Anal. Chem. l W Q ,61, 65-73
termine if a flat base line can be maintained without variation in sulfur signal by using feedback control between the pressure imposed on the column and the flow rate of hydrogen.
ACKNOWLEDGMENT We thank Eric Stahlberg for his help in developing s o h a r e to make the 3-D plots shown in this paper. We also thank Eric Wright for his assistance in developing the data acquisition software. LITERATURE CITED (1) Brody, S. S.; Chaney, J. E. J . Qas Chromafogr. 1988, 4 . 42-46. (2) McGuffln, V. L.; Novotny, M. Anal. Chem. 1981, 53, 946-951. (3) Olesk, S. V.; French, S. B.; Novotny, M. Ctuomatogfaphla 1984, 78, 489-496. (4) Shafer, K. H.; Griffiths, P. R. Anal. Chem. 198% 55, 1939-1942. (5) Smith, R. D.; Kallnoski. H. T.; Udseth, H. R. Mass Spectrom. Rev. 1987, 8 , 445-496. (6) Markldes, K. E.; Lee, E. D.; Bolick, R.; Lee, M. L. Anal. Chem. 1988, 58, 740-743. (7) Muller, C. H., 111; Schofieid, K.; Steinberg, M.; Brokla, H. P. Inf. Symposium on Combustion. 1879, 77, 667-879. (8) Gilbert, P. T. In Analytical Flame Spectroscopy; Mavrodlneanu, R., Ed.; Springer-Verlag: New York, 1970. ~ 2nd ed.: ChaDman and (9) Qavdon. A. 0. The S ~ c t r o s. c _ oof~Flames, M i : London, 1974. ' (10) Dagnall, R. M.; Thompson, K. C.; West, T. S. Anakst 1987, 9 2 , 508-512. - - - - .-. (11) Fair, R. W.; Thrush, B. A. Trans. Faradey Soc.1969, 65, 1208-1218. (12) Sugiyama, T.; Surukl, Y.; Takeuchi, T. J . Chromatogr. 1973, 7 7 , 309-3 16. (13) Burnett, C. H.; Adams. D. F.; Farweii, S. 0. J . Ckomrogr. Scl. 1978. 16, 68-73. (14) Deming, S. N.; Morgan, S. L. Anal. Chem. 1973, 45, 278A-283A. (15) Box, G. E. P.; Hunter, W. 0.; Hunter, J. S. StaHstics for €xperlmenters; Wlley: New York, 1978.
65
(16) Dixon, I?.N. Roc. R . Soc. London, A 1983, 275, 431-446. (17) Parse. R. W. B.; Gaydon. A. G. The IdenMcaHOn ofMoleculer Specha, 4th ed.; Wiley: New York, 1976. (18) Simpson, C. J. S. M.; Gait, P. D.; Simmle, J. M. Roc. R. Soc. London A 1978, 348, 73-60. (19) Sathyamurthy. N.; Raff, L. M. J . Chem. Phys. 1977, 66, 2191-2210. (20) Juvet, R. S., Jr.; Durbln, R. P. Anal. Chem. 1988, 38, 565-569. (21) Parsons. M. L.; Smith. B. W.; Bentley. G. E. Handbook of Flame Spectroscopy; Plenum: New York, 1965. (22) Danlels, F.; Aiberty, R. A. Physical Chemlstry; Wiley: New York. 1975; Vol. 4. (23) Smith, M. Y. J . Inst. Fuel 1989, 42, 248-250. (24) Reigie, L. L.; W a r t h y , W. J.; Ling, A. C. J . Chem. Eng. Data 1973, 78, 79-86. (25) Dixon-Lewis, G. Roc. R. Soc. London, A 1972, 330, 219-245. (26) Russell, K. E.;' Simons, J. Roc. R. Soc. London, A 1953, 277, 271-279. (27) Suglyania, T.; Suzuki. Y.; Takeuchi, T. J . Chmatogr. 1973, 80, 61-67. (28) Maruyama, M.; Kakemoto, M. J . Chmmafogr. Scl. 1978, 76. 1-7. (29) Buffington. R.; Wilson. M. K. Detectws for Gas chnwnatognrphy-A Racifcel Rimer; Hewlett-Packard Palo Alto, CA, 1987. (30) Julin. E. G.; Vandenborn, H. W.; Kirkland, J. J. J . C h m a t c g r . 1975, 772, 443-453. (31) Luffer, D. R.; Galante, L. J.; David, P. A.; Novotny, M.; Hieftje, 0. M. Anal. Chem. 1988, 6 0 , 1365-1369. (32) Pekay, L. A.; Oleslk. S. V. Analysis of Nonvolatile Drugs by SFC1FPD; in preparation for submission to J . Chromatogr.
'
RECEIVED for review August 8,1988. Accepted October 11, 1988. Financial support for this project was provided by the US.Department of Energy, Pittsburgh Energy and Technology Center, No. DE-AC-87PC79887;the Ohio Coal Development Office, No. R-86-026-01-OH, and the Ohio Mineral and Mining Resource Research Institute for a fellowship given to L. A. Pekay.
Detector Discrimination in Ion Microscopic Images: Characterization and Correction Yong-Chien Ling, Lisa K. Turner, Mark T. Bernius,l and George H. Morrison*
Baker Laboratory of Chemistry, Cornel1 University, Ithaca, New York 14853-1301
Secondary Ion mass spectrometric images that conialn contrast artlfacts caused by detector dlscrhninatlons are studied from the quantlflcatlon standpolnt. The Image detector conslsts of an Ion detectlng microchannel plate and a proxlmlty focused fluorescent screen coupled to a video or 35mm camera for image recording. The orlglns and characterlstlcs of the various contrast artlfacts are reported with partlcuiar reference to the dlfferences caused by the recording devlce used, thus provldlng a generallzed methodology to select the approprlate detector for Image quantlflcatlon. Based on an evaluatlon of the various correctlon schemes, desired attrlbUtes of a new image detector along with advanced Image processing technlques for Improving quantlflcatlon accuracy are given.
