# A method to relate the presence of structural defects and impurities and their impact on the electrical activity in silicon for photovoltaic applications

Sirine Houam, Hadjer Ouaddah, Gabrielle Regula, Isabelle Périchaud, Guillaume Reinhart, Marisa Di Sabatino Lundberg, Lasse Vines, Mathieu Silly, Fabrice Guittonneau, Laurent Barrallier, et al.

► To cite this version:

Sirine Houam, Hadjer Ouaddah, Gabrielle Regula, Isabelle Périchaud, Guillaume Reinhart, et al. A method to relate the presence of structural defects and impurities and their impact on the electrical activity in silicon for photovoltaic applications. Solar Energy Materials and Solar Cells, 2025, 282, pp.113415. 10.1016/j.solmat.2025.113415. hal-04882845

HAL Id: hal-04882845

https://hal.science/hal-04882845v1

Submitted on 13 Jan 2025

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**Graphical abstract:**

Relation between sub-grain boundaries characterised by Electron Backscatter Diffraction, interstitial oxygen concentration maps (coloured maps) obtained by Micro Fourier Transform Infrared spectrometry and photoluminescence map (black & white map) of the same region to characterise the electrical activity.

<figure><img src="image_2.png" /></figure>

**Highlights:**

- A method that relates structural defects, impurity concentration and electrical activity at different scales is proposed.

- Sub-grain boundaries and twin boundaries are characterised.

- Oxygen in precipitate form is associated to structural defects. Carbon and copper impurities are also related to the highest defective regions.

- The degradation of electrical properties at several scales is directly related to the presence of sub-grain boundaries, grain boundaries and impurities.
# A method to relate the presence of structural defects and impurities and their impact on the electrical activity in silicon for photovoltaic applications

**Authors** : Sirine Houam¹, Hadjer Ouaddah¹, Gabrielle Regula¹, Isabelle Périchaud¹, Guillaume Reinhart¹, Marisa Di Sabatino Lundberg², Lasse Vines³, Mathieu G. Silly⁴, Fabrice Guittonneau⁵, Laurent Barrallier⁵, Etienne Pihan⁶, Nathalie Mangelinck Noël¹

1 Aix Marseille Univ, Université de Toulon, CNRS, IM2NP, Marseille, France

2 Department of Materials Science and Engineering, NTNU, NO-7491 Trondheim, Norway

3 University of Oslo, Department of Physics, Oslo, Norway

4 Synchrotron SOLEIL, L'Orme des Merisiers, Départementale 128, 91190, Saint-Aubin, France

5 Arts et Métiers Institut de Technologie, HESAM 2, Cours des Arts et Métiers, Aix-en-Provence 13617, France

6 Univ. Grenoble Alpes, CEA, Liten, Campus INES, 73375, Le Bourget du Lac, France

## Abstract:

In this work, we combine complementary diagnostic methods to characterise thoroughly crystalline silicon with a view to its application to photovoltaic (PV) solar cells: structural and chemical analysis, and charge transport investigations. On one hand, secondary ion mass spectrometry (SIMS) and Micro Fourier transform infrared spectroscopy (µFTIR) are used to detect chemical impurities and their spatial distribution. On the other hand, electron backscatter diffraction (EBSD) reveals grain structure up to the presence of sub-grains. In addition, surface photovoltage (SPV) is used to measure minority carrier lifetime whereas photoluminescence (PL) is used to provide maps that show the variation of the non-radiative recombination activity on the analysed areas. The method consists of taking benefit of complementary characterisation techniques at different scales to investigate structural defects, location and form of impurities as well as the related electrical properties in silicon for photovoltaic applications.

This study shows that impurities concentrate at defect sites: sub-grains and twin boundaries depending on their crystalline quality. Concerning the charge transport characterisation, these areas act as recombination centers, decreasing the electrical properties. Noticeably, the oxygen is found to be present in interstitial position in the bulk whereas at the level of sub-grains, it is in precipitate form. Combining these techniques enhances our understanding of the impact of impurities in conjunction with the presence of structural defects at several scales on photovoltaic performance.

