A novel technique for microstructure characterization of garnet films
Source abstract
Abstract
Abstract Bismuth-substituted iron garnet exhibits remarkable magneto-optical properties in magnetic photonic crystal (MPC) structures, and has the potential to improve performance in optical integrated circuits for communication networks and optical sensing applications. The microstructure and thickness of these garnet films has a strong influence over their optical, magnetic and magneto-optic (MO) properties. The thickness of these films has been measured indirectly via optical interference and ellipsometry, which are unable to map thickness variations due to surface roughness of both the film and the substrate. However, little direct observation has been carried out to provide detailed information about the microstructure of the garnet films, especially in cross-section. All these have resulted in a lack of understanding of processing–microstructure–MO property relationship, which in turn impedes the development of high-performance garnet films. The combined use of focused ion beam (FIB) milling and transmission electron microscopy (TEM), described as the FIB–TEM method hereafter, provides a reliable, direct means of characterization of site-specific micron-sized regions of a specimen within a reasonable length of time. A sound understanding of processing–microstructure–MO property relationship enabled by FIB–TEM method is crucial in the design of the manufacturing processes for such functional films as well as nanostructures that have garnet layers. This paper reports the experimental procedures of FIB–TEM technique for characterization of these films and discusses the strength and issues associated with its application on (Bi, Dy) 3 (Fe, Ga) 5 O 12 garnet films.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Magneto-Optical Properties and Applications · Photonic Crystals and Applications · Magnetic properties of thin films
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (26)
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