity of sample particles acting against the flow field force w25x. By knowing the exact FFF channel geometry, the hydrodynamic diameter can be calculated directly from the retention times of particles separated by FFF. Therefore, this approach is considered as a standardless size characterization technique. In practice hydrodynamic diameter (dh) can be calculated directly from the retention times (tr) by using Eq. (1). dhs2kTVtr yhpw2Vc
(1)
where k is the Boltzmann’s constant y16 g cm2 sy2 Ky1), T is absolute temperature (K), V is channel flow rate (cm3 sy1), h is the carrier liquid viscosity (g cmy1 sy1), w is the FlFFF channel thickness (cm), and Vc is cross-flow rate (cm3 sy1). To convert retention time to hydrodynamic diameter without standards of known diameters to calibrate the FlFFF channel, one must carefully determine the actual channel thickness. Here, we used the breakthrough volume to calculate the void
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volume. Knowing the experimentally void volume (V 0), the channel thickness (w) is then computed wwsV 0 ybL, where b is the channel breadth and L is the channel lengthx. 2.2. Number average-, mass average-, Z averageparticle sizes, and polydispersity Three average particle diameters are defined as follows: (1) the number average particle size (dn); (2) mass average particle size (dm); and (3) Zaverage particle size (dz). The Z-average particle size is related to sedimentation equilibrium of the particles. The ICP-MS detector signal (S) at each i interval is used to calculate these average diameters with Eqs. (2)–(4). dnswy8Siz~ y wy8Si ydiz~
(2)
dmswy8Sidiz~ y wy8diz~
(3)
x
x
|
x
|
|
x
|
dzswy8Sidi2z~ y wy8Sidiz~ x
|
x
|
(4)
Polydispersity is a measure of the peak homogeneity. Two approaches are used to determine polydispersity and results are compared. With the first approach the polydispersity index (PDI) is defined as a ratio of mass average particle size (dm) to number average particle size (dn) w26x. Another polydispersity characterization is given by the percent coefficient of variation (%CV), which is defined as a percentage of the ratio of the standard deviation (s) in particle diameter to the mean diameter (dmean) w5x. The s value is calculated by taking the peak width at 60.6% maximum peak height to be 2s w5x. This assumption holds for a normal distribution profile only. In this study another approach is proposed and used to assess the s value. In view of the statistical moments presented in the Gaussian distribution function, approximately 68.3% of a Gaussian area lies between ys and qs w27x. This implies that 2s is equal to 68.3% of the total area. The relationship between cumulative area and the diameter is plotted (Fig. 1), and the diameter range that yields the centered area of 68.3% is determined. The diameter range (2s) is measured by subtracting the diameter position at 15.85% total area from the
Fig. 1. An example of a cumulative area plot (sample S2). (A) Represents minimum particle size, which is determined from the point where cumulative area starts to deviate from zero. (B) Represents the diameter where 15.85% of the total area is detected. (C) Represents mean particle size, which is the point where a half of total cumulative area is detected. (D) represents the diameter where 84.15% of the total area is detected. The difference between point D and point B is equal to 2s. E represents maximum particle size, which is determined from the saturation point of cumulative area. Values for sample S2 are as follows: As47 nm, Bs105 nm, Cs157 nm, Ds217 nm, Es365 nm, ss56.
