Ferroelectric Domain Wall Injection

Ferroelectric domain wall injection has been demonstrated by engineering of the
local electric field, using focused ion beam milled defects in thin single crystal lamellae of KTiOPO4 (KTP). The electric field distribution (top) displays localized field hot-spots, which correlate with nucleation events (bottom). Designed local field variations can also dictate subsequent domain wall mobility, demonstrating a new paradigm in ferroelectric domain wall control.

Sequential injection of domain walls into ferroelectrics at different bias voltages: Paving the way for “domain wall memristors”

Simple meso-scale capacitor structures have been made by incorporating thin (300 nm) single crystal lamellae of KTiOPO4 (KTP) between two coplanar Pt electrodes. The influence that either patterned protrusions in the electrodes or focused ion beam milled holes in the KTP have on the nucleation of reverse domains during switching was mapped using piezoresponse force microscopy imaging. The objective was to assess whether or not variations in the magnitude of field enhancement at localised “hot-spots,” caused by such patterning, could be used to both control the exact locations and bias voltages at which nucleation events occurred. It was found that both the patterning of electrodes and the milling of various hole geometries into the KTP could allow controlled sequential injection of domain wall pairs at different bias voltages; this capability could have implications for the design and operation of domain wall electronic devices, such as memristors, in the future.

Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies

Morphological changes in the retinal vascular network are associated with future risk of many systemic and vascular diseases. However, uncertainty over the presence and nature of some of these associations exists. Analysis of data from large population based studies will help to resolve these uncertainties. The QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) retinal image analysis system allows automated processing of large numbers of retinal images. However, an image quality assessment module is needed to achieve full automation. In this paper, we propose such an algorithm, which uses the segmented vessel map to determine the suitability of retinal images for use in the creation of vessel morphometric data suitable for epidemiological studies. This includes an effective 3-dimensional feature set and support vector machine classification. A random subset of 800 retinal images from UK Biobank (a large prospective study of 500,000 middle aged adults; where 68,151 underwent retinal imaging) was used to examine the performance of the image quality algorithm. The algorithm achieved a sensitivity of 95.33% and a specificity of 91.13% for the detection of inadequate images. The strong performance of this image quality algorithm will make rapid automated analysis of vascular morphometry feasible on the entire UK Biobank dataset (and other large retinal datasets), with minimal operator involvement, and at low cost.