## 1. Introduction:

Crystalline silicon is the most common semiconductor used for solar cells, representing approximately 95% of the modules sold today. Among these, monocrystalline silicon (c-Si) cells currently exhibit the highest efficiency reaching 27.3% in laboratory scale [1]. Although these records are reached, solar cell efficiency is strongly dependant on the crystallographic defects and impurities present in the silicon material which are for a large part induced during the crystal growth step whatever the process used. Even in the case of the dominant method of Si fabrication for the PV industry, Czochralski (Cz) growth which produces mono-crystalline ingots, the issue of dislocations remains a main concern to improve the cost/efficiency ratio [2].
In general, dislocations are one of the most important efficiency limiting defects in Si solar cells and can reorganise as sub-grain boundaries (Sub-GBs) due to dislocation pile up and polygonalisation [3–5]. Dislocation clusters and Sub-GBs both act as effective recombination centres for charge carriers and thus reduce the efficiency of solar cells drastically [6] as they constitute preferential sites for impurity segregation, be it in the dislocation cores or in their vicinity. The presence of these particular structural defects and the associated impurity segregation play a key role in the prospects of increasing the process yield and reuse or recycling of the crucibles and seeds [6]. Impurities from the feedstock, furnace environment or crucible not only interact with the nucleation and development of the defects but can additionally segregate on structural defects enhancing their electrical activity and ultimately degrading the cell conversion efficiency [7]. Moreover, the use of recovered or recycled Si which is fast developing [8] also opens new challenges and opportunities for re-optimising industrial processes [9]. Oxygen (O) is one of the most deleterious impurities [6,10], in particular for the Cz process.

This study aims to propose a method to characterise fully the structural defects, segregation of impurities within or nearby these defects and the global and local impact on the electrical activity. We investigate more specifically the effect of oxygen segregation at extended defects in crystalline silicon. Several techniques, including electron backscatter diffraction (EBSD), secondary ions mass spectrometry (SIMS), Fourier transform infrared spectroscopy (FTIR), photoluminescence (PL) and surface photovoltage (SPV), are combined to investigate grain structure formation, crystallographic defects, impurities such as oxygen, carbon and copper and their impact on the charge transport.

## 2. Experimental section:

Two samples are extracted from the same cast mono ingot at different heights. Sample 1 is close to the pavement of seeds and sample 2 is situated few centimeters above. The ingot is P-doped with boron that corresponds to a concentration of $10^{16}$ at/cm$^3$. The dimensions and general information are given in (Table 1). The samples are selected for their singular structural defects and their contamination with impurities, in particular with oxygen and represent extreme cases of defects in Si ingots. They have been both mirror-polished on both sides.

<table><caption>**Table 1:** Information about the studied samples.</caption><thead><tr><td>Sample name</td><td>Structure</td><td>Dimensions (cm<sup>2</sup>)</td><td>Width (cm)</td><td>Resistivity (Ω.cm)</td></tr></thead><tbody><tr><td>Sample 1</td><td>Monocrystalline</td><td>6.7×10<sup>-1</sup> and 9.6×10<sup>-1</sup></td><td>0.05</td><td>1</td></tr><tr><td>Sample 2</td><td>Multicrystalline</td><td>1 and 9×10<sup>-1</sup></td><td>0.044</td><td>1</td></tr></tbody></table>

In the following, the techniques that are more original and less common are explained in more details.

### 1.1 Structural defect characterisation

#### Electron backscatter diffraction microscopy

To acquire precise insights about the grain structure and about the presence of sub-grains, electron backscatter diffraction (EBSD) measurements are conducted using a FEG-SEM JEOL JSM 7001F equipped with a HKL Nordlys camera with a 7.0 µm step size. For this technique, the sample needs to be mirror-polished with a preparation generating a minimum of defaults. It is illuminated with an electron beam of energy 20 keV which allows penetration of around 10 nm from the surface. The silicon sample is tilted at 70° in order to obtain a maximum number of electrons backscattered towards the direction of the detector. The electron diffraction patterns (Kikuchi bands) are processed in the Channel5 software and then indexed. It allows to generate multiple maps pertaining to crystallographic orientations.