diameter position at 84.15% total area. wNote: 100y2Xs68.3, hence Xs15.85x. To illustrate a wide range of size information that can be obtained, an example of cumulative area plot is shown in Fig. 1 for sample S2. 3. Experimental 3.1. Instrumentation An FlFFF system (Model F-1000-FO, FFFractionation LLC, Salt Lake City, UT, USA) equipped with a 10 kDa molecular weight cut-off, regenerated cellulose acetate membrane (FFFractionation LLC) was used. The FlFFF channel was 27.7 cm long, 2.0 cm wide, and 0.02 cm thick. The slurry sample was injected directly into an injector valve (Rheodyne) with a fixed loop (20 ml) attached to the FlFFF channel front end. An HPLC pump (Model 6010, Hitachi Instruments, Inc., Stoughton, MA, USA) was used to regulate the channel flow at 1 ml miny1. Another HPLC pump (Model 300, Scientific Systems, Inc., State College, PA, USA) was employed to furnish the cross-flow rate (0.6 ml miny1). A UV detector (Model L 4000, Hitachi Instruments) was set at 254 nm to monitor light attenuation of the separated slurry particles. An
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Table 1 FFF-ICP-MS operating conditions Flow FFF Model F-1000-FO FFF channel dimensions (cm) Carrier liquid Channel flow rate (ml miny1) Cross flow rate (ml miny1) Equilibration time (min) Membrane
27.7 long=2.0 wide=0.02 thick 0.1% FL-70 anionic surfactant 1.0 0.6 4.0 10 kDa MWCO poly(regenerated cellulose acetate)
ICP-MS Elan 5000a Rf generator frequency (MHz) Rf forward power (W) Torch Torch injector Spray chamber Nebulizer Nebulizer gas flow rate (l miny1) Intermediate gas flow rate (l miny1) Outer gas flow rate (l miny1) Resolution Scanning mode Measurement per peak Dwell time (ms) Isotopes monitored (myz)
40 1000 Fassel type Ceramic alumina Ryton䉸 Scott double pass Gem-tip cross flow 0.98 0.96 14.5 1"0.1 at 10% peak maximum Peak hop transient signal 1 100 27 Al, 29Si, 48Ti, 57Fe, 90Zr, 208Pb
ICP-MS (SciexyElan 5000a, Perkin-Elmer Instruments, Shelton, CT) was used as an element detector sequentially after the UV absorption detector. Owing to the similarity of the FlFFF channel and ICP-MS sample flow rates typically used for analysis, an ICP-MS cross-flow nebulizer was connected directly to the UV detector outlet with a 50-cm length of poly(tetrafluoroethylene) tubing (PTFE, 0.3 mm i.d.). The experimental operating parameters are summarized in Table 1. Peaks were evaluated using PeakFit娃 (SPSS, Chicago, IL, USA). 3.2. Slurry samples Commercially available CMP slurry samples containing particles with hydrodynamic diameters of less than 600 nm were studied. These sometimes include proprietary mixtures of buffers, salts, surfactants, or other additives. Slurry samples (as received) were shaken vigorously, subsampled, and an aliquot was diluted with deionized water to a final concentration of approximately 0.1% solid (alumina slurries), or approximately 1–2% solid (silica slurries) before injection into the FlFFF
channel. The slurry samples and their percent solid contents present in the ‘as-received’ samples are listed in Table 2. 3.3. Carrier liquids Two carrier liquids were tested. A 30 mM Tris buffer was prepared by dissolving approximately 3.6 g of Tris(hydroxymethyl aminomethane) (Aldrich Chemical Company, Inc., Milwaukee, WI, catalog number T8, 760-2) in 1000 ml deionized water and adjusted to pH 7 with concentrated nitric acid. A 0.1% FL-70 anionic surfactant was prepared by diluting 1 ml of concentrated FL-70 (Fisher Scientific Co., Pittsburgh, PA, catalog number SF 105-1) to a final volume of 1000 ml with deionized water. Fresh carrier liquids were prepared daily. 3.4. Data treatment Raw fractograms were translated into size distribution profiles by applying an Excel (Microsoft䉸 Excel 2002, Redmond, WA) spreadsheet. PeakFit娃 (SPSS, Chicago, IL, USA) was
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Table 2 List of samples % Solid Alumina slurry A1 A2 A3 A4 A5
8.0 3.6 2.3 4.0 10.2
Silica slurry S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13
5.5 11.0 13.0 11.4 26.1 19.0 30.2 25.9 54.7 14.8 1.9 2.0 5.9
used to evaluate peaks, adjust baselines, and plot cumulative area. 4. Results and discussion 4.1. Choice of carrier liquid The carrier liquid must be compatible with the membrane and disperse slurry particles, and it
Fig. 3. Fractograms of silica slurry (sample S13). (a) (dark line) is a silicon fractogram, for which the 29 Si count rate axis (on the left) is used. (b) (gray line) is an UV-based fractogram, for which the absorption at 254 nm axis (on the right) is used. A shift in retention times is observed with the UV detector. Peak diameters are 156 nm and 180 nm from silicon and absorption fractograms, respectively. Note: an unresolved fraction (*) was detected with a UV detector.
