research papers
Synchrotron microanalysis techniques applied to potential photovoltaic materials
aESRF, 6 rue Jules Horowitz, BP 220, 38043 Grenoble, France
*Correspondence e-mail: julie.villanova@esrf.fr
X-ray synchrotron radiation techniques are used to characterize photovoltaic-related semiconductors. Micro-X-ray-fluorescence and X-ray beam induced current mapping of multicrystalline silicon photovoltaic cells show metallic impurities accumulating at the interface of crystallographic defects, and current variations over the cell that are attributed to bulk defects and structural variation of the silicon. Similarly, studies on a single-crystal GaAs using
and X-ray excited optical luminescence show an inhomogeneous As distribution correlated with the signal, with higher As concentration regions having stronger signal. Both examples show how the combination of synchrotron microanalysis techniques can contribute to a better understanding of the optical properties of photovoltaic materials.Keywords: microanalysis; photovoltaic; XRF; XBIC; XEOL.
1. Introduction
Efficient dedicated characterization tools are essential for developing advanced materials for solar cells. Compared with standard and well established laboratory tools, synchrotron radiation techniques can unravel structure–property relationships thanks to the large penetration depths, high throughput and superior tunability in terms of probe (energy, spot size, etc.). The current levels of hard X-ray beam intensities and in situ capabilities allow parallel signal acquisition and analysis of buried structures and phenomena, under realistic operation conditions. Indeed, synchrotron-radiation-based methods have been proven as major sources of information on solar cells. X-ray diffraction (XRD) and small-angle X-ray scattering techniques have provided structural information at the atomic and at the mesoscopic scales (Schorr, 2011; Chiu et al., 2008). has delivered information on local phenomena helping to identify degradation and failure mechanisms (Buonassisi et al., 2005a). Insight into chemical states and bonding has become accessible via techniques (Lauermann et al., 2005). However, to facilitate further breakthroughs, new methodological improvements are necessary in this field; for instance, coupling advanced X-ray characterization tools; higher-resolution imaging techniques with multiple length domains to correlate data acquired at different scales; and the complementarity of combining techniques. In this respect, the characterization of photovoltaic materials at the hard X-ray microprobe ID22 at the European Synchrotron Radiation Facility (ESRF) has recently stimulated the application of (XRF), X-ray beam induced current (XBIC) and X-ray excited optical luminescence (XEOL) at the micrometre scales. Several photovoltaic-related issues have been addressed at ID22; for example, the formation of impurity aggregates, radiative recombination centers, and breakdown effects (Trushin et al., 2010; Gundel et al., 2009, 2010). Furthermore, the increased spatial resolution towards nanoscales in two-dimensional imaging techniques reaches a previously inaccessible level of detail (Kwapil et al., 2009). In this work we report a few recent examples of these hard X-ray microanalytical tools applied to two photovoltaic-related semiconductors: Si and GaAs.
polarization,2. Experimental details
The synchrotron-radiation-based measurements were performed at the hard X-ray microprobe beamline ID22 at the ESRF. The high-β straight section of ID22 is equipped with two insertion devices, an in-vacuum U23 and a revolver U35/U19. The optical layout of the beamline consists of several pairs of slits, attenuators, a horizontally deflecting Si mirror, and a double Si crystal monochromator that is moveable so that either pink beam or monochromatic beam mode can be operated in the experimental hutches. There are two experimental stations that share common instrumental equipment shown in Fig. 1: an X-ray focusing device (Kirkpatrick–Baez mirrors), a high-precision piezostage to align and to raster-scan the sample in the beam, a visible-light microscope (VLM) to visualize the regions of interest of the samples, as well as various detection schemes and two-dimensional/three-dimensional X-ray imaging (XRI) detectors. The two experimental hutches are clearly identified by their spatial resolutions: EH1 devoted to microanalysis and EH2, also known as the ID22 nano-imaging station (ID22NI), exclusively used for nanoanalysis. Both hutches are commonly dedicated to hard X-ray microanalysis consisting of the combination of XRF, XRD and XRI techniques in the multi-keV X-ray regime (Martinez-Criado et al., 2012). In addition, in order to perform even more challenging experiments mainly driven by semiconductor research, and to enlarge the experimental capabilities of the beamline in the microspectroscopy domain, micro-XBIC and micro-XEOL methods have also recently been integrated within the multimodal detection approaches as displayed in Fig. 1 (Martinez-Criado et al., 2011; Trushin et al., 2010). In general, and XBIC are complementary tools used to discriminate areas of semiconductors according to their electrical behaviour and/or recombination activity. Another advantage of both techniques in comparison with standard and light beam induced current (LBIC) measurements is the long absorption length of the X-ray excitation, which allows measurements of buried layers without being influenced by surface effects. Silicon drift detectors or a 13-element Si(Li) detector are routinely available to detect XRF on the microprobe, whereas Keithley 6485 picoamperemeters are commonly used to register XBIC signals. The arrangements are basically preconfigured modules optimized for three different wavelength ranges: (i) 360–800 nm, (ii) 800–1600 nm, (iii) 1600–2200 nm. Each module comprises motorized optics to collect and focus the signal on an optical fibre that transmits the light to a spectrometer. The monochromator separates the light into its different wavelengths which are then incident on a linear charge-coupled-device detector. This combination produces fast acquisitions and provides high spectral resolution and maximum efficiency over the entire optical wavelength range (Martinez-Criado et al., 2006).