INTRODUCTION The unique feature of high analytical sensitivity together with good spatial and depth resolution has made ion microscopy, based on secondary ion mass spectrometry (SIMS), an ideal tool for spatially resolved elemental analysis (1,2). Current address: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109. 0003-2700/89/0361-0065$01.50/0
In microelectronics applications, for example, ion microscopy is used to resolve minute contaminants for failure analysis and to characterize dopant distribution for process control (3-5). "here are many applications in the life sciences for which ion microscopy is used to map the distribution of physiologically important elements (6,7).The information derived from ion images is crucial to investigate the relationships among a microfeature's elemental composition, physical structure, and property. The ion image, in the digital form, is represented as an array of integers where the array indices register the position of a feature in the object field and the array values represent the corresponding ion intensity a t a given mass to charge ratio. Ideally, for analytical image quantification,the index and value of each picture element (pixel) should correspond to a known function of the location and number of ions emitted from the sample surface. In practice, however, wide departures from this idealization are not uncommon. Distortion of the spatial integrity and intensity fidelity in an ion image results in various forms of contrast artifacts whose sources are sample imperfections and/or instrumental discriminations. The presence of contrast artifacts induced by sample imperfections, such as surface roughness and crystallographic structure, poses a common problem. Topographic contrast resulting from surface relief, for example, often develops during the sputter analysis and cannot be detected easily. This 0 1988 Amerlcan Chemical Society
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Table I. Ion Microscope Instrumental Parameters parameter
description
instrument primary ions primary ion energy primary ion current primary ion beam size primary ion raster area transfer optics contrast aperture detection mode
CAMECA IMS-3f SIMS ion microscope 02+
10 keV 1 pA and below 150 pm diameter 250 pm X 250 pm 150 pm field of view 60 pm in diameter microchannel plate/fluorescentscreen or electron multidier mode
problem, however, has recently been solved empirically by using instrumental refinements and digital image processing techniques (8-1 1). Fundamental studies incorporating the underlying physical phenomena are required to reach a satisfactory solution. Contrast artifacts originating from instrumental discriminations can also occur during the transmission and detection of images. Instrumental refinements and digital image superposition techniques were developed to correct the artifacts produced during image transmission (12,131. Contrast artifacts induced by the detector system (a microchannel platefluorescent screen assembly coupled to a video or 35mm camera) have been attributed mostly due to the detector’s lack of linear response and sensitivity. A number of studies have been attempted to minimize these artifacts, such as replacing the entire detection system with a tandem dual microchannel plate/RAE (resistive anode encoder) (14) or substituting the camera with a CID (charge injection device) camera (15). The RAE approach is designed to permit direct digitization of electrons from the microchannel plate, while the CID approach is intended to reduce the dark current noise. The improvement of information resulting from the above correction schemes not only satisfies each individual application but also provides a better understanding of the image collection process. However, the pitfalls of inevitable nonlinear response from the microchannel plate still pose major implications for image quantification (16). Therefore, a generalized image analysis methodology is needed to aid the selection of an appropriate detector and to ensure the objective evaluation of the newly developed correction schemes, i.e., their effects upon the accuracy of image quantification and spatial resolution. We tackled the resolution aspect previously by designing and implementing an objective means based on the modulation transfer function to evaluate the image resolution as a function of instrumental performance (17). The present study is conducted in recognition of the need for a comprehensive and objective means that evaluates the performance of image detectors and that provides correction schemes for various contrast artifacts. I t is the aim of this contribution to deal with the aspects of improving image quantification accuracy by characterizing the relevant artifacts resulting from detector discrimination.
EXPERIMENTAL SECTION Instrumentation. Ion microscopic analyses were performed on a CAMECA IMS-3f ion microscope, described in detail elsewhere (18). Instrumental conditions are presented in Table I. Images were acquired with an improved version of an on-line microscopic image digital acquisition system (MIDAS) (19) developed in thia laboratory. The heart of MIDAS is a PDP-l1/34A minicomputer interfaced to a Trapix image processor 55/32 (Recognition Concepts, Inc.) The video camera used with MIDAS was a 6 year old Quantex ISIT. A brand new Cohu Newvicon was used in the blooming study because of i b better sensitivity. Gain settings for both the ion microscope’s microchannel plate (Varian, VUW-896OY) and the video camera were read directly into a PDP-l1/34A minicomputer via a 12-bit analog to digital