should not interact with particles or change particle size characteristics. An FL-70 anionic surfactant has been used previously as a particle dispersing reagent and carrier liquid for FlFFF w5,13x. Tris buffer (pH 7) and FL-70 surfactant were compared as carrier liquids for a silica slurry with known particle size (sample S5, Fig. 2). The Tris buffer caused delayed elution and larger peak particle diameter than expected, which might be a consequence of particle agglomeration. The FL-70 gives an accurate peak diameter and is the preferred carrier liquid used throughout this work. 4.2. Fractograms of alumina and silica slurries
Fig. 2. Silicon fractograms of silica slurry (sample S5). (a) (dark line) is a fractogram of silica slurry when 0.1% FL-70 was used as carrier liquid. (b) (gray line) is a fractogram of silica slurry when 30 mM Tris was used as carrier liquid. Diameters at peak are 143 nm and 198 nm for FL-70 and Tris carrier liquids, respectively. The reported value by an independent laboratory for sample S5 is 143 nm. Void volume peak appears at 60 s.
The UV absorption- and Si-based fractograms obtained for a silica slurry (sample S13) are shown in Fig. 3. In the absorption fractogram a small, unresolved fraction was observed before the entire size distribution (*, Fig. 3). This fraction could be due to organic additives present in the slurry solution, although this hypothesis requires verification. Furthermore, a slight shift of the UV fractogram peak to larger particle size compared to the Si fractogram was observed. A similar shift was reported by Taylor et al. for Al2O3 particles w17x. They explained that the light scattering intensity of smaller particles in this size range was less on a per mass basis relative to larger particles, and this could possibly be the reason for the observed
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4.3. Particle size characteristics Particle size at peak maximum (dp) is used to identify the dominant particle sizes of the slurry samples investigated. To measure a breadth of size distributions, particle size ranges at half maximum height (Dd0.5) from distribution profiles are calculated. The measured dp and Dd0.5 values for alumina and silica CMP slurries are summarized in Table 3. To obtain the mean (dmean), minimum (dmin), and maximum (dmax) particle sizes present in a slurry sample, the diameter distribution profile is converted into a cumulative area plot as shown in Fig. 1. Mean, minimum, and maximum particle sizes values are determined from this cumulative plot. The values of dmean, dmin and dmax, are
Fig. 4. Size distributions of alumina slurry samples. (A) Aluminum fractogram of sample A2 (peak diameter at 396 nm). (B) Aluminum fractograms of alumina slurries. The dark line (a) shows size distribution of sample A4 (peak diameter at 148 nm). The gray line (b) shows size distribution of sample A3 (two peak diameters at 251 and 394 nm).
shift in the absorption fractogram. The UV detector response is due to both light absorption and Mie theory. This shift supports a value of alternative detector types we.g. light scattering detector or element specific detectors like inductively coupled plasma atomic emission spectrometry (ICP-AES) or ICP-MSx to obtain accurate particle size characterization. Fractograms of diluted alumina and silica slurry samples are transformed into diameter distributions by applying Eq. (1). Size distributions of selected slurry samples are illustrated in Figs. 4 and 5. Only one (sample A4) of five alumina slurries showed a normal distribution. In contrast, most silica slurries (except samples S4 and S6) yielded normal distributions. An anomalous distribution can indicate multiple groups of particle sizes, and multiple peaks appear when particles of different sizes are resolved. Broad size distributions are observed for all samples studied. These fractograms are quantified to obtain particle size information.
Fig. 5. Size distributions of silica slurry samples. (A) Silicon fractograms of silica slurries. The dark line (a) shows size distribution of sample S2 (peak diameter at 146 nm). The gray line (B) shows size distribution of sample S9 (peak diameter at 217 nm). (b) Silicon fractogram of sample S4 (peak diameters at 100 and 202 nm). (C) Silicon fractogram of sample S6 (peak diameters at 80 and 207 nm).
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Table 3 Particle size information dp (nm)
Dd0.5 (nm)
dmean (nm)
dmin (nm)
dmax (nm)
dn (nm)
dm (nm)
dz (nm)
dnydp
Alumina slurry A1 A2a A3b A4c A5*
395 396 251, 394 148 260, 500
214 76 263 131 399
348 348 292 158 304
28 103 98 42 80
601 488 467 350 680
274 298 263 150 n.d.
338 327 291 155 n.d.
375 350 316 170 n.d.
0.69 0.75 0.67 1.01 n.d.
