Structural and optical properties of magnetron-sputtered Er-doped AlN films grown under negative substrate bias
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Abstract
Abstract In seeking suitable host-microstructures enhancing photoluminescent (PL) efficiency of reactively sputtered Er-doped AlN films deposited under varying negative substrate bias, we observed that the films’ Er-emitted PL intensity at the wavelength of 1.54 μm builds up with bias up 50 V, before falling sharply and collapsing for U above 90 V. We studied for further insight the bias-induced effects on the film microstructural and optical properties. The transmission electron microscope images of these films biased up to ∼50 V showed improved film crystallographic quality with wide and long columnar microstructures. The columnar crystallized volumes therein are maximized around a particular bias value around 90 V, above which they have finely grained nano-granular microstructures. This film-microstructural evolution concurs well with the film refractive index n and extinction coefficient k obtained from ellipsometry. Both surge for films biased from ∼50 V to ∼90 V. These correlating evidences point out that adequate adatom mobility gained under low-energy ion bombardment with bias up to 50 V properly configures the AlN Wurzite planes while leaving behind fewer sites for PL-quenching defects. Meanwhile, Urbach tail states associated with other visible-light absorbing defects created by some material disorder in appropriate locations within the columns should have absorb the exciting photon energy and transfer it efficiently to the emitting Er3+ ions. These evidences further indicate that the newer defects created under bias above 100 V act as non-radiative centers in putting out the PL. We suggest that these latter defects would be located with a high density, at the numerous grain boundaries of the nano-crystallized volumes.
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Research context
OpenAlex topics: GaN-based semiconductor devices and materials · Acoustic Wave Resonator Technologies · Ga2O3 and related materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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