(A/D) converter. Images were also recorded directly from the microscope’s fluorescent screen (a P20 phosphor) by a Canon 35-mm camera, using Kodak Technical Pan 2415 B&W film,which was later digitized off-line by using a Joyce-Lobe1Model 6 microdensitometer with a 10-bit A/D converter. A rotating, frequency-calibratedchopper (mechanical, Ithaco Model 382A) was used for the dead time study. Image Standards. Representative samples of known chemical matrices were used, and included (a) polished pieces of Al, (b) semiconductor-gradeSi wafers (General Diode), (c) pellets pressed from powder mitures of MgO (Spex),CaO (Allied Chemical),and 100-meshAg (Spex), (d) polished, single-crystal Si slices doped with B (GeneralDiode) with certified concentrations ranging from 1 X 1015to 1 X 10l8atoms/cm3,and (e) a microelectronic chip. Prior to analysis, the samples were washed ultrasonically in methanol, followed by distilled, deionized water. Computer Software. Software from the CAMECA was modified to synchronize the data acquisition between the probe (using an electron multiplier) and the image mode (using a microchannel plate-fluorescent screen assembly). The software package, SIMIPS (secondary ion masa image processing system) (20),was adopted for on-line digital image acquisition by using the video camera, while IMGSCN (image scan) (21)was employed to acquire the image off-line from the photographic film. The digital images were subjected to computer processing using SIMIPS. Both SIMIPS and IMGSCN were coded in FORTRAN IV and operated uner the RT-11 operating system. RESULTS AND DISCUSSION The analytical usefulness of an image detector is determined by its temporal response, spatial resolution, spectral sensitivity, and the convenience, reproducibility, and accuracy of relevant quantitative measurements. The final selection of the analyzing scheme and the detector used is dominated by the kind of information sought and the type of samples being analyzed. In our laboratory, we use the video camera for image depth profile analysis because of its continuous on-line image acquisition and digitization capability, while the 35-mm camera is used to acquire images of higher spatial resolution when the analyzed sample can generate an intense image whose spatial resolution is not limited by the instrument. This is because the film has a smaller pixel size (a diameter of 2.5 pm vs 25 pm) and a larger image format (24 X 37 mm2 vs 16 x 16 mm2) compared to the video camera. However, the selection of the recording camera for better quantification reproducibility and accuracy has not yet been clearly defined. To provide such a guideline for camera selection, the results are categorized into parameters of linearity, limit of detection, dynamic range, blooming, lag, dead time, dead space, and uniformity. In addition, this analysis scheme also reveals the source of various contrast artifacts and facilitates the estimation of relevant quantification uncertainty. Although these parameters are all directly related to the quantification accuracy, the parameters of blooming, dead space, and uniformity are applicable only to image detectors. Linearity. The linearity of the system is defined as the functional relationship between a map of the secondary ions emitted from the sample surface and the intensity of pixel values recorded by the imaging system. The linearity accuracy determines the success of the calibration of the signal (pixel value) produced to the measured quantity (secondary ion intensity) and hence the production of valid quantitative information. In practice, this functional relationship is rarely linear due to the many nonlinear transduction stages involved in the information acquisition process (Figure 1). The pixel values, however, could be linearized by using a proper linearization function that includes all parameters responsible for nonlinearity. The “linearization”function is found by fitting the intensity response to the count rate read from the electron multiplier (EM), while incorporating the gains of both the video camera (VC) and the microchannel plate (MCP). Sufficient data
ANALYTICAL C M M I S T R Y . VOL. 61, NO. 1. JANUARY 1, 1969
67
I
. t
t
t
t
t
t
1
2
3
4
5
6
t 7
t
t
8
9
Flgun 1. Schematic diagram of
informallon acquisition process. Shown are four trarmduclion stages, the corresponding signal at each
stage, and the instrumental components: (1) secondary ion source; (2) mlcrochannel plate: (3)fluorescent screen: (4) photo lens f = 55 mm. f11.2; (5) photo lens f = 50 mm, 111.4 when using Me0 c a m . or f = 55 mm. f l t . 2 when using 35-mm camera; (6) video camera cf 35mm camera: (7) video digitizer when using video camera or AID converter in the microdensitometer when using 35-mm camera: (8) computer; (9)display monitor. Table 11. Ion Intensity-Pixel Value Calibration Relationship ion species.
"Al+ %i+
%a+
a
% RSTD'
lv * 8 *8
1.94 X 1.71 X lo-' 1.57 X lo-'
* 12
0
* % RSTD -4.808 -4.673 -4.942
*
3 4 3
e
%
RSTD
* * *
16.540 3 16.216 4 17.190 3
'The ranges of the secondary ion intensity used to dculate the to 2 x l W A for "AI+, 3 x calibration relationship are 2 X 1Ul8 to 1 X lU" A for %i+, and 3.5 X lCr16to 5 x lW" A for Wa+. 'Percentaee relative standard deviation. covering a large range of secondary ion intensities are generated by varying the intensity of the primary ion beam. In addition, the magnification factor of the microscope is selected to ensure that the entire image area is covered by the MCP. The intensity response is the average pixel value of a homo. geneously intense ion image collected by using a chemically and spatially homogeneous sample. The count rate from the EM is normalized over the number of pixels in the image. Prior to the fitting procedure, the intensity response is compensated for the difference in gain and offset of the video digitizer in the image processor. The uncertainty due to this compensation process is negligible because the linear relationship between the intensity response and the gainfoffset is experimentallyreproducible. The form of the linearization function is similar to that derived previously (22) log 0 = aP + @ log (v) + y log (M) + 6 (1) where I is the secondary ion count rate in counts per second per pixel, P is the pixel intensity reapom of a 30-frame image, V and M represent the gain of the VC and the MCP, respectively; a,@, y, and 6 are coefficients to he determined empirically. In moat studies, the linearization function can he simplified to improve the fitting precision and efficiency by keeping the gain of the MCP constant, viz. log 0 = aP + @ log (v) + t (2) The Coefficients a,&and e, with associated fitting errors for ions of nAl+, ?Si*, and 'Oca+,have been determined and are given in Table 11. Differences in the coefficient values are anticipated since the MCP, fabricated with lead-doped glass (23),is a particle-momentum-sensitive current amplifier (24). Detailed examination of the experimental error, however, makes it difficult to conclude any mass discrimination effect as the coefficients' differences between the ions are within the uncertainties imposed by the experimentalerror. Considering the relatively small differences in the mass of the three ions
-2.
of the
" 0 cacatmtbn (alm./cms, X' 10'6 Measuedand hearized ion Mensity respmsesasa lurclia, wncentration of the "Bdoped Si slices.