Silica slurry S1 S2d S3 S4e S5f S6g S7 S8 S9h S10 S11 S12 S13i
149 146 169 100, 202 140 80, 207 191 158 217 146 143 169 156
109 136 151 93 133 258 202 160 141 134 159 149 144
161 157 177 117 151 189 199 175 221 161 177 175 165
40 47 33 32 38 27 32 33 75 34 38 37 37
360 365 385 295 380 400 403 402 381 355 400 367 350
149 142 156 107 136 141 168 153 208 143 158 156 148
169 162 180 125 157 189 204 183 224 167 190 179 170
188 181 201 145 177 226 233 210 239 189 220 200 190
1.00 0.97 0.92 1.07 0.97 0.68 0.88 0.97 0.96 0.98 1.10 0.92 0.95
n.d., not determined. Noisy signal was detected for sample A5*. This might be due to additives that are used for stabilizing slurry sample. a Fig. 4A. b Fig. 4Bb, Fig. 7, and Fig. 8. c Fig. 4Ba. d Fig. 1, and Fig. 5Aa. e Fig. 5B. f Fig. 2, and Fig. 5Aa. g Fig. 5C, and Fig. 9. h Fig. 5Ab, and Fig. 6. i Fig. 3.
summarized in Table 3. From Eqs. (2)–(4), the average diameters (dn, dm and dz) were calculated for all samples. These results also are summarized in Table 3. Among the 18 samples, dmean and dmax were determined by an independent laboratory for two silica slurries (S1 and S5). The dmean and dmax values of 139 and 337 nm, and 143 and 337 nm were reported for S1 and S5 samples, respectively. Our dmean and dmax values agree closely with these data (Table 3). For samples that exhibit normal size distributions, dm and dz values are shifted to the larger diameter size relative to dmean, because larger particles are more heavily weighted than smaller particles. In contrast, dn values are shifted toward
smaller diameter size compared to dmean, because no mass factor is involved in the dn calculation. The differences in the average particle diameters indicate the importance of identifying the type of size information being reported, particularly when dealing with highly polydisperse materials. To emphasize these differences, four size distribution profiles showing area distribution (diameter distribution that is directly translated from raw fractogram), number distribution, mass distribution, and Z (sedimentation) distribution are illustrated in Figs. 6 and 7. The normal size distributions of a silica slurry (sample S9) are presented in Fig. 6. Peak maximum shifts to the larger diameter sizes in the following order: number distribution-area
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pendent methods (e.g. light scattering, SdFFF) will be used to verify size distributions and the contribution of steric retention for slurries containing large particles. 4.4. Polydispersity (PDI and %CV)
Fig. 6. Normal size distributions of silica slurry (sample S9). Arrows indicate peak maximum. (A) Number distribution (dark line, a) and mass distribution (gray line, b). (B) Area distribution (dark line, a) and Z distribution (gray line, b).
distribution-weight distribution-Z distribution. The broad asymmetrical (bimodal) size distributions of an alumina slurry (sample A3) is shown. Upon close inspection of the area distribution, two peak position maxima were observed. The first peak (smaller diameter size) is more pronounced than the second peak (larger diameter size) in the number distribution. The second peak, compared to the first one, becomes more distinct when the mass average- and the Z average-size distributions are plotted. Mixed mode retention (i.e. normal and steric modes) might occur in this separation leading to the anomalous distribution. Steric inversion is a phenomenon in which larger size particles elute earlier than smaller particles. When particles are retained in the steric mode, a fronting peak shape often is observed. For example, the area distribution (Fig. 7Ba) could result from steric inversion that would lead to inaccurate particle size information if ignored. To avoid steric effects the sample was filtered to remove particles )600 nm before sample injection. However, the CMP slurry particle size distribution might be altered during sample pretreatment, and in future studies inde-
Separation of nanoparticles by FlFFF generates concentration profiles that are broadened by the particle polydispersity. Small diameter particles elute in the fronting edge of the profile, whereas the large diameter sizes elute in the tailing edge of the profile. This results in a broad peak, which is the sum of a continuum of unresolved peaks w28x. The peak broadening contribution from polydispersity can be characterized by FlFFF. In this study two definitions of polydispersity (PDI and %CV) were tested, and the polydispersity values are summarized in Table 4. For the %CV definition of polydispersity, s is calculated two ways. One is a proposed method, which assesses the s value by finding the centered particle size range that yields 68.3% of the total area. The other method, which determines the s value by measuring the peak width at 60.6% maximum peak height w5x, is used for comparison only.
Fig. 7. Non-normal size distributions of alumina slurry (sample A3). Arrows indicate peak maximum. (A) Number distribution (dark line, a) and mass distribution (gray line, b). (B) Area distribution (dark line, a) and Z distribution (gray line, b).
