Epitaxial MgSnN 2 on 4H‐SiC (0001): An Earth‐Abundant Nitride for Green Optoelectronics and Photovoltaics

ABSTRACT Group II–IV‐Nitrides has emerged as a novel class of Earth‐abundant semiconductors. Owing to their tunable bandgaps these materials are attractive candidates for replacing expensive Ga‐based alloys in photovoltaics and green‐gap optoelectronics. In this work, epitaxial growth of MgSnN 2 thin films on 4H‐SiC (0001) substrates by direct current magnetron sputtering is demonstrated. Mg and Sn metal targets were co‐sputtered in a nitrogen‐containing atmosphere at growth temperatures up to 500 °C. The film composition is varied from Sn‐rich to stoichiometric and Mg‐rich regimes. X‐ray diffraction and cross‐sectional transmission electron microscopy confirm the MgSnN 2 layers grow epitaxially in a wurtzite crystal structure, exhibiting the epitaxial relationships with the substrate: MgSnN 2 [0001]//4H‐SiC [0001] and MgSnN 2 []//4H‐SiC[]. Improved crystalline quality is observed for higher deposition temperatures and stoichiometric composition, as evidenced by the narrowing of rocking curve's linewidths. Optical characterization reveals a high absorption coefficient (∼10 5 cm − 1 ) in the visible spectrum. Photoluminescence measurements show emission peaked at ∼2.4 eV, highly desirable for optoelectronic devices in the challenging green spectral region. These results establish MgSnN 2 as an earth‐abundant, environmentally‐friendly material, structurally compatible with III‐nitrides, with potential for cost‐efficient components in sustainable optoelectronics and photovoltaics.

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Journal
Advanced Optical Materials
Published
2026-09-10
DOI
https://doi.org/10.1002/adom.71758
Primary Topic
Machine Learning in Materials Science
Type
article
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Epitaxial MgSnN 2 on 4H‐SiC (0001): An Earth‐Abundant Nitride for Green Optoelectronics and Photovoltaics

Dat Q. Tran, D. Gogova, Vanya Darakchieva, Per Eklund et al.
Advanced Optical Materials
Machine Learning in Materials Science
article

Epitaxial MgSnN 2 on 4H‐SiC (0001): An Earth‐Abundant Nitride for Green Optoelectronics and Photovoltaics

Dat Q. Tran, D. Gogova, Vanya Darakchieva, Per Eklund, B. Pécz, Danial Shafizade, Arnaud le Febvrier, Ching‐Lien Hsiao, Niraj K. Singh, V. Stanishev, A. Kovács, Minho Kim, A. Sulyok, K. Frey
article en

Abstract

ABSTRACT Group II–IV‐Nitrides has emerged as a novel class of Earth‐abundant semiconductors. Owing to their tunable bandgaps these materials are attractive candidates for replacing expensive Ga‐based alloys in photovoltaics and green‐gap optoelectronics. In this work, epitaxial growth of MgSnN 2 thin films on 4H‐SiC (0001) substrates by direct current magnetron sputtering is demonstrated. Mg and Sn metal targets were co‐sputtered in a nitrogen‐containing atmosphere at growth temperatures up to 500 °C. The film composition is varied from Sn‐rich to stoichiometric and Mg‐rich regimes. X‐ray diffraction and cross‐sectional transmission electron microscopy confirm the MgSnN 2 layers grow epitaxially in a wurtzite crystal structure, exhibiting the epitaxial relationships with the substrate: MgSnN 2 [0001]//4H‐SiC [0001] and MgSnN 2 []//4H‐SiC[]. Improved crystalline quality is observed for higher deposition temperatures and stoichiometric composition, as evidenced by the narrowing of rocking curve's linewidths. Optical characterization reveals a high absorption coefficient (∼10 5 cm − 1 ) in the visible spectrum. Photoluminescence measurements show emission peaked at ∼2.4 eV, highly desirable for optoelectronic devices in the challenging green spectral region. These results establish MgSnN 2 as an earth‐abundant, environmentally‐friendly material, structurally compatible with III‐nitrides, with potential for cost‐efficient components in sustainable optoelectronics and photovoltaics.

Advanced Optical Materials
Linköping University (SE), Uppsala University (SE), Laboratoire de physique des Solides (FR), Ernst Ruska Centre (DE), Engineering Link (Canada) (CA), HUN-REN Centre for Energy Research (HU), Stanford University (US)
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Openalex Percentile: Top 24%
Machine Learning in Materials Science
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