ABSTRACT
This report is on studies directed at the nature of magneto-electric (ME) coupling by ferromagnetic resonance (FMR) under an electric field in a coaxial nanofiber of nickel ferrite (NFO) and lead zirconate titanate (PZT). Fibers with ferrite cores and PZT shells were prepared by electrospinning. The core-shell structure of annealed fibers was confirmed by electron- and scanning probe microscopy. For studies on converse ME effects, i.e., the magnetic response of the fibers to an applied electric field, FMR measurements were done on a single fiber with a near-field scanning microwave microscope (NSMM) at 5-10 GHz by obtaining profiles of both amplitude and phase of the complex scattering parameter S11 as a function of bias magnetic field. The strength of the voltage-ME coupling Av was determined from the shift in the resonance field Hr for bias voltage of V = 0-7 V applied to the fiber. The coefficient Av for the NFO core/PZT shell structure was estimated to be - 1.92 kA/Vm (- 24 Oe/V). A model was developed for the converse ME effects in the fibers and the theoretical estimates are in good agreement with the data.
ABSTRACT
We develop empirical models to predict the contribution of topographic variations in a sample to near-field scanning probe microwave microscopy (NSMM) images. In particular, we focus on |S11| images of a thin Perovskite photovoltaic material and a GaN nanowire. The difference between the measured NSMM image and this prediction is our estimate of the contribution of material property variations to the measured image. Prediction model parameters are determined from either a reference sample that is nearly free of material property variations or directly from the sample of interest. The parameters of the prediction model are determined by robust linear regression so as to minimize the effect of material property variations on results. For the case where the parameters are determined from the reference sample, the prediction is adjusted to account for instrument drift effects. Our statistical approach black is fully empirical black and thus complementary to current approaches based on physical models that are often overly simplistic.
ABSTRACT
Near-field scanning microwave microscopy offers great potential to facilitate characterization, development and modeling of materials. By acquiring microwave images at multiple frequencies and amplitudes (along with the other modalities) one can study material and device physics at different lateral and depth scales. Images are typically noisy and contaminated by artifacts that can vary from scan line to scan line and planar-like trends due to sample tilt errors. Here, we level images based on an estimate of a smooth 2-d trend determined with a robust implementation of a local regression method. In this robust approach, features and outliers which are not due to the trend are automatically downweighted. We denoise images with the Adaptive Weights Smoothing method. This method smooths out additive noise while preserving edge-like features in images. We demonstrate the feasibility of our methods on topography images and microwave |S11| images. For one challenging test case, we demonstrate that our method outperforms alternative methods from the scanning probe microscopy data analysis software package Gwyddion. Our methods should be useful for massive image data sets where manual selection of landmarks or image subsets by a user is impractical.