A. Siripinyanond, R.M. Barnes / Spectrochimica Acta Part B 57 (2002) 1885–1896 Table 4 Polydispersity of CMP slurries PDI (dmydn)
Polydispersity (%CV68.3)a
Polydispersity (%CV60.6)b
Alumina slurry A1 A2 A3 A4 A5
1.24 1.10 1.11 1.03 n.d.
32.5 26.6 32.7 31.3 n.d.
24.4 11.2 44.0 32.3 n.d.
Silica slurry S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13
1.13 1.14 1.16 1.17 1.15 1.34 1.21 1.20 1.08 1.17 1.20 1.15 1.15
34.5 35.7 35.9 42.7 37.4 48.7 41.7 41.7 26.9 37.6 44.6 35.1 36.4
28.0 36.3 38.1 33.3 38.7 59.8 44.0 38.9 26.9 34.2 34.5 37.1 37.9
n.d., not determined. Noisy signal was detected for sample S5. a Calculated by finding the centered particle size range that yields 68.3% of the total area. b Calculated by measuring the peak width at 60.6% maximum peak height.
A dissimilarity of polydispersity values calculated by these two methods (PDI and %CV) is observed. However, the values show similar trend for silica slurry samples. Upon examining PDI values, samples can be classified into three groups: highly polydisperse (dm ydn)1.3, or %CV)45); moderately polydisperse (1.1-dm ydn-1.3, or 30-%CV-45); and slightly polydisperse (dm y dn-1.1, or %CV-30). The polydispersity index from dm ydn values increases in the following sample order: S6 )S7 ;S8 ;S11 ;S4 ;S10 ;S3 ;S5 ;S12 ;S13 ;S2 ;S1 )S9. Similarly, the polydispersity calculated by using the %CV definition (both approaches) increases in almost the same order. Switching in the polydispersity order was observed for a few moderately polydisperse samples. The difference in polydispersity values indicates that the definition used in the calculation should be strictly specified. Since
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the similar trends are detected by both indicators (dw ydn, and %CV), either polydispersity definition can be used. In the A2 aluminum sample fractogram (Fig. 4A), an asymmetric distribution is observed. This bimodal distribution suggests the presence of more than one group of particles in the sample. The polydispersity value should be high when more than one species is present. However, the values calculated by all definitions for non-normal size distribution particles seem to be lower than expected. The polydispersity (%CV60.6), in which s is calculated by measuring the peak width at 60.6% maximum peak height, is prone to significantly more error compared to %CV68.3, in which s is calculated by 68.3% of the total area. Therefore, polydispersity calculated for abnormal distributions are suspect. 4.5. Deviation distribution
from
monomodal
normal
For a Gaussian size distribution the calculated number average particle diameter (dn) should agree well with the detected particle size at peak (dp). The ratio between number average particle size (dn) and particle size at peak (dp), as summarized in Table 3, is proposed here as a rapid quantitative measure of deviation from normal distribution. Other accurate ways exist to perform statistical test for normal distribution. Here, the dn ydp is used as a quick measure, because the values are readily available. A ratio of 1 indicates a symmetrical normal distribution profile, whereas a ratio deviating from 1 suggests a presence of distribution asymmetry. The dn ydp values of most silica slurries are close to unity indicating monomodal normal distribution profiles. Silica sample S6 exhibited a ratio of approximately 0.7. Among the five alumina slurry samples, only sample A4 yielded a ratio close to 1. The ratios of approximately 0.7 were calculated for other alumina samples indicating deviation from normal distribution. This suggests that two or more groups of particle sizes coexist in these slurries. To evaluate this hypothesis, size distribution peaks of one alumina slurry (sample A3) were deconvolved using the PeakFit娃 program. Three groups of particle sizes
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and Zr was observed with Si in some silica slurries (Fig. 9). Most CMP slurries are used for polishing the surfaces of integrated chips and other types of semiconductor products, and therefore the concentrations of trace impurities in the CMP slurries must be carefully controlled. Obtaining information indicating whether those impurities are present as dissolved fractions or associated with the slurry particles is useful. Since these elements are present in trace levels, an ‘as-received’ slurry sample was injected directly into the FlFFF channel without dilution or pretreatment. As a consequence, sample overloading may cause peak distortion and inaccurate particle size distributions. Nonetheless, this experiment was undertaken to examine only the presence of co-existing elements with silicon, and not to characterize their exact particle sizes. An example of trace elements fractograms of a silica slurry is illustrated in Fig. 9. Titanium, Al, Fe, Zr and Si exhibit similar fractograms. Early elution of Pb relatively to other elements suggests the formation of lead-nanocolloids in the slurry solution. Lead is, therefore, not considered to be a Fig. 8. An example of peak deconvolution (sample A3). (A) Size distribution of an alumina slurry (peak diameters at 251 and 394 nm). (B) Three deconvolved Gaussian peaks of sample A3. The (a) line shows the peak maximum at 226 nm (dns221, dms239, dzs258 nm, dmydns1.09). The (b) line shows the peak maximum at 305 nm (dns303, dms309, dzs 314 nm, dmydns1.02). The (c) line shows the peak maximum at 387 nm (dns386, dms389, dzs392 nm, dmydns1.01). (C) Summation of peaks a, b, and c (B). This results in a convoluted peak that resembles the original size distribution (A).