3. XBIC
Fig. 2 displays the (SEM) image (a), µ-XRF (b and c) and µ-XBIC (d) measurements collected on a multicrystalline silicon in the region of a crystalline defect consisting of a twin and misoriented grains within the bulk matrix. The µ-XRF and µ-XBIC maps were acquired in the experimental hutch EH1 with a micrometre-sized beam [8 µm × 2 µm (H × V)] over a sample area of 400 µm × 32 µm (H × V) with an of 17 keV. The XRF maps were recorded by measuring the different fluorescence line intensities at an incident angle of 45 ± 5° with respect to the sample surface. The step size was 8 µm × 2 µm (H × V) to match the beam size, and integration times were determined by the counting statistics (∼8 s point−1). The information depth of the fluorescence photons is element dependent and is about 9 µm for Si, 26 µm for Fe and 61 µm for Zn at 17 keV. The measurement sensitivity is around 0.2 ± 0.1 p.p.m. for the metallic impurities.
The SEM image (Fig. 2a) shows that the scanned area is characterized by morphological and crystalline differences within the Si matrix. The Si matrix is (111)-oriented, and both the twin and the other grains exhibit small, predominantly pyramid-shaped, surface pits. These features arise from a texturization step in the cell processing, designed to reduce optical reflectivity and enhance optical confinement, and thus to increase the efficiency of the solar cell. The shape of the grain boundary correlates with variations observed in the µ-XRF maps. The uniform green part on the left of the XRF map (Fig. 2b) corresponds to the twin, whereas the spatial fluctuations observed near the centre (red and blue stripes) correspond very well to the boundaries between twin and misoriented grains. Thus, our observations reveal the sensitivity of the µ-XRF method to the morphological and structural order present on and near the silicon surface.
Some micrometric aggregates of iron and zinc, that are not c). Such impurities, very often localized at the grain boundaries (Buonassisi et al., 2006a,b), do not generally have a significant impact on the photovoltaic performance of the cell.
were also found at the grain boundaries, as illustrated in Fig. 2(The µ-XBIC measurements clearly correlate with the XRF map of the silicon with respect to the interface region between the twin and the misoriented grains [compare Figs. 2(b) and 2(d)]. While a photocurrent variation of about 20% can be deduced between the two areas, the change in the Si only corresponds to ∼2%. This substantial difference could result from the bulk sensitivity of the µ-XBIC method (determined by the here equal to the thickness of the sample). The µ-XBIC signal is not only linked to the textured surface, but also to the dispersed small metallic nanoprecipitates with a concentration of ∼1012 cm−3 (metallurgical silicon), or to the high dislocation density (El Ghitani et al., 1993). The dimensions of nanoclusters are below the spatial resolution reached in experimental hutch EH1 but are detectable in experimental hutch EH2 which is optimized for a 100 nm beam size (Buonassisi et al., 2005b). In summary, the combination of µ-XRF and µ-XBIC measurements permits a direct correlation between chemical and photocurrent changes within the sample matrix. In comparison with LBIC, where the carriers are photo-generated within a few tens of micrometres of the surface and then diffuse through the bulk, the X-ray beam passes through the total thickness of the sample, generating carriers along the full length of its path.