used here, and assuming that the gain of the MCP is i n v e ~ l y proportional to the logmitbm of the mass of the ion (25).the sensitivity of the image detector could be experimentally regarded as mass independent over the mass range studied. Hence, the use of a universal linearization function is acceptable. If a more accurate linearization function is desired, it would he obtained by using several linearization functions, each derived by a smaller range of the secondary ion fluxthat closely matches the sample's EM count rates. The n m o w range is necessary hecause the MCP gain, above certain gain settings, varies with the density of the incoming particles (26). With either linearization, however, frequent calibrations are still needed because of the poor reproducibility of locating the video camera a t the same position relative to the image plane. Furthermore, the precision of the measured VC gain along with the Coefficients @ and t has more bearing on the calibration accuracy of the ion intensity than that of the pixel value and the coefficient a. The higher impact is expected because the magnitude of the coefficients @ and e is much greater than that of the coefficient a. Improvements in fitting error and the elimination of the need for frequent calibrations are achieved by using a 35-mm camera in place of the video camera. This is because the gain fluctuations are eliminated, and, in addition, the 35-mm camera is housed in a fixed bracket on the instrument, thus ensuring a reproducible position relative to the image plane. The linearization function is thus simplified to log
*
(0= aP + 7 )
(3)
where a = 2.41 X lo4 4.9%. and q = -1.460 f 3.5% for a Si wafer image with 1-sexposure time. These coefficients are experimentally reproducible. The effect of linearization is best demonstrated by imaging a series of B-doped Si slices with llB concentrations spanning 2 decades. Figure 2 shows the measured and the linearized ion intensity responses of llB using the 35-mm camera as the recording device. Limit of Detection. The limit of detection GOD) is de. fined as the lowest concentration of an analyte that an analytical process can reliably detect (27). Experimentally, the LOD can he computed readily with an appropriate mathematical formula, which is, in essence, a ratio of the assay precision to the sensitivity (i.e., the slope of the calibration curve). Conveutional form& require the standard deviation of measuring the blank sample response to estimate the assay precision (28,29). The availability of a blank sample is scarce in SIMS analysis as most of the samples being analyzed are d i d state in nature. Hence, we use Liteanu and Rica's fomula (30),which uses the standard deviation about they intercept of the calibration curve, to approximate the assay precision. Thus, the need of blank samples is relieved. The LOD of a series of B-doped Si slices is measured by using the EM, the
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ANALYTICAL CHEMISTRY, VOL. 61, NO. 1, JANUARY 1, 1989
VC, and the 35-mm camera. The LOD from the 35-mm camera is 1.4 X 10l6 atoms/cm3, comparable to 1.3 X 10l6 atoms/cm3 obtained by using the EM, and is an order of magnitude lower than that obtained by using the VC (1.7 x 10'' atoms/cm3). This is expected because the large amount of dark current noise in the video camera (Le., at high gain setting) prohibits the same length of integration time (the integration time is 1 s) as the film (the exposure time is 10 s). The correlation coefficient of the linearization function is above 0.95 for all three measurements. It should be pointed out that the amount of sample consumed (or needed) is proportional to the integration time since SIMS is a destructive process. Hence, the above comparison is of greater importance when the sample amount is scarce. Dynamic Range. The dynamic range is the ratio of the largest readable signal to the smallest useful signal of the detection system and is a good index of the system's information storing capacity. For a video camera, two different kinds of dynamic range are of practical use. The first is the single frame dynamic range (SFDR, instantaneous device response), which is determined by the size of the video digitizer used. Ideally for an 8-bit video digitizer a range of 255:l is expected because the readout noise is a t best one bit. The second kind is the multiframe dynamic range (MFDR, accumulative system response), which is dependent upon the individual memory size of the computer used. For the 16-bit PDP-l1/34A, an optimal range of 65535:l is achievable. For a true integrating device such as the photographic film, the dynamic range is dictated by the size of the A/D converter (10-bit) employed by the microdensitometer or the size of the computer memory (X-bit), whichever is smaller. An optimal range of 1023:l is expected. The arguments upon which the dynamic range depends are, however, based on the presumptions that the measured signal (i.e., the pixel value) is proportional linearly to the information of interest (i.e., the secondary ion intensities) and is collected under similar instrumental conditions and the same detector gain settings for each frame image. In practice, it is the intrinsic information storing capacity of the recording media, namely the silicon target in the ISIT video camera or the film in the 35-mm camera, that determines the useful dynamic range. With a nonlinear imaging system such as the one used in this study, it is only meaningful to evaluate the dynamic range in terms of the linearized signal with appropriate gain settings. The estimated SFDR is about 20:l using the video camera and about 2901 using the film. The 201 ratio is obtained by incorporating the largest readable signal of 7650 and the smallest useful signal of 1 into eq 2 using the coefficients for %Si+from Table I1 and the VC gain setting of 2000. The 290:l ratio is obtained similarly by incorporating 1023 and 1 into eq 3. The small SFDR associated with the video camera enforces the necessary compromise of increasing (or decreasing) the primary ion beam intensity and/or the video camera gain in order to reveal features of low (or high) emission, while running the risk of saturating (or ignoring) regions of higher (or lower) emissions. This phenomenon was verified with the series of B-doped Si slices. The video camera gain setting must be adjusted during the analysis to record the image of various llB concentrations. Increasing the inteflation time does little to improve the outcome. This implies that although image integration could improve the overall signal-to-noiseratio (S/N), the trade-off between features with emission variations larger than the SFDR is still inevitable. The achievable MFDR could vary up to 3 orders of magnitude by adjusting the video camera gain alone, while that for the film remains about the same. Therefore, the video camera is better suited to analyze samples containing large analyte concentration ranges between image frames (such as doped
eW 77
..
0
20
40
60
80
s
100
X Microchannel Plate Gain
Figwe 3. Ilustratbn of noise levels contained in an knage. Presented is the number of noise pixels (with the primary ion beam off) vs the percentage microchannel plate gain at various percent video camera gain, after 6 years of camera operation.
microelectronic devices) and lesa useful to investigate samples with large concentration gradients within a single frame image (such as biological specimens). The film functions in the opposite manner. The SFDR is the more relevant of the two types of dynamic range since it determines the range of intensity levels that can be measured simultaneously. The SFDR is dictated by the amount of noise in the image. Thus a comparison of the amount of noise introduced into the image as a function of the gain of the video camera and the microchannel plate will be instructive. Referring to Figure 3, the optimal MCP gain should be its maximum, as the number of noise pixels is independent of the MCP gain and is due mainly to the 6 year old video camera. This is different from the observation measured 4 years ago with the same camera that indicated the optimal MCP gain was at 60% maximum (31). Blooming. Feature blooming is manifested in the detection system's inadequacy to spatially resolve the incoming particles at the output image, thus degrading the system's feature resolving capability. This is caused by two factors. First, the intrachannel cross-talks occur at the microchannel plate, the fluorescent screen, and the solid-state target of the video camera. The relevant blooming effect is generally negligible when the flux of the incoming ions is below saturation level. The second source is the physical spread of electrons while being accelerated from the rear of the microchannel plate to the fluorescent screen. This latter effect was studied by imaging 43A10+ions from the Cu-A1 grid. A small number of 43A10+ions are detected with a new Newvicon video camera under maximum instrumental magnification. Figure 4 shows a 64-frame averaged 43A10+ image in which the angled lines depict the A1-Cu boundaries of the grid. The bright spots within the squares are determined to be 43A10+ions. Each square corresponds to an area of 540 x 540 pm2 on the image plane or 2.45 X 2.45 pm2on the object plane. The bright spots do not originate from the dark current noise since they do not appear in the previous or successive frame images. The two line trace profiles and the even brightness of the spots within the boxes indicate that feature blooming is uniform in all directions. The helix-like noise pattern is the result of setting the gain of the Newvicon video camera at its maximum. The spot size of the 43A10+ions is estimated to be about 271 pm. This estimation is based on the assumptions: (a) that each spot results from a point emission from the sample surface, (b) that the instrumental spatial resolution is 0.87 pm at a magnification factor of 220 (In,and (c) that the electrons exiting from the rear of a single microchannel plate channel can bloom to a spot of approximately 80 pm in diameter on the fluorescent screen (23). By summing the blooming effects of 80 pm with the size of the magnified ions, Le., 0.87 X 220 pm, we arrive at the estimated value.
