We report the fabrication and characterization of porous silicon templates for electrodeposition of high aspect ratio one-dimensional metallic nanostructures (nanowires/nanoparticles) in them. Even though nanostructures/nanowires in the past have been fabricated in alumina, polymer or silica templates, the advantages of this approach are the possibility for seamless integration of nanostructures with other silicon components, and silicon based sensors because of better physical and electrical interconnection between the nanostructure and the silicon substrate. In this work, fabrication and characterization of nanowires/nanostructures such as single-segment Ni–Fe and Au and two-segment Ni–Fe/Au electrodeposited in the porous silicon template are presented. The templates with ordered and controlled nanometer-sized pores, 40 nm and 290 nm in diameter, were created through porous Si etching. The morphology, composition and structural characteristics of the template and of the single-segment Ni–Fe and Au and two-segment Ni–Fe/Au nanostructures of diameter 275±25 nm, length up to 100 μm and pitch of 1 μm were analyzed using scanning electron microscopy and X-ray diffraction techniques. The micrographs confirm that the plating parameters have a strong influence on morphology and composition of the structures. Further, the Ni–Fe images show the formation of both vertical and branched nanowires along with nanoparticles, from breakage/discontinuous growth of nanowires. Ni–Fe nanostructures were further analyzed for temperature-dependent magnetization and magnetization vs. magnetic field measurements using a commercial physical property measurement system. They reveal no magnetic anisotropy of the nanostructures probably due to a balance between ‘reduced’ shape anisotropy from branched and rough pore surfaces and magnetocrystalline anisotropy.
Porous Silicon Magnetic Anisotropy Magnetocrystalline Anisotropy Metallic Nanostructures Nickel Silicide
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access