4. XEOL
Fig. 3(a) shows the average XRF spectrum of a single-crystal GaAs compound semiconductor sample, with a thickness of 300 µm, acquired over an area of 140 µm × 100 µm in experimental hutch EH1. The XRF as well as signals were simultaneously excited with a 12.2 keV monochromatic beam focused to a spot size of 3.3 µm × 1.2 µm (H × V). For this energy the information depths for Ga and As fluorescence and for photons are 6.5 µm, 4.8 µm and 0.5 µm, respectively. The sample area was scanned with a step size of 4 µm in both vertical and horizontal directions, and a counting time of 2 s point−1. The X-ray characteristic lines of the major elements (Ga and As) and minor dopants (unintentional Ni and Fe) are indicated. Setting regions of interest around the fluorescence lines of each element, Figs. 3(c)–3(f) display the respective intensity maps. In comparison, Fig. 3(b) shows the intensity map of the luminescence peak located at ∼1.5 eV, which is associated with the recombination of a donor bound-exciton (D0X) (Sell et al., 1973). The edge of the sample can be easily identified on the left. The signal falls to zero in this region but not the fluorescence, probably due to an irregular sample cut, reflecting the smaller information depth of the compared with XRF photons. The Ga fluorescence map exhibits an almost constant intensity beyond the edge, suggesting a homogeneous distribution all over the scanned area. However, the As fluorescence map shows regions with higher intensity close to the edge, and an invariable distribution over the right half. Regions having a higher XRF intensity suggest a deviation from exact 1:1 GaAs stoichiometry. This deviation could be due to arsenic-related bulk defects, like arsenic antisites and interstitials (Chadi & Chang, 1989; Stellmacher et al., 2001), or arsenic precipitates (McInturff et al., 1992; Feenstra et al., 1993), known to be recombination centres (Viturro et al., 1992). The map shows areas with a stronger signal, indicating a higher density of donors or their transition rates. There seems to be a correlation between high signals and As fluorescence intensities. The former would suggest that the D0X recombination could be associated with As-related defects. However, more detailed investigations are necessary to complete the picture of the statistical dependence between the two data sets. Finally, the XRF maps of both Ni and Fe exhibit a random distribution, without any correlation with the map. Transition metals are efficient optically active recombination centres in GaAs, as observed in the of doped samples (Van de Ven et al., 1986). However, the absence of any observable dependent phenomena between the and the Ni/Fe maps points out that these elements are not involved in the D0X recombination, i.e. neither the Ni nor the Fe impurities act as donor centres. This is a good example of how the combination of µ-XEOL and µ-XRF imaging helps to shed more light on the effect of dopants and defects on the optical properties of photovoltaic cells.
5. Conclusion
In summary, synchrotron microanalytical techniques were applied to two potential photovoltaic-related semiconductors: multicrystalline Si and single-crystal GaAs. µ-XRF and µ-XBIC measurements on a Si
showed micrometre-sized aggregates of metallic impurities at the interface between crystallographic defects, whereas changes in the X-ray beam induced current were attributed to structural modifications and bulk defects. Thus, the combination of both tools provides critical information for promoting improvements in the performance of solar cells. In another example, using µ-XRF and µ-XEOL, a heterogeneous distribution of As in GaAs accompanied by striking luminescence changes was observed. Although more detailed investigations are required to explain this finding, our observations show a direct path to a comprehensive understanding of the relationship between material, structural and optical properties in photovoltaic cells.Acknowledgements
We thank D. Camel and S. Dubois from CEA/INES for supplying the mc-Si sample and for interesting discussions about solar cells.