ANALYTICAL CHEMISTRY, VOL. 61. NO. 1, JANUARY 1. 1989
2 ->"
69
.:a Md.ocaramch -:aMd.oearoCah
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75 50 25 0 0
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8
10
12
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F i Numk
Flg1~0 5. Lag character!stks of the MIDAS system. The pkd value (averam) of the Image fw the same secondary ion intensiiy under differentvideo camera gain senlngs is plottedas a fundion of the Reid number (each field is ' I w s aparl). Two fields comprise one lrame. The ligm wth is Mocked between WrJ number 8 and IO. F a shnpliiny.
only nonzero pixel values are shown.
I
1.2
Flgure 4.
Blooming of a small number of '3AIO+ ions. See text for
details. Experimentally, however, the spot size is 360 wm. This discrepancy is attributed to the violation of the second assumption in the estimation. The practical instrumental spatial resolution is probably worse than the optimum resolution because it is difficult to fine tune the instrument with an ion image of low intensity. The even brightness of the each individual spot contained in the rectangular box indicates that feature hlooming is uniform in all directions. Because blooming spreads excharge evenly a c r w the image field, it places little constraint on quantification work. However, the degradation of image spatial resolution is still inevitable. The same phenomenon was also observed when using the 35-mm camera as the recording device (17). Lag. Lag is a memory effect phenomenon in which signals left over from previous events are recorded. This is due to read-out delay whereby the signals on the fluorescent screen and the solid-state target of the video camera cannot be read out fully during a single frame scan. The study of this phenomenon is made possible with the consecutive frame imagegrabbing capability of the Trapix image processor. Sixteen consecutive frame images of a polished Si wafer, with the light path between the fluorescent screen and the video camera blocked immediately after grabbing certain %i+ images, were collected and analyzed. For a lag-free system, the transition from the %i+ image to the blank image should comprise two kinds of images, one with certain ion counts and the other with none. The decay curve (pixel value vs field number) should appear as a step function; however, as demonstrated in Figure 5, this is not the case. An exponential decay curve is obtained instead. The third-field lag (the percent residual signal in the third field, or 50 1118, after the illumination source is removed) varies from 30% to 50% depending on the video camera gain setting and the secondary ion intensity. This range is higher than that specified for a new ISIT video camera (5% to 35%) (32). Our experimental setup cannot mensure the contrihution of lag from the fluorescent screen. However, for a P20 phosphor, the small amount of residual signal (10% after 2 ms) (33)should make its contrihution negligible. I t should be pointed out that this lag effect is local pixel value dependent and has more impact on image integration work, where featurea of higher emission are relatively enhanced in terms of pixel values.
L
D
MqnHkoUm Factor
Flen6. Loss of s@al at the micmchannel p h t e h 10 the hQhRux of
incomlng ions.
Dead Time. In contrast to lag which generates artificial signals, dead time effect results in a failure of the system to record the incoming signals. This phenomenon is modlt noticeable when there are too many incoming secondary ions (electrons, or photons) impinging on the surface of the microchannel plate (fluorescent screen, or video camera). The dead time may be caused by (a) the finite transit time (about 1ns) for a beam of electrons to exit from the channels of the microchannel plate, however this is usually negligible, (h) the possibility that, under certain operational conditions, the luminescence efficiency of the phosphor contained in the fluorescent screen may decrease (34).this phenomenon usually complicates and prohibits the independent investigation of the artifacts from each contributor, (c) the finite time to neutralize the positive charges on the output end of the microchannel plate by the strip current (26), and (d) the finite time required to grab and digitize the image a t s video frame rate. Case c is manifest in a plot of relative ion counts per pixel versus the instrumental magnification factor a t various secondary ion intensities (Figure 6). The density of incoming ions is adjusted manually by varying the intensity of the primary ion beam until the brightness of the image on the fluorescent screen is less than full brightness. With a high and 1 X flux of incoming ions (5 X A), the average ion counts per pixel does not increase with decreasing mag. nification factor and the dead time effect is noticeable a t magnificationfactors leas than 140. However, with a low flux of incoming ions (1x lWL4A) the artifact disappears and the line profile is similar to that of an ideal curve in which the number of secondary ions is inversely proportional to the square of the magnification factor. This implies that in
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ANALYTICAL CHEMISTRY, VOL.
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.
0.41’ ’ * ’ 0 2 4 6 8
OpmtiOMl nm (ymtha)
Fi@m8. Aglng draracterish 01 ltw microchannel plate. The Inset d e w Um mncenkbliy d W bnaglng area. The central kv areas are labeled to indicate the corresponding unnormny index curve.
.
.
r”
mure 7. LOSS ot s gnal due to me inherent ‘/so s aead time. Shown are 1Ai a stationa~mechanical chopper with me m a f m denoted by an arrow and (E, the integrated mage 01 tour consecu1fvetrames of the rotating (CIoCkwisei chopper. Tne marmr m each frame s h Q h lighted oy the arrow.
practical analysis it is mandatory to first check the incoming ion intensity to avoid possible dead time effects. Also by so doing, the possibility of losing signal during the transmission process in the fluorescent screen and the video camera is minimized. However, this is only a preventive measure. In order to correct this dead time effect, a detailed study of the time mechanism, analogous to that of the electron multiplier (35), is needed. Case d is studied by analyzinga series of single frame images hy using a rotating chopper with calibrated rotating frequency. A marker is attached to the chopper surface to generate moving events that are indicated by the marker’s relative location (Figure 7AL Figure 7 8 shows the integrated image of four consecutive frames of the chopper rotating (clockwise) at 0.833 cycles per second. This means the marker should move 2 slots in every ‘Igo8. It is expected that if the imaging system has true integrating capability, then any event occurring during this grabbing period should he recorded.