In the inverse pole figure maps, the colour representation of each pixel corresponds to the crystallographic orientation of the {hkl} plane, perpendicular to one of the axes within the reference
frame. Coincidence site lattice (CSL) maps are reconstructed to emphasise boundaries with specific characteristics. Within these maps, specific attention is given to highlight the main twin boundaries, including Σ3<111>, Σ9<110>, Σ27a<110>, Σ27b<210>. The twin boundaries are defined using the Brandon's criterion, which provides a range of angles around the ideal angle for a specific twin boundary [11] corresponding to twist rotations about the corresponding axis (hkl): (60.80 ± 8.66)°, (38.94 ± 5.00)°, (31.58 ± 2.89)°, and (35.42 ± 2.89)° for Σ3<111>, Σ9<110>, Σ27a<110>, Σ27b<210>, respectively [12]. In addition, Sub-GBs that correspond to misorientations along the boundary lower than 5° are revealed by this method. It is worth noting that the lowest misorientation this technique can detect is 0.5°.

Stereographic projections of relevant crystallographic planes can also be investigated to characterise the
possible symmetry configurations of the GBs and to determine the crystallographic common plane of
adjacent twins.

The use of EBSD compared for example to electron microscopy offers a significant advantage by
enabling the analysis of structural defects, including Sub-GBs with a misorientation down to 0.5°, even
in large-sized samples.

## 1.2 Chemical measurements

Secondary ions mass spectrometry (SIMS)

To characterise the chemical distribution of the impurities, Dynamic-SIMS (D-SIMS) measurements
(Figure 1) are conducted using a Camera IMS 7f instrument equipped with a Cs primary ion beam
source. The sample chamber reaches a vacuum of ~1×10⁻⁹ torr before starting the bombardment of the
sample with a beam of 15keV Cs⁺ primary ions. A raster of 130 µm is used in the present measurements,
where secondary ions are extracted from the central part of the crater. Their intensity is then detected by
the mass spectrometer for every crater. The raw secondary ion intensity measurements are converted
into quantitative impurity concentrations by using the calibration factor known as the RSF (relative
sensitive factor). In SIMS it varies from an element to another, it depends as well on the primary ions
beam used in the sputtering. In our experiments, the RSF for the analysed elements are the following:
[C]= 3.10¹³ atoms/cm³, [O]= 8.10¹² atoms/cm³ and [Cu]= 7.10¹⁴ atoms/cm³.

For each position along the investigated lines, several measurements are performed along the depth of
the crater area, as illustrated in Figure 1.c. Dynamic-SIMS provides chemical information up to a depth
of 1-2 µm, in contrast to static-SIMS, which only offers insights onto the sample surface. To obtain a
line profile, consecutive depth profiles are recorded along a line of the sample (Figure 1.a).

To avoid surface contamination errors, the first measurements are not taken in consideration and a mean
value is calculated for every crater as well as the standard deviation (Figure 1.c yellow area). SIMS
enables the determination of the total concentration of impurities, even when precipitated, across the
measurement area, allowing for the visualisation of their variations. The SIMS technique used in this
study is characterised by its ability to perform surface scan while minimising noise. It also provides
highly accurate concentration profiles, even at ppm or ppb levels, due to its sensitivity. So, although it
is a rather common methods, the particular conditions and apparatus used here are adapted to the
characterisation of low concentration levels usually found in high quality PV Si material.

Despite the precision of the values obtained through this technique, a notable drawback is its destructive
nature, preventing the execution of additional tests on the same area, as the sample surface is sputtered
during the analysis. Thus, the longer the analysis the deeper the crater in the sample.
<figure><img src="image_3.png" /><figcaption>**Figure 1:** SIMS measurement protocol to get impurity concentration line-scan-profiles reducing surface artefacts; a) the sample is irradiated with a primary ions beam, b) secondary ions sputtered from the sample are detected using a mass spectrometer, c) a profile of the impurity density is plotted for each scanned crater, d) a mean impurity density profile along the scanned line is plotted.</figcaption></figure>

Fourier transform infrared spectroscopy (FTIR) and Micro-FTIR (μFTIR)

FTIR measurements are carried out in an infrared spectrometer that is essentially composed of a continuous-spectrum infrared source and a Michelson interferometer, to investigate in particular the distribution of oxygen in interstitial position. By comparison with SIMS measurements, this method allows to specify if oxygen is found in interstitial position or in precipitate form.

The FTIR technique is based on the vibration modes that are characteristics of the binding between
atoms. Indeed, each type of binding vibrates at a characteristic wavenumber, 1107 cm⁻¹ for the Si-Oᵢ
bond and 605.5 cm⁻¹ for the Si-Cₛ bond as an example. As a result, the absorbance peak height is
proportional to the impurity concentration.

