Engineering the semiconductor/oxide interaction for stacking twin suppression in single crystalline epitaxial silicon(111)/insulator/Si(111) heterostructures

The integration of alternative semiconductor layers on the Si material platform via oxide heterostructures is of interest to increase the performance and/or functionality of future Si-based integrated circuits. The single crystalline quality of epitaxial (epi) semiconductor-insulator-Si heterostructures is however limited by too high defect densities, mainly due to a lack of knowledge about the fundamental physics of the heteroepitaxy mechanisms at work. To shed light on the physics of stacking twin formation as one of the major defect mechanisms in (111)-oriented fcc-related heterostructures on Si(111), we report a detailed experimental and theoretical study on the structure and defect properties of epi-Si(111)/Y2O 3/Pr2O3/Si(111) heterostructures. Synchrotron radiation-grazing incidence x-ray diffraction (SR-GIXRD) proves that the engineered Y2O3/Pr2O3 buffer dielectric heterostructure on Si(111) allows control of the stacking sequence of the overgrowing single crystalline epi-Si(111) layers. The epitaxy relationship of the epi-Si(111)/insulator/Si(111) heterostructure is characterized by a type A/B/A stacking configuration. Theoretical ab initio calculations show that this stacking sequence control of the heterostructure is mainly achieved by electrostatic interaction effects across the ionic oxide/covalent Si interface (IF). Transmission electron microscopy (TEM) studies detect only a small population of misaligned type B epi-Si(111) stacking twins whose location is limited to the oxide/epiSi IF region. Engineering the oxide/semiconductor IF physics by using tailored oxide systems opens thus a promising approach to grow heterostructures with well-controlled properties. © IOP Publishing Ltd and Deutsche Physikalische Gesellschaft

Calibration of multi-layered probes with low/high magnetic moments

We present a comprehensive method for visualisation and quantification of the magnetic stray field of magnetic force microscopy (MFM) probes, applied to the particular case of custom-made multi-layered probes with controllable high/low magnetic moment states. The probes consist of two decoupled magnetic layers separated by a non-magnetic interlayer, which results in four stable magnetic states: ±ferromagnetic (FM) and ±antiferromagnetic (A-FM). Direct visualisation of the stray field surrounding the probe apex using electron holography convincingly demonstrates a striking difference in the spatial distribution and strength of the magnetic flux in FM and A-FM states. In situ MFM studies of reference samples are used to determine the probe switching fields and spatial resolution. Furthermore, quantitative values of the probe magnetic moments are obtained by determining their real space tip transfer function (RSTTF). We also map the local Hall voltage in graphene Hall nanosensors induced by the probes in different states. The measured transport properties of nanosensors and RSTTF outcomes are introduced as an input in a numerical model of Hall devices to verify the probe magnetic moments. The modelling results fully match the experimental measurements, outlining an all-inclusive method for the calibration of complex magnetic probes with a controllable low/high magnetic moment

An Adaptive Tissue Characterisation Network for Model-Free Visualisation of Dynamic Contrast-Enhanced Magnetic Resonance Image Data

Dynamic contrast-enhanced magnetic resonanceimaging (DCE-MRI) has become an important source of informationto aid cancer diagnosis. Nevertheless, due to the multi-temporalnature of the three-dimensional volume data obtained fromDCE-MRI, evaluation of the image data is a challenging task andtools are required to support the human expert. We investigatean approach for automatic localization and characterization ofsuspicious lesions in DCE-MRI data. It applies an artificial neuralnetwork (ANN) architecture which combines unsupervised andsupervised techniques for voxel-by-voxel classification of temporalkinetic signals. The algorithm is easy to implement, allows forfast training and application even for huge data sets and canbe directly used to augment the display of DCE-MRI data. Todemonstrate that the system provides a reasonable assessment ofkinetic signals, the outcome is compared with the results obtainedfrom the model-based three-time-points (3TP) technique whichrepresents a clinical standard protocol for analysing breast cancerlesions. The evaluation based on the DCE-MRI data of 12 casesindicates that, although the ANN is trained with impreciselylabeled data, the approach leads to an outcome conforming with3TP without presupposing an explicit model of the underlyingphysiological process

An Adaptive Tissue Characterisation Network for Model-Free Visualisation of Dynamic Contrast-Enhanced Magnetic Resonance Image Data

Dynamic contrast-enhanced magnetic resonanceimaging (DCE-MRI) has become an important source of informationto aid cancer diagnosis. Nevertheless, due to the multi-temporalnature of the three-dimensional volume data obtained fromDCE-MRI, evaluation of the image data is a challenging task andtools are required to support the human expert. We investigatean approach for automatic localization and characterization ofsuspicious lesions in DCE-MRI data. It applies an artificial neuralnetwork (ANN) architecture which combines unsupervised andsupervised techniques for voxel-by-voxel classification of temporalkinetic signals. The algorithm is easy to implement, allows forfast training and application even for huge data sets and canbe directly used to augment the display of DCE-MRI data. Todemonstrate that the system provides a reasonable assessment ofkinetic signals, the outcome is compared with the results obtainedfrom the model-based three-time-points (3TP) technique whichrepresents a clinical standard protocol for analysing breast cancerlesions. The evaluation based on the DCE-MRI data of 12 casesindicates that, although the ANN is trained with impreciselylabeled data, the approach leads to an outcome conforming with3TP without presupposing an explicit model of the underlyingphysiological process

An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data

Twellmann T, Lichte O, Nattkemper TW. An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE TRANSACTIONS ON MEDICAL IMAGING. 2005;24(10):1256-1266.Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process

Matrix metalloproteinases (MMP-8,-13, and-14) interact with the clotting system and degrade fibrinogen and factor XII (hagemann factor)

Tschesche H, Lichte A, Hiller O, Oberpichler A, Buttner FH, Bartnik E. Matrix metalloproteinases (MMP-8,-13, and-14) interact with the clotting system and degrade fibrinogen and factor XII (hagemann factor). In: CELLULAR PEPTIDASES IN IMMUNE FUNCTIONS AND DISEASES 2. Vol 477. KLUWER ACADEMIC / PLENUM PUBL; 2000: 217-228