However,Figure 7B indicates that each frame image comprises only a single field, i.e.. the movement of the marker is recorded only during the 1/80 s. This 50% loss of signal is fully reproducible. Dead Space. A similar phenomenon of consistent loss of signal also occurs with the configuration of the microchannel plate used in this study. This is because the finite thickness of the wall of each individual channel forms an interchannel web (dead space) which does not convert the incoming ions into measurable signals (23,363). The amount of the signal loss is determined by the open-area-ratio on the microchannel plate’s surface and is at least 50%. The phenomenon of signal loss due to dead space is reproducible and thus does not affect the relative quantification results hut severely hampers the detection limit. This is detrimental to trace analytical applications. Uniformity. The nonuniformity (shading) of the system is defined as the variation in the digital output within a frame for a sample of uniform intensity and is regarded as the systematic noise. This nonuniformity defect is caused by the spatially heterogeneous response from the video camera or from the micmchannel plate. Shading from the video camera is due to the transmission difference across the faceplate. The edge of the faceplate is thicker than the center and accounts for the drop of signal a t the corners (37). This is a fixed shading and does not change with operational time. On the other hand, microchannel plate shading is mainly due to the accumulative destructive bombarding of ita surface by the impinging energetic ions and is a function of the operational time. In order to quantitatively analyze the aging effect of the microehannel plate, a uniformity index is adapted and plotted as a function of accumulating operational time (Figure 8). The uniformity index is obtained hy dividing the average ion counts in each of three inner concentric rings by those in the outer concentric ring labeled “X”. Unity is expected for a polished, homogeneous Si wafer aample under perfect imaging circumstances, i.e., with a new micmchannel plate. Uniformity is observed during the first month of operation. After 2 months under normal operation, the channels in the central region (labeled as ”+”) become less sensitive than those on the neighboring regions. This trend of decreasing sensitivity from channels in the central region continues with increasing operational time. The worst case is seen after 30 months of operation when the sensitivity of channels in the central region is only 50% of those in the peripheral region. It is obvious that certain correction schemes are necessary to compensate for this shading artifact before any quantification is attempted. One intuitive way is by dividing each element of the linearized sample image, S(xy).hy the corresponding element of a linearized reference image, R ( x y ) ,and then scaling the result
ANALYTICAL C M M I S T R Y , VOL. 61, NO. 1. JANUARY 1. I989
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71
%re 9. Effects 01 linearization and shading-correction operations. Shown are (A) the 128-frame-averaged "Si+ reference image collected from a Si wafer, and the 32-frame-averaged "AI+ sample images collected from a microelectronics chip where (E).(C). and (D) correspond to
horlglnal. linearized. and shading-corrected images, respectively.
with the average pixel intensity of the same reference image,
R,, to ensure that the corrected image. C(xy),has the same total pixel intensity as S(xy). (4)
The reference image is of a polished Si wafer for theinunediate application. To prohibit division by zero and to minimize the random noise contribution, this normalization concept can be extended to C ( x y ) = 0 if R ( x y ) 5 B ( x y ) or S(x,y) 5 B(x,y) else
where B(x,y) is the blank image recorded when there are no incoming secondary ions. Rb,is the average pixel intensity
from a region with a unity uniformity index in the linearized reference image with the background image being subtracted first. The effect of applying this shading-correction algorithm 88 a function of the S/Nof the sample image is studied by using a single frame and a 32-frame-averagedimage of a Si wafer. The reference image is a 128-frame-averaged image obtained by using the same Si wafer. The ranges, means, standard deviations, and percentage relative standard deviations of pixel values for the original, the linearized, and the shading-corrected images are given in Table 111. The linearization operation stretches the contrast to the same degree with both images based on the similarity of the range increment. The shading-correction operation improves the heterogeneous response of both images. However, it yields different degrees of improvement judging from the difference in the ratio of the percentage relative standard deviation between the single frame and the 32-frame-averaged image, i.e., 12.415.0 vs
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ANALYTICAL CHEMISTRY, VOL. 61, NO. 1, JANUARY 1, 1989 ~~
Table 111. S t a t i s t i c a l R e s u l t s of Processed I m a g e s
no. of
frames original image linearized image shading corrected
1 32' 1 32 1 32
range
90
min-mas mean stddev 155-255 157-245 78-255 80-253 166-255 167-218
220.1 221.4 187.1 189.1 187.4 189.4
11.9 7.5 28.1 17.6 23.2 6.6
RSTD" 5.4 3.4 15.0 9.3 12.4 3.5
image
"Relative standard deviation. 'The pixel values are divided by 32 to yield values that are numerically comparable to those obtained from a single frame image. 3.59.3. Thw is expected since the random noise (or the error) in the linearized image is propagated to the shading-corrected image by the ratio operation. Thus, a high-quality image of the sample as well as the reference is equally important to the success of the shading-correction operation. The same phenomena are observed when applying the shading-correction operation to a real sample, for example, a microelectronicchip. Figure 9A shows a 128-frame-averaged reference image acquired from a Si wafer when the microchannel plate has been operated for 30 months. Due to higher accumulative radiation doaage, the pixels in the central region are dimmer than those in the peripheral regions. This microchannel plate aging effect is reflected clearly in the nAl+ image (Figure 9B) collected from the microelectronic chip; the features in the central region are of lower intensity. Figure 9C shows the linearized image where the contrast is stretched and the features with lower intensity appear less visible. This artifact of decreasing contrast is remedied after the shading correction is applied as Figure 9D illustrates. The features in the central region are clearly visible. Exact registration between the sample image and the reference image is necessary before shading correction. Thii is of special significance when the images are recorded on film and digitized off-line. In addition, feature shift due to the mass difference between the ion from the sample image and the reference image must also be taken into consideration.