exist as shown in Fig. 8. This suggests that the anomalous size distribution of sample A3 consists of three individual normal size distributions. The dp values from the deconvoluted peaks (230 and 381 nm) are smaller than the observed dp values (251 and 394 nm) from the original size distribution. The dn ydp values for these three deconvolved peaks are 0.98, 0.99 and 1.0, indicating the Gaussian nature of the peaks. 4.6. Co-elution of trace metals By using ICP-MS as a simultaneous, elementspecific FFF detector, co-elution of Al, Fe, Ti, Pb
Fig. 9. Ion fractograms of silica slurry (sample S6) showing co-elution of trace elements with silica particles. (A) Titanium (dark line, a), Pb (gray line, b), and Fe (q, c) fractograms. (B) Aluminum (dark line, a), and Zr (gray line, b) fractograms. Total quantitative determination of element concentrations shows the presence of Pb at ;5 mg, Fe, Ti and Zr at ;20 mg, and Al at ;100 mg per 1 g of dried solid sample.
A. Siripinyanond, R.M. Barnes / Spectrochimica Acta Part B 57 (2002) 1885–1896
silica particle constituent. However, because Al, Fe, Ti or Zr co-eluted with the silica particles, they most likely are constituents of the particles. Further investigation should be undertaken to identify the physical forms of these elements in the silica slurry. Chemical pretreatment by acid leaching or acid digestion is suggested. Quantitative recovery from acid leaching should be obtained if the elements were present as weakly adsorbed forms. Poor recovery is expected if the elements were strongly adsorbed or constituents of solid particles.
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any materials. Polishing efficiency of CMP slurry may depend on the particle mass as well. Upon close examination of average diameters values, dm and dmean are quite similar. In all cases, differences of dm from dmean values are less than 7%. Although co-elution of major and trace elements can be examined, no attempt was made to distinguish inorganic particles from organic macromolecule additives in this study. The presence of lead-nanocolloids deserves further investigation. The application of FFF-ICP-MS to other commercial CMP slurries, (e.g. ceria slurry) also should be investigated.
5. Conclusion Acknowledgments A novel application of FlFFF-ICP-MS to CMP slurries illustrates the capability of the technique for industrial micromaterials. Since UV spectrophotometry is subjected to error caused by Mie scattering, ICP-MS detection provides more reliable particle size distribution information. Several useful particle characteristics can be extracted from a single fractogram dataset. Two definitions of polydispersity are compared to determine peak homogeneity. Our preference is to use the dm ydn value, because the degree of deviation from monodispersity (dm ydn ideally is equal to 1) can be evaluated immediately. To obtain accurate dm ydn or %CV values, the band broadening contribution from the laminar flow profile should be corrected. This can be accomplished by running a standard monodisperse particle. The dm ydn or %CV values are then calculated and subtracted from the dm ydn or %CV values of unknown samples w5x. Although other investigators have made this correction, it is ignored here, because band broadening contributes only slightly to samples that are highly polydisperse. Since the polydispersity values from nonnormal distribution (mainly those calculated from %CV definition) can be misleading, we suggest that a criterion of dn ydp ranging between 0.8 and 1.2 be met. When the dn ydp lies outside this range, the size distribution pattern should be carefully inspected. Peak deconvolution may be applied before the particle size information is acquired. Considering the average particle sizes, we recommend that the mass average diameter (dm) be reported since mass is an important property of
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