From ultrathin to bulk: decoding thickness-unrestricted ferroelectricity in Y:HfO2 via first-principles
Source abstract
Abstract
Doped hafnium oxide (HfO2) ferroelectrics show great potential in next-generation memory and compute-in-memory applications due to their compatibility with advanced silicon-based technology. Typically, HfO2 shows a reverse size effect, where the polar orthorhombic phase (PO, space group Pca21) is stabilized only at thicknesses of a few nanometers. Yttrium-doped hafnium oxide (Y:HfO2) exhibits a distinct behavior, maintaining robust polarization from ultrathin films to bulk crystals. However, the mechanism enabling Y:HfO2 ferroelectricity which is critical for expanding device scalability and performance remains unclear. In this work, the multi-field stabilization mechanisms of the PO phase are systematically investigated for bulk and thin film Y:HfO2 via first-principles calculations. The synergistic effect of composite defects combined with Y dopants and tetra-coordinated oxygen vacancies (Y+VO4), strain, and electric field significantly broadens the window of thermodynamically metastable PO phase. Notably, we find that the strain requirement can be significantly relaxed with increasing concentration of Y dopants or Y+VO4 defect pairs, revealing the feasibility of achieving ferroelectricity in bulk Y:HfO2 without substrate constraints. Moreover, we demonstrate an increase in critical thickness to stabilize the PO phase in Y:HfO2 thin film compared with pure HfO2, which is ascribed to the effects of Y+VO4 defects on the surface energy. These findings clarify the key role of Y+VO4 defects in realizing thickness-unrestricted ferroelectricity in Y: HfO2 and provide critical theoretical guidance for optimizing the fabrication process of high-performance HfO2-based ferroelectric devices.
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Research context
OpenAlex topics: Ferroelectric and Negative Capacitance Devices · Advanced Sensor and Energy Harvesting Materials · Semiconductor materials and devices
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (77)
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