CONCLUSIONS Quantitative characterization of secondary ion images suffering from image detector discriminations has been attained. The image detection system, comprised of three physically separate components (microchannel plate, fluorescent screen, and ISIT video camera or 35-mm camera), is studied as an integrated system under its current configuration to make the results of immediate use. We find that the quantification accuracy is limited most by the lack of a reproducible calibration curve from the image detection system. Evidence indicates that detector selection is critical to the measured limit of detection and that the optimal imaging condition also varies with the aging of the detector. Adequate dynamic range is crucial to reveal large image concentration gradients. Blooming has more impact on resolving spatial details than on quantifying compositional concentrations. Lag effect tends to amplify features of high emission and is more perceptible with integrated images. Dead time correction is required when the flux of incoming secondary ions is high. Dead space in the detector has no bearing on quantification accuracy but is detrimental to trace analytical applications. Digital image processing techniques using pixel-to-pixel normalization to compensate for the spatially heterogeneous detector response are useful to correct the nonuniformity of the microchannel plate. While image processing can compensate for certain flaws in ion images; much work is still needed to improve the lin-
earity of detector response and feature intensity-dependent contrast artifacts. This deficiency of reproducibility can be improved by compensating the discriminating response of the microchannel plate, reducing the total number of nonlinear transduction stages of images, and minimizing the remaining undesirable artifacts. Three approaches are currently pursued. First, replace the image recording device (i.e., the ISIT video or the 35-mm camera) with one of higher dynamic range to minimize the degradation of linearity caused by gain variations. The newly developed low-light, high-sensitivity CCD (charge-coupleddevice) camera, which is capable of providing digital output, appears promising. In addition, contrast artifacts introduced by lag and dead time would be alleviated by using this kind of true integrating device. Hence, the dependence of the microchannel plate response on gain setting, incoming ion mass, and incoming ion density can be better understood and compensated. Second, reduce the number of the image transduction stages to only one by substituting the entire image detection system with a CCD image detector for direct in situ ion detection, digitization, and recording to improve the linearity (38)and to totally remove the microchannel plate-dependent response. Third, implement digital image processing techniques to minimize the remaining artifacts which originate from dead space and blooming. Signal loss in the dead space is restricted by the physical configuration of the device used and is difficult to recover instrumentally. However, the missing data can be interpolated from the neighboring pixels providing that the relationship of intensity between neighboring pixels can be revealed. As with most spectrometers, a deconvolution approach can be applied to restore the deterioration of spatial resolution if the instrument transfer function is known.
LITERATURE CITED Morrison, G. H.; Slodzlan, G. Anel. Chem. 1975, 47, 932A-943A. Rudenauer, F. 0. S I A . surt. Interface Anal. 1984, 8 , 132-139. Grasserbauer, M.; Wilhartlz, P.; Stlngeder, G. M l k r o c f r h . Acta 1983, 11 1 , 467-492. Grasserbauer, M.; Zolotov, Yu. A.; Morrison, G. H. Pure Appl. Chem. 1986, 5 7 , 1153-1170. Bryan. S. R.; Griffle, D. P.; Woodward, W. S.; Linton, R. W. J . Vac. Sd. Technol. A 1985, 3(6),2102-2107. Burns, M. S. J . Micrac. 1982, 127, 237-258. Chandra, S.; Morrison, G. H. Schsnce 1985, 228, 1543-1544. Kobayashi, H.; Suruki. K.; Yukawa, K.; Tamura. H.; Ishbni, T. Rev. Sci. Instrum. 1977, 48. 1298-1302. Bayly, A. R.; Fathers, D. J.; Vohreik, P.; Wails. J. M.; Waugh, A. R.; Wolstenholme. J. In Secondary Ion M ss Spectmmtry (SIMS-V); Bennlnghoven, A., Colton, R. J., Simons, D. S., Werner, H. W., Eds.; Sprkger-Verlag: Berlin, 1986; pp 253-256. Bernlus, M. T.; Ling, Y. C.; Morrison, G. H. J . Appl. Phys. 1988, 5 9 , 3332-3338. Rudenauer, F. G.; Steiger, W. Ultramicroscopy 1988, 2 4 , 115-124. Turner, L. K.; Ling, Y. C.; Bernlus, M. T.; Morrison, G. H. Anal. Chem. 1987, 59, 2463-2468. Newbury, D. E. In 34th ASMS Conference on Mass Spectrometry and Applied Topics, Clncinnatl, OH, June, 1986. Mom, R. W.; Furman, B. K.; Evans, C. A., Jr.; Bryson, C. E.; Petersen, W. A.; Keiley, M. A.; Wayne, D. H. Anal. Chem. 1983, 5 5 , 574-578. Bryan, S. R.; Woodward, W. S.; Griffis, D. P.;Linton, R. W. J . Microsc. 1984, 138, 15-28. Newbury, D. E.; Bright, D. S. In Secondary Ion Mass Spectrometry (SIMS-VI);Benninghoven, A., Huber, A. M., Werner, H. W., Eds.; Wiley: New York; pp 389-392. Bernius, M. T.; Ling, Y. C.; Morrison, G. H. Anal. Chem. 1988, 58, 94-101. Lepareur, M. Rev. Techn . Thornson-CSF 1980. 12, 225-265. Furman, B. K.; Morrison. G. H. Anal. Chem. 1080, 5 2 , 2305-2310. Ling, Y. C.; Bernius. M. T.; Morrlson, G. H. J . Chem. Inf. Comput. Sei. 1987, 27, 88-94. Ling, Y C.; Bernius, M. T.; Morrison, G. H. Materials Science Center, Report No. 5921. Cornell University, 1986. Harris, W. C. Ph.D. Dlssertatlon, Cornell University, 1983. Wiza, J. W. Nucl. Instrum. Methods 1979, 162, 587-601. Burrous, C. N.; Lieber, A. J.; Zavlansteff, V. T. Rev. Sei. Instrum. 1967, 38, 1477-1481. Tuithoff, H. H.;Boorboom, A. J. H.Int . J . Mass Spectrom . Ion Phys 1074, 15, 105-109. Applications for Microchannel Plate ; MCP-2819D, VarianlLight Sensing and Emission Division: Palo Alto, CA. 1979. Guidelines for Data Acquisition and Data Quality Evaluatlon in Environmental Chemistry. Anal. Chem. 1880, 52, 2242-2249.