This technique involves directing an infrared beam from an infrared source to a Michelson interferometer. The beam splitter of the interferometer divides the beam in two. Half of the light beam is directed to a fixed mirror and the other half to a moving mirror, then the two beams are recombined and the resulting beam passes through the sample. A detector records the signal after the sample in the form of an interferogram. A Fourier transform processing allows to obtain the absorbance spectrum as a function of the wavenumber [13]. From the absorbance peak height at one specific wavelength, the Beer-Lambert equation allows to determine the impurity concentration.

Measurements are carried at room temperature with a diaphragm and a 6 mm diameter infra-red probe.
The sample needs to be 500-2000 µm thick and both sides mirror-polished to limit light diffusion on
sample surfaces. The precision of the obtained results depends on the sample quality and thickness. The
thicknesses of samples 1 and 2 are 500 µm and 440 µm, respectively.

As concern the μ-FTIR, a FTIR spectrometer is connected to an infrared microscope. The infrared light beam of the spectrometer is directed into the microscope to realise an infrared absorbance spectrum map of a specific sample area. This particular system works between 4000 cm⁻¹ and 900 cm⁻¹. An array of 64 x 64 detectors allows characterising an area of 170 × 170 µm² with a spatial resolution of 10 µm. For each pixel, the absorbance peak height or the absorbance peak area at one specific wavelength can be
extracted from the data. Due to the spectral range of the detector, only $O_i$ maps can be obtained. Indeed, the characteristic wavenumber of Si- $C_s$ is out of the map detector range [900 - 4000 cm⁻¹].

The originality of µ-FTIR stands in the microscopy and on the high spatial resolution that allows to build $O_i$ distribution maps.

## Energy Dispersive X-ray Spectroscopy

Scanning Electron Microscopy coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDS) is a powerful analytical technique that combines high-resolution imaging with elemental chemical analysis. SEM uses a focused electron beam to scan the surface of a sample. The interaction between the electrons and the sample produces various signals, including secondary electrons, backscattered electrons, and characteristic X-rays. The X-rays emitted by the sample are specific to each element and are used by the EDS for chemical quantification of the element. For our analysis, a spectral range between 0 and 10 keV is used to illuminate the sample. The penetration depth is between 0.3 and 3µm and the number of channels is fixed to 2048 which corresponds to a channel width of 5 eV. However, the EDS technique is not adapted to the detection of light impurities and this is the main reason why SIMS (for global concentration) and FTIR (for interstitial or substitutional positions) are also used in this work.

## 1.3 Charge transport characterisation and investigation

Electrical tests are conducted to evaluate the charge transport properties and the effect of the structural defect-impurity interaction on it. On one hand, photoluminescence maps enable localising crystallographic defects that are responsible of non-radiative recombination, and on the other hand, surface photovoltage is used to estimate the relaxation time of the recombination of excess minority carriers.

### Photoluminescence (PL)

PL is employed to characterise the radiative band-to-band recombination of the minority carriers. Electrons are excited from the valence band to the conduction band of the material by a laser whose energy is larger than the band gap and that corresponds, in our set up, to a wavelength of 915 nm which allows a penetration of around 40-50 µm in the material. Subsequently, the photoexcited carriers relax and spontaneously recombine with holes. The emission of photons driven by band-to-band recombination is detected by a CCD camera, which makes it possible to construct a PL map to localise specific areas where the radiative rate of the electron-hole pair recombination is reduced, giving rise to darker contrasts on the maps [15].

### Surface photo voltage (SPV)

SPV represents a non-destructive, contactless characterisation method widely employed for investigating various semiconductor materials. It offers insights into parameters such as bandgap, minority carrier relaxation time, diffusion length, and surface band bending variations [16].

SPV measurements are conducted on the TEMPO beamline at the synchrotron SOLEIL. The soft X ray beamline optimised to perform time resolved photoemission spectroscopy covers the energy range between 50 to 1500eV [17]. The time profile of the SPV is obtained after core level energy shift under laser excitation of the sample.

