Profile estimation for Pt submicron wire on rough Si substrate from experimental data.

Opt Express

Department of Process and Chemical Engineering, University of Bremen, Badgasteiner Str. 3, D-28359 Bremen, Germany.

Published: September 2012

An efficient forward scattering model is constructed for penetrable 2D submicron particles on rough substrates. The scattering and the particle-surface interaction are modeled using discrete sources with complex images. The substrate micro-roughness is described by a heuristic surface transfer function. The forward model is applied in the numerical estimation of the profile of a platinum (Pt) submicron wire on rough silicon (Si) substrate, based on experimental Bidirectional Reflectance Distribution Function (BRDF) data.

Download full-text PDF

Source
http://dx.doi.org/10.1364/OE.20.021678DOI Listing

Publication Analysis

Top Keywords

submicron wire
8
wire rough
8
profile estimation
4
estimation submicron
4
rough substrate
4
substrate experimental
4
experimental data
4
data efficient
4
efficient forward
4
forward scattering
4

Similar Publications

Results on the magnetic domain walls in rapidly solidified magnetostrictive and non-magnetostrictive amorphous submicronic wires are reported. Utilizing Lorentz transmission electron microscopy (LTEM) for the first time in this context, we have visualized and analyzed the domain walls in such ultra-thin amorphous wires. All the investigated samples display vortex magnetic domain walls, regardless of wire composition or diameter.

View Article and Find Full Text PDF

Network on chip (NoC) is the main solution to the communication bandwidth of a multi-processor system on chip (MPSoC). NoC also brings more route requirements and is highly prone to errors caused by crosstalk. Crosstalk has become a major design problem in deep-submicron NoC communication design.

View Article and Find Full Text PDF

Direct fabrication of high-quality vertical graphene nanowalls on arbitrary substrates without catalysts for tidal power generation.

Nanoscale

October 2022

Jiangsu Key Laboratory of Materials and Technology for Energy Conversion, College of Materials Science & Technology, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China.

The non-catalytic preparation of high-quality vertical graphene nanowalls (VGN) and graphene-based high output power hydrovoltaic effect power generation devices has always been difficult to achieve. In this work, we successfully prepared VGN with defect density, few layers and submicron domain size on a variety of substrates without catalysts through reasonable adjustment of growth conditions by the hot-wire chemical vapor deposition (HWCVD) method. The Raman test of the VGN prepared under optimal conditions showed that its / value was less than 1, and / was more than 2.

View Article and Find Full Text PDF

Submicrometric magnetic amorphous wires are good candidates for future development of miniaturized sensors and magnetic logic applications. Here we report the results of an in-depth investigation of magnetization switching in rapidly solidified nearly zero magnetostrictive (CoFe)SiB amorphous samples with diameters of the actual magnetic wires between 300 and 450 nm. All samples were found to be magnetically bistable, displaying characteristic rectangular hysteresis loops.

View Article and Find Full Text PDF

Nonmetallic inclusion (NMI) populations in superelastic (SE) Nitinol fine wires (<140 μm in diameter) were investigated by combining plasma focused ion beam (PFIB) serial sectioning with scanning electron microscopy (SEM). High purity (HP)—lower oxygen content and standard purity (SP)—higher oxygen content Nitinol wires were sectioned and imaged. The three-dimensional (3D) reconstructions provided more complete connectivity of NMIs and pores as well as information about the distribution of the features within the wire volume that is not possible with traditional two-dimensional (2D) imaging techniques.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!