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Anal. Chem. 1989, 6 1 , 73-77 (28) Nomenclature, Symbols, Units and their Usage in Spectrochemical Analysis-I1 Data Interpretation: Spectrochim Acta, Part 8 1078, 338, 241-245. (29) Long, G. L.; Winefordner, J. D. Anal. Chem. 1083, 5 5 , 712A-724A. (30) Liteanu, C.; Rica, 1. Statfsticel Theory and Methodology of Trace Ana@&; Wiiey: New York, 1980; Chapter 7. (31) Patkln, A. J. Ph.D.Dissertetlon, Cornell Unlverslty, 1983. (32) ZSZT Cemera Tubes 4849 l H Series : RCAlSolM State Division: Lancaster, PA, 1977. (33) Martin, A. In Advances h €iecbon/cs and Electron phvslcs, Hawke. P. W., Ed.; Academic Press: New York. 1986 Voi. 67, pp 183-323. (34) Leverenz, H. W. An Introduction to Luminescence of Solids; Wiiey: New York, 1950; pp 381-390. (35) Traxylmayr, U.; Reldling, K. Znt. J . Mass. Spectrom. Zon Phys. 1084, 6 1 , 261-276.
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(38) Taylor, R. C.; Hattrick, M. C.; Maiina, R. F. Rev. Sci. Znstrum. 1083, 5 4 , 171-175. (37) Robinson, G. A. The S///mn Intenslfler Target Tube: Seeing h the Dark; RCAlSold State Division: Lancaster, PA, 1977. (38) Turner, L. K.; Mantus, D. S.; Ling, Y. C.; Morrison, G. H. In 36th ASMS Conference on Mass Spectrometry and Applied Topics; San Francisco, CA. June 1988.
RECEIVED for review August 12, 1988. Accepted October 7, 1988. This work was supported by National Science Foundation, the Office of Naval Research, and the Materials Science Center at Cornell.
Differential Gas Chromatographic Mass Spectrometry Amit Ghosh and Robert J. Anderegg*
Department of Chemistry, University of Maine, Orono, Maine 04469
A computer program for extracting single-component mass spectra from poorly separated chromatographic peaks of a gas chromatographic mass spectrometric (GC-MS) data set Is described. On the basis of the successive subtraction of pairs of raw mass spectra, the “ditferentiaimass spectra” are free from background lons and extraneous lons from coelutlng compounds. The cleaned up spectra allow good comparison to library spectra and facllltate reliable ldentiflcation. The technique also increases the apparent chromatographic resolution by sharpening the peaks. The method Is similar in some respects to other forms of derivative spectroscopy but has the advantage of reducing chemical noise. The approach is conceptually slmple, rapid, and easy to implement. Components that have an elution ditference of only one scan can be resolved. I n a direct comparlson, the method compares favorably to other readily available techniques for spectral cleanup.
INTRODUCTION The combination of gas chromatography (GC) and continuously scanning mass spectrometry (MS) provides an extremely powerful analytical tool for the characterization of complex mixtures. A difficulty remains, however, in that not all components in the mixture may be chromatographically resolved, even with high-resolution capillary GC (I). This leads to mass spectra that represent mixtures and are therefore subject to errors in interpretation or unreliable results in library retrieval routines. A large number of strategies have evolved to clean up the mass spectra of mixtures, ranging from the simplest background subtraction routine to sophisticated spectral reconstruction (2,3)or fador analysis (4,5). Although the methods vary widely in their approach, required computing power, and analysis time, they all have a common goal: to extract clean spectra from dirty ones. The ’dirt” can arise from instrument background (air, pump oil vapor, water), chromatographic septum or column bleed, or coeluting interferent, and it can produce spectra in which ion intensities are distorted or extraneous ions are present that have no relation to the compound of interest. In a number of types of spectroscopy, it has been shown that taking the first or higher derivative of the analytical signal *Author to whom correspondence should be addressed. 0003-2700/89/0361-0073$01.50/0
can result in improved spectral resolution (6). The idea is that
the analytical signal is changing at a different rate than that of the noise. We undertook this study to see if a similar approach could be used with repetitively scanned GC-MS data to provide spectra of better quality. T o obtain the ‘differential” of a GC-MS data set, we subtracted the raw ion abundances of ions of the same mass in successive pairs of spectra, plotting the result. Compounds whose concentrations are increasing are represented in this subtracted plot by ions with positive abundances. That is, the abundance of ions for such compounds is greater in spectrum number X + 1than in spectrum number X. Compounds whose concentrations are decreasing appear with negative abundances. The ions of these compounds will be less abundant in spectrum X + 1than in spectrum X. Compounds present in about the same concentration in both spectra X and X + 1are not observed, because the abundances of their ions will subtract to zero. Despite the obvious simplicity of the approach, we find that it is capable of substantial spectral improvement. The method was first evaluated with simulated data using a commercial spreadsheet program. Finally, we tested the program on actual GC-MS data from the analysis of fuel oil and compared it to alternative methods of spectral cleanup.
EXPERIMENTAL TECHNIQUES GC-MS experiments were simulated by using an IBM PC-XT with 640K core memory as we have previously described (7). LOTUS 1-2-3(version 2.0) was used as the spreadsheet without any modification. For the analysis of real data, programs were written in FORTRAN on the data system of a Hewlett-Packard 5985 B GC-MS system. The data fide used was a GC-MS analysis of a fuel oil using capillary column GC and electron ionization mass spectrometry. Two consecutive scans (mass spectra) of a chromatogram (total ion plot) are considered. The absolute abundances of the masses in the first scan are subtracted from the absolute abundances of the corresponding masses in the later scan. The substracted intensities are plotted either in the positive or negative directions. They are normalized to a maximum of 100, taking the highest absolute values for each direction. These normalized intensities plotted against their m / z values give a ’differential mass spectrum”,which is assigned the same scan number BS the later of the two original spectra. The unnormalized abundances of the differential mass spectrum are summed to give a total ion abundance, in both the positive and negative directions, at that scan. Similarly,the total ion abundances are calculated for each scan for the entire scan range of the chromatogram. The total ion abundances are normalized to a maximum of 100 and plotted 0 1988 American Chemical Society