A modulated laser of 640 nm wavelength is used. XPS spectra are collected, when the laser is turned on. The electrons are excited by the laser from the valence band to the conduction band, consequently
altering the surface energy of the sample. The shift of the SPV is controlled by the shift of the photoemission peak and the recombination time of these charge carriers is measured after the laser is switched off (Figure 2). The photoemission measurement is performed using a high energy resolution n MBS A-1 photoelectron analyser [18]. The modification in charge of the surface is monitored as a function of time throughout the experiment by the delay line detector integrated into the photoelectron analyser. Experimental details can be found elsewhere [19,20].

In the present work, by measuring Si2p core level spectra at a photon energy of 350 eV, the collected photoelectrons have a kinetic energy of about 250 eV. The inelastic mean free path of electron in SiO2 is estimated to be about 1 nm, so it is worth reminding that this technique studies only the sample surface. By studying the variation of the binding energy (E<sub>b</sub>) of the 2p orbitals of Si over time, the relaxation time linked to the minority carrier lifetime can be evaluated.

A representative spectrum is depicted in Figure 2.e (black curve). Following laser photoexcitation, it is assumed that the recombination rate is limited by the barrier crossing induced by band curvature from thermionic emission [21]. ΔV<sub>SP</sub> decreases as recombination proceeds, and thus the relaxation time of photoexcited carriers increases over time as the surface potential returns to equilibrium.
As a consequence, this spectrum is fitted using the thermionic emission equation [22] (Figure 2.e red curve)

<figure><img src="image_4.png" /><figcaption>**Figure 2:** Principle of the SPV technique; LASER on a) creation of electron-hole pairs; b) accumulation of excess charges; LASER off c) start of relaxation by thermionic effect and recombination via a surface state; d) complete relaxation, return to equilibrium. This figure is adapted from Silly et al; e) Representative spectrum (black) and corresponding adjusted curve (red) by the thermionic emission equation. [23].</figcaption></figure>

In our study, it is used to determine a relative carrier lifetime (or recombination velocity) of minority
carriers, a metric that can be related to more conventional minority carrier lifetime measurements
although the values which correspond to a relaxation time measurement are not comparable
quantitatively. The main interest and singularity of the SPV is that it can be associated as well to X-ray
photoemission (XPS) to characterise impurities on the same sample which is one of the prospects of
the current study.
# 3. Results and discussion:

## 3.1 Grain structure and sub-grains:

Figures 3.a and b show inverse pole figure maps of samples 1 and 2, respectively. Sample 1 exhibits a monocrystalline structure for the usual criteria which consists in defining grain boundaries only for misorientations above 5° in silicon. However, from this map, it can be clearly seen that sub-grains are present within the sample. In Figure 3.a, variations in orientation are observed, with lower misorientations in the upper part compared to the lower part of the sample. Oppositely, sample 2 has a clear multi-crystalline grain structure.

<figure><img src="image_5.png" /><figcaption>**Figure 3:** Inverse pole figure maps in the sample normal direction (growth direction) obtained by EBSD: a) sample 1, b) sample 2, c) colour code of the grain orientation and sample orientation.</figcaption></figure>

Figure 4.a shows the CSL map and demonstrates that the sample 1 is a mono-crystal constituted of sub-grains with a misorientation between 1° and 4°. Figure 4.b illustrates that sample 2 is characterised by various structural defects, such as Σ3, Σ9 and Σ27a twin boundaries and sub-grains with a misorientation between 1° and 3°. The latter are in a higher amount than other defects.

Within Si ingots, the predominant twin boundaries manifest as Σ3 type. This high frequency is due to their lower formation energy, as established in prior studies, showcasing their exclusive nucleation during solidification in samples having very low impurity levels [24,25]. Twin boundaries of higher orders, including Σ9 and Σ27 result from successive twinning and competition in pure samples. The presence of impurities can further promote the nucleation of higher-order twin boundaries, as studied in the case of carbon contamination [26].

The singularity of these specimens lies in the presence of Sub-GBs. The latter can originate either from the polygonisation of dislocations within monocrystalline seeds [3,27] or from the thermal treatments and mechanical shocks on the seeds [28].
<figure><img src="image_6.png" /><figcaption>**Figure 4:** Grain and Sub-GB maps obtained by EBSD: a) sample 1, b) sample 2, c) colour code of the grain boundary types, d) colour code of the sub-GBs corresponding to misorientation range. The white dotted rectangles correspond to areas studied by SIMS (see figures 5 and 6) RAGB stands for random angle grain boundaries.</figcaption></figure>

## 3.2 Relating impurity characterisation with structural defects

The global concentrations of oxygen in interstitial position and carbon in substitutional position measured in our samples are exhibited in table 2.

