Annealing and profile of interstitial iron in boron-doped silicon

Gao, Xin; Mollenkopf, H. C.; Yee, Sinclair S.

doi:10.1063/1.106103

Cited by 19 publications

(9 citation statements)

References 7 publications

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“…10 The corresponding paired iron fraction f are between 0.6 at 364 K and 0.15 at 394 K, consistent with the observation, made by Gao et al, 11 0 that appears near this edge, while in the neutral region most of the Fe i was supposed to be paired with boron. However, we found above that, in the investigated temperature range ͑364-394 K͒, the fraction f of paired iron in the neutral region lies between 0.15 and 0.6.…”

Section: ͓S0003-6951͑96͒01613-8͔supporting

confidence: 77%

Comment on ‘‘Iron diffusivity in silicon: Impact of charge state’’ [Appl. Phys. Lett. 66, 860 (1995)]

Heiser¹,

Mesli²

1996

Applied Physics Letters

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Section: ͓S0003-6951͑96͒01613-8͔supporting

confidence: 77%

Comment on ‘‘Iron diffusivity in silicon: Impact of charge state’’ [Appl. Phys. Lett. 66, 860 (1995)]

Heiser¹,

Mesli²

1996

Applied Physics Letters

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“…However, there are good reasons not to abandon the surface or SiN x film gettering mechanism in bottom samples entirely. Factors in favor include the SIMS data for SiN x samples, the same interstitial iron decay in non-sister samples, and previous studies that have found iron gettering to free surfaces in singlecrystal silicon [32], [33]. Trapping of interstitial iron at crystal defects in mc-Si could slow down the effective diffusion process of interstitial iron, and iron release from elsewhere in the material at 500°C could complicate the kinetics further.…”

Section: Comparison Of Annealed Iodine-ethanol and Silicon Nitridementioning

confidence: 99%

Passivation Effects on Low-Temperature Gettering in Multicrystalline Silicon

Al‐Amin

Murphy

2017

IEEE J. Photovoltaics

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Annealing at ࣘ 500°C changes minority carrier lifetime in as-grown multicrystalline silicon substantially. Part of the change arises from internal gettering of impurities, but surface passivation for lifetime measurement results in additional effects. We report experiments that aim to clarify the role of passivation. Long-term annealing (up to 60 h) is performed on silicon nitride passivated multicrystalline silicon, and lifetime and interstitial iron concentrations are monitored at each processing stage. Lifetime in all samples is improved under certain conditions, with improvements always achieved at 400°C. Increases are pronounced in low-lifetime bottom samples, with improvement by a factor of 2.7 at 400°C or 3.8 at 500°C. Important differences are found compared with our previous study with iodine-ethanol passivation. First, as-received lifetime is higher with silicon nitride not due to a substantial difference in surface recombination. Second, while interstitial iron concentrations often initially increase with iodineethanol, they tend to reduce with silicon nitride. Third, lifetime in high-lifetime samples reduces substantially with iodine-ethanol but increases with silicon nitride. Secondary ion mass spectrometry reveals high iron concentrations in annealed silicon nitride. Results are discussed in terms of gettering of impurities to, and bulk passivation arising from, silicon nitride films.

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“…Literature values are from Refs. [26][27][28][29][30][31][32] and were originally collated by Ref. [23]; acquisition methods include DLTS, thermally stimulated capacitance (TSCAP, a predecessor technique to DLTS), and Hall effect.…”

Section: Five-parameter Fit Across All Temperaturesmentioning

confidence: 99%

How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements

Kurchin

Poindexter

Vähänissi

et al. 2020

IEEE J. Photovoltaics

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Defect-assisted recombination processes are critical to understand, as they frequently limit photovoltaic (PV) device performance. However, the physical parameters governing these processes can be extremely challenging to measure, requiring specialized techniques and sample preparation. And yet the fact that they limit performance as measured by current-voltage (JV) characterization indicates that they must have some detectable signal in that measurement. In this work, we use numerical device models that explicitly account for these parameters alongside high-throughput JV measurements and Bayesian inference to construct probability distributions over recombination parameters, showing the ability to recover values consistent with previously-reported literature measurements. The Bayesian approach enables easy incorporation of data and models from other sources; we demonstrate this with temperature dependence of carrier capture cross-sections. The ability to extract these fundamental physical parameters from standardized, automated measurements on completed devices is promising for both established industrial PV technologies and newer research-stage ones.

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Annealing and profile of interstitial iron in boron-doped silicon

Cited by 19 publications

References 7 publications

Comment on ‘‘Iron diffusivity in silicon: Impact of charge state’’ [Appl. Phys. Lett. 66, 860 (1995)]

Comment on ‘‘Iron diffusivity in silicon: Impact of charge state’’ [Appl. Phys. Lett. 66, 860 (1995)]

Passivation Effects on Low-Temperature Gettering in Multicrystalline Silicon

How Much Physics is in a Current–Voltage Curve? Inferring Defect Properties From Photovoltaic Device Measurements

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