Abstract
The energy demand for computing and data storage will continue to rise exponentially unless non-traditional computing architectures and innovative storage solutions are explored. Low-energy computing, including compute-in-memory architectures, has the potential to address these energy and environmental challenges and, in particular, tetrahedral (wurtzite-type) ferroelectrics are promising options for both performance and integration with existing semiconductor processes. The AlScN alloy is among the few tetrahedral materials that exhibit ferroelectric switching, but the electric field required to switch the polarization i.e., the coercive field, E_c, is on the order of MV/cm, which is about 1–2 orders of magnitude higher than more traditional oxide perovskite ferroelectrics (E_c < 100 kV/cm). Instead of further engineering AlScN and related alloys, we explore the alternative route of computationally identifying new materials with switching barriers lower than AlN while still possessing high enough intrinsic breakdown fields. Going beyond binary compounds, we explore the search space of multinary compounds with wurtzite-type structures. Through this large-scale search, we identify four promising ternary nitrides and oxides, including Mg2PN3, MgSiN2, Li2SiO3, and Li2GeO3, for future experimental realization and engineering. In > 90% of the considered multinary materials, we identify unique switching pathways and non-polar structures that are distinct from the commonly assumed switching mechanism in AlN-based materials. Our results disprove the existing design principle based on reduction of wurtzite c/a lattice parameter ratio while supporting two emerging design principles – ionicity and bond strength.
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Structures along the calculated polarization switching pathways
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