<table><caption>**Table 2:** FTIR global measurements of interstitial oxygen and substitutional carbon in both samples 1 and 2.</caption><thead><tr><td>Global concentration (at/cm³)</td><td>Sample 1 (e= 500µm)</td><td>Sample 2 (e= 440µm)</td></tr></thead><tbody><tr><td>Interstitial oxygen (Oᵢ)</td><td>9.8 × 10¹⁷</td><td>7.2 × 10¹⁷</td></tr><tr><td>Substitutional Carbon (Cₛ)</td><td>2.5 × 10¹⁷</td><td>2.0 × 10¹⁷</td></tr></tbody></table>

To have an insight of the concentration of impurities at the level of the defects, SIMS measurements are performed along lines crossing the defects. They allow obtaining the total concentration of oxygen (both in interstitial Si sites and in precipitates) and carbon (both in substitutional sites and in precipitates such as SiC). On top of C and O, Cu concentration is also measured by SIMS as a representative fast diffusing metallic impurity. For all the SIMS measurements in the following, it is worth mentioning that it is not possible to use the same concentration scale for metallic and light impurities profiles, due to the difference in order of magnitude.

Figure 5 presents SIMS spectra obtained on sample 1. The zone determined by the black dotted rectangle on the CSL mapping shows a high concentration of oxygen (1.2×10²⁰ atoms/cm³), which is two orders of magnitude higher than the value measured by FTIR globally (9.8×10¹⁷ atoms/cm³ in table 1) in the most misoriented zone of sample 1 (Figure 5.a). This peak reveals local segregation in this strongly misoriented region. The peak does not correspond to a precise Sub-GB on the EBSD maps. However, it might be located in the region of high misorientation which could originate from the presence of structural defects like dislocations. This will be further discussed in section 3.3 showing the PL map of the same area. Additionally, in the same scanned area, two peaks with significant carbon concentrations are observed (Figure 5). The first peak corresponds to the highest carbon concentration of 3.2×10¹⁸ atoms/cm³ while the second peak corresponds to a value of 2.2×10¹⁸ atoms/cm³ of the same order of magnitude. This second peak corresponds exactly to the position of the oxygen peak observed in Figure 6, confirming the segregation of impurities in this zone.
In Figure 5, it is also observed that at the position X₁, significant concentrations of copper and carbon impurities are present. Additionally, at the position X₂, all three impurities, copper, oxygen, and carbon, exhibit maximum concentrations. This observation suggests the possibility of impurity co-precipitation.

<figure><img src="image_7.png" /><figcaption>**Figure 5:** Sample 1; total impurity concentration obtained by SIMS and compared to EBSD; a) CSL map, SIMS profile: b) copper and carbon, c) oxygen. The investigated area corresponds to the white rectangle in Figure 4.a.</figcaption></figure>

SIMS measurements are also conducted on sample 2, more precisely in the white frame in Figure 4.b along two lines in two different areas of the sample (X and Y in Figure 6 and Figure 7). The D-SIMS spectra of O, C, and Cu are superimposed in Figures 6 and 7 in relation to the grain structure and grain boundary types. In the X rectangle, the three peaks corresponding to the maximum concentrations of impurities overlap similarly to what is observed in sample 1 and could correspond to impurity co-precipitation.

These peaks align with the position of a Σ9{122}₁,₂ twin boundary and seems to correspond to it. Oppositely, there is no measurable concentration at the level of the Σ3 twin boundaries. The latter corresponds to higher crystalline quality compared to Σ9{122}₁,₂ twin boundaries which could explain this observation. Segregation of Cu was detected in former work at the level of Σ3 twin boundaries and associated to copper silicide [29,30] which is not the case here.

<figure><img src="image_8.png" /><figcaption>**Figure 6:** Sample 2; total impurity concentration obtained by SIMS and compared to EBSD on zone X; a) CSL map, SIMS profile: b) oxygen and carbon, c) copper. The investigated area corresponds to a part of the white rectangle in Figure 4.b.</figcaption></figure>
Figure 7 shows the SIMS spectra of the three impurities along a second line in the measurement zone. Despite the presence of a Σ3{112}$_{1,2}$ twin boundary and of a sub-grain boundary along the measurement line, stable concentrations of light impurities are observed.