References
Buonassisi, T., Istratov, A. A., Heuer, M., Marcus, M. A., Jonczyk, R., Isenberg, J., Lai, B., Cai, Z., Heald, S., Warta, W., Schindler, R., Willeke, G. & Weber, E. R. (2005b). J. Appl. Phys. 97, 074901. Web of Science CrossRef Google Scholar
Buonassisi, T., Istratov, A. A., Marcus, M. A., Lai, B., Cai, Z. H., Heald, S. M. & Weber, E. R. (2005a). Nat. Mater. 4, 676–679. Web of Science CrossRef PubMed CAS Google Scholar
Buonassisi, T., Istratov, A. A., Pickett, M. D., Heuer, M., Kalejs, J. P., Hahn, G., Marcus, M. A., Lai, B., Cai, Z., Heald, S. M., Ciszek, T. F., Clark, R. F., Cunningham, D. W., Gabor, A. M., Jonczyk, R., Narayanan, S., Sauar, E. & Weber, E. R. (2006a). Prog. Photovolt. Res. Appl. 14, 513–531. Web of Science CrossRef CAS Google Scholar
Buonassisi, T., Istratov, A. A., Pickett, M. D., Marcus, M. A., Ciszek, T. F. & Weber, E. R. (2006b). Appl. Phy. Lett. 89, 042102. Web of Science CrossRef Google Scholar
Chadi, D. J. & Chang, K. J. (1989). Phys. Rev. B, 39, 10063–10074. CrossRef CAS Web of Science Google Scholar
Chiu, M. Y., Jeng, U. S., Su, C. H., Liang, K. S. & Wei, K. H. (2008). Adv. Mater. 20, 2573–2578. Web of Science CrossRef CAS Google Scholar
El Ghitani, H., Pasquinelli, M. & Martinuzzi, S. (1993). J. Phys. III, 3, 1941–1946. CAS Google Scholar
Feenstra, R. M., Woodall, J. M. & Pettit, G. D. (1993). Phys. Rev. Lett. 71, 1176–1179. CrossRef PubMed CAS Web of Science Google Scholar
Gundel, P., Martinez-Criado, G., Schubert, M. C., Sans, J. A., Kwapil, W., Warta, W. & Weber, E. R. (2009). Phys. Status Solidi RRL, 3, 275–277. Web of Science CrossRef CAS Google Scholar
Gundel, P., Schubert, M. C., Heinz, F. D., Kwapil, W., Warta, W., Martinez-Criado, G., Reiche, M. & Weber, E. R. (2010). J. Appl. Phys. 108, 103707. Web of Science CrossRef Google Scholar
Kwapil, W., Gundel, P., Schubert, M. C., Heinz, F. D., Warta, W., Weber, E. R., Goetzberger, A. & Martinez-Criado, G. (2009). Appl. Phys. Lett. 95, 232113. Web of Science CrossRef Google Scholar
Lauermann, L., Pistor, P., Kotschau, I. & Bar, M. (2005). Mater. Res. Soc. Symp. Proc. 865, 41–52. CrossRef CAS Google Scholar
McInturff, D. T., Woodall, J. M., Warren, A. C., Braslau, N., Pettit, G. D., Kirchner, P. D. & Melloch, M. R. (1992). Appl. Phys. Lett. 60, 448–450. CrossRef CAS Web of Science Google Scholar
Martinez-Criado, G., Alen, B., Homs, A., Somogyi, A., Miskys, C., Susini, J., Pereira-Lachataignerais, J. & Martinez-Pastor, J. (2006). Appl. Phys. Lett. 89, 221913. Google Scholar
Martinez-Criado, G., Alén, B., Sans, J. A., Homs, A., Kieffer, I., Tucoulou, R., Cloetens, P., Segura-Ruiz, J., Susini, J., Yoo, J. & Yi, G. (2011). Nucl. Instrum. Methods Phys. Res. B, doi:10.1016/j.nimb.2011.08.013. Google Scholar
Martinez-Criado, G., Tucoulou, R., Cloetens, P., Bleuet, P., Bohic, S., Cauzid, J., Kieffer, I., Kosior, E., Labouré, S., Petitgirard, S., Rack, A., Sans, J. A., Segura-Ruiz, J., Suhonen, H., Susini, J. & Villanova, J. (2012). J. Synchrotron Rad. 19, 10–18. Web of Science CrossRef IUCr Journals Google Scholar
Schorr, S. (2011). Solar Energy Mater. Solar Cells, 95, 1482–1488. Web of Science CrossRef CAS Google Scholar
Sell, D. D., Stokowski, S. E., Dingle, R. & DiLorenzo, J. V. (1973). Phys. Rev. B, 7, 4568–4586. CrossRef CAS Web of Science Google Scholar
Stellmacher, M., Bisaro, R., Galtier, P., Nagle, J., Khirouni, K. & Bourgoin, J. C. (2001). Semicond. Sci. Technol. 16, 440–446. Web of Science CrossRef CAS Google Scholar
Trushin, M., Seifert, W., Vyvenko, O., Bauer, J., Martinez-Criado, G., Salome, M. & Kittler, M. (2010). Nucl. Instrum. Methods Phys. Res. B, 268, 254–258. Web of Science CrossRef CAS Google Scholar
Van de Ven, J., Hartmann, W. J. A. M. & Giling, L. J. (1986). J. Appl. Phys. 60, 3735. CrossRef Google Scholar
Viturro, R. E., Melloch, M. R. & Woodall, J. M. (1992). Appl. Phys. Lett. 60, 3007. CrossRef Web of Science Google Scholar
© International Union of Crystallography. Prior permission is not required to reproduce short quotations, tables and figures from this article, provided the original authors and source are cited. For more information, click here.