Combinatorial Synthesis of Magnesium Tin Nitride Semiconductors.

Nitride materials feature strong chemical bonding character that leads to unique crystal structures, but many ternary nitride chemical spaces remain experimentally unexplored. The search for previously undiscovered ternary nitrides is also an opportunity to explore unique materials properties, such as transitions between cation-ordered and -disordered structures, as well as to identify candidate materials for optoelectronic applications. Here, we present a comprehensive experimental study of MgSnN2, an emerging II-IV-N2 compound, for the first time mapping phase composition and crystal structure, and examining its optoelectronic properties computationally and experimentally. We demonstrate combinatorial cosputtering of cation-disordered, wurtzite-type MgSnN2 across a range of cation compositions and temperatures, as well as the unexpected formation of a secondary, rocksalt-type phase of MgSnN2 at Mg-rich compositions and low temperatures. A computational structure search shows that the rocksalt-type phase is substantially metastable (>70 meV/atom) compared to the wurtzite-type ground state. Spectroscopic ellipsometry reveals optical absorption onsets around 2 eV, consistent with band gap tuning via cation disorder. Finally, we demonstrate epitaxial growth of a mixed wurtzite-rocksalt MgSnN2 on GaN, highlighting an opportunity for polymorphic control via epitaxy. Collectively, these findings lay the groundwork for further exploration of MgSnN2 as a model ternary nitride, with controlled polymorphism, and for device applications, enabled by control of optoelectronic properties via cation ordering.

Ternary nitride semiconductors in the rocksalt crystal structure.

Inorganic nitrides with wurtzite crystal structures are well-known semiconductors used in optical and electronic devices. In contrast, rocksalt-structured nitrides are known for their superconducting and refractory properties. Breaking this dichotomy, here we report ternary nitride semiconductors with rocksalt crystal structures, remarkable electronic properties, and the general chemical formula Mgx TM 1-xN (TM = Ti, Zr, Hf, Nb). Our experiments show that these materials form over a broad metal composition range, and that Mg-rich compositions are nondegenerate semiconductors with visible-range optical absorption onsets (1.8 to 2.1 eV) and up to 100 cm2 V-1⋅s-1 electron mobility for MgZrN2 grown on MgO substrates. Complementary ab initio calculations reveal that these materials have disorder-tunable optical absorption, large dielectric constants, and electronic bandgaps that are relatively insensitive to disorder. These ternary Mgx TM 1-xN semiconductors are also structurally compatible both with binary TMN superconductors and main-group nitride semiconductors along certain crystallographic orientations. Overall, these results highlight Mgx TM 1-xN as a class of materials combining the semiconducting properties of main-group wurtzite nitrides and rocksalt structure of superconducting transition-metal nitrides.

COMBIgor: Data-Analysis Package for Combinatorial Materials Science.

Combinatorial experiments involve synthesis of sample libraries with lateral composition gradients requiring spatially resolved characterization of structure and properties. Because of the maturation of combinatorial methods and their successful application in many fields, the modern combinatorial laboratory produces diverse and complex data sets requiring advanced analysis and visualization techniques. In order to utilize these large data sets to uncover new knowledge, the combinatorial scientist must engage in data science. For data science tasks, most laboratories adopt common-purpose data management and visualization software. However, processing and cross-correlating data from various measurement tools is no small task for such generic programs. Here we describe COMBIgor, a purpose-built open-source software package written in the commercial Igor Pro environment and designed to offer a systematic approach to loading, storing, processing, and visualizing combinatorial data. It includes (1) methods for loading and storing data sets from combinatorial libraries, (2) routines for streamlined data processing, and (3) data-analysis and -visualization features to construct figures. Most importantly, COMBIgor is designed to be easily customized by a laboratory, group, or individual in order to integrate additional instruments and data-processing algorithms. Utilizing the capabilities of COMBIgor can significantly reduce the burden of data management on the combinatorial scientist.