There is a significant peak for C and O only at the intersection of a random angle grain boundary (RAGB) and a Σ3 {111}$_{1,2}$. Another peak of O and Cu is observed but inside the grain close to a RAGB. A smaller Cu peak is measured at the level of the Σ3{112}$_{1,2}$ and could be related to the formation of copper silicide at the level of these grain boundaries as mentioned above [30] all the more as this particular Σ3 twin boundary is not the most densely packed [31].

<figure><img src="image_9.png" /><figcaption>**Figure 7:** Sample 2; total impurity concentration obtained by SIMS and compared to EBSD on zone Y; a) CSL map, SIMS profile: b) carbon, c) copper, d) oxygen. The investigated area corresponds to a part of the white rectangle in Figure 4.b)</figcaption></figure>

**3.3 Relating impurity characterisation, structural defects and charge transport evaluation**

The regions chosen for SIMS scans are lines that cross many times sub-GBs for sample 1 and that are electrically active in PL maps. Then, the same regions are studied in µFTIR to compare all the data together. As for sample 2, since there is a variety of GB types, we choose to measure the concentration along two lines, crossing different grain boundaries.

Figure 8 exhibits the PL map of sample 1 in the same area as in Figure 5. Notably, the SIMS peaks in positions X$_{1,1}$ and X$_{1,2}$ in Figure 8. c and d. correspond to regions of high non radiative band to band recombination (black contrast in Figure 8.a). These high recombination regions can be related to defects where impurities segregate although they are not revealed by EBSD. These defects could correspond to dislocations.
<figure><img src="image_10.png" /><figcaption>**Figure 8:** Sample 1; Electrical activity PL related to total impurity concentration (same area as in figure 5); a) PL mapping, b) CSL map, SIMS spectra (same area as figure 5); c) carbon and copper, d) oxygen.</figcaption></figure>

µFTIR is used on the same sample to obtain localised map of interstitial oxygen Oᵢ. The maps are displayed by the coloured maps superimposed on the PL mapping in Figure 9. In this figure, the warm colours indicate a high quantity of Oᵢ, whereas the cool colours indicate a low concentration of Oᵢ. These measurements on sample 1 span from blue to red which corresponds to the 8.0×10¹⁷ cm⁻³-9.6×10¹⁷ cm⁻³ range.

All maps reveal that the highest concentrations of Oᵢ align with the interior of the grains, while the lowest concentrations correspond to regions of a strong non radiative recombination, as characterised by dark PL contrast. As shown above, the latter zones correspond to the highest total concentration in oxygen measured by SIMS. These observations demonstrate that oxygen segregating at defect sites primarily exists in precipitated form, and it can be clearly seen that these regions are directly linked to intense non radiative recombination activity. This is reproducibly observed for the lowest misorientation Sub-GBs characterised in this sample. The boundaries, nearby dislocation walls, contribute to degraded electrical properties, serving as effective sites for impurity segregation [28,32,33].

<figure><img src="image_11.png" /><figcaption>**Figure 9:** PL and µFTIR maps showing depletion of Oᵢ at the Sub-GBs in sample 1. The yellow dotted rectangles show the positions of the µFTIR maps that are displayed just beside them. As a reminder, the area shown in both figures 5 and 8 is highlighted with a white frame in figure 4.a</figcaption></figure>
Furthermore, additional measurements are conducted on sample 1 to assess the material electrical properties using the SPV method. Figure 10 depicts the specific regions where relaxation time measurements are performed (highlighted by the numbered rectangles). These values represent relaxation times associated with minority carrier recombination rates. The longer relaxation time $τ$ values are observed within the grains all along the sample, which confirms and quantifies the results obtained by PL, while faster deexcitation processes arise at the grain boundary. Boundaries, either of grains or of sub-grains, are known to strongly affect the minority charge carrier relaxation [34–36].

<figure><img src="image_12.png" /><figcaption>**Figure 10:** Relaxation time measurements obtained by SPV, compared to the CSL map positions (0 to 11) on sample 1.</figcaption></figure>

To check the last assumption, the MEB-EDS technique is used. The measurements are done in zones close to the SIMS measurement line. The obtained maps typically show different elements in distinct colours or brightness levels. Areas with higher concentrations of a particular element appear brighter or more intense in that element maps. The electronic images obtained for sample 1 (Figure 11) show that there are many micro precipitates that contain C, O and copper to a lesser extent. It confirms the presence of oxides and carbides. The contrast zones in all images are at the same position emphasising the formation of micro-clusters constituted of complex precipitates.

<figure><img src="image_13.png" /><figcaption>**Figure 11:** Sample 1; images obtained by SEM-EDS, a) electronic image of the clusters, chemical distribution images; b) carbon, c) oxygen.</figcaption></figure>
The same technique is used for sample 2 again close to the SIMS measurements line. Many clusters are observed. Figure 12 shows a closeup on one of the clusters: the contrast in the images confirms again that these micro-precipitates are made of carbon and oxygen as well as other measurements carried-out in the same sample in different zones.

<figure><img src="image_14.png" /><figcaption>**Figure 12:** Sample 2; images obtained by SEM-EDS, a) electronic image of the cluster, chemical distribution; b) carbon, c) oxygen.</figcaption></figure>

**Conclusion:**

This study investigates the impact of structural defects combined with impurities on the charge transport properties in silicon samples for photovoltaic applications.

It can be inferred that the studied impurities (O, C and Cu) tend to segregate in high disordered structural defects such as GBs and Sub-GBs, in the form of precipitates or co-precipitates. Moreover, it is deduced that oxygen in particular, tend to segregate in form of precipitates at structural defects, whereas it is in interstitial position inside the grains.

The electrical properties are compromised at structural defects such as Sub-GBs. In our investigations, this degradation is directly related with the presence of O, C, and Cu.

One of the main contributions of this work is the establishment of a method that allows to compare and relate the characterisation of grain boundaries, structural defects, impurity segregation and charge transport evaluation and thus, the impact on the electrical properties. One specificity is that these investigations can be conducted on centimetre samples mono or multi-crystalline with information down to the scale of the structural defects like sub-GBs.

In particular, the novelty of our study lies in the investigation of Sub-GBs whose interaction with impurities and impact on the charge transport are less explored compared to the grain boundary case. Indeed, in Si materials for solar cells ultimate defects are nowadays dislocations or, when dislocations undergo polygonisation, Sub-GBs.

The main objective behind the selection of these particular techniques is to be able to provide maps at the scale of centimeter size samples with a spatial resolution sufficient to evidence defects as small in size and misorientation as sub-grains. Additionally, the chosen techniques allow a fine positioning to be able to compare the results extracted from the different techniques used and corresponding to structural defects, impurities and electrical properties. At last, most of the chosen techniques are non-destructive and do not require a particular preparation except for mirror polished surface state. As concern, FTIR and especially µFTIR are one of the most powerful non-destructive techniques to determine oxygen in
interstitial position and carbon in substitutional position with a detection limit that allow the
characterisation of concentrations corresponding to the current standards in Si for PV applications.

This combination of characterisation methods, which probes the sample at different scales, is versatile
and can be applied to samples issued from conventional growth processes including the dominant one,
Czochralski (Cz). It is also applicable to samples with conventional doping levels and corresponding
resistivity. These characterisation techniques can be adapted to new doping impurities as for example
gallium used as a replacement of boron in present solar cells. This method could also be extended to
other materials of interest beyond silicon.

This approach could be further extended with the use of X-ray emission to detect impurity bonds at the
same time as surface photo-voltage. Moreover, diffraction imaging methods, namely X-ray topography
and Rocking Curve Imaging, can be used as well to obtain a finer knowledge on the structural defects
(down to dislocations) *ex situ* [37] and *in situ* [26] while keeping a mesoscopic scale approach.

Acknowledgements:

The experiments at SOLEIL synchrotron on the TEMPO beamline have been made possible by
beamtime allocation for the proposal 20190088. The work has been partly supported by the FUSING
project (Phc AURORA N°45310UH) and ANR project DeFI2 (No. ANR-23-CE50-0022-01).

Keywords:

Monocrystalline silicon, solar cells, crystal growth, grain boundaries, sub-grain boundaries, recombinant activity, impurity segregation, precipitates, lifetime, growth defects, photovoltaic properties.

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