darfix - data analysis for dark-field X-ray microscopy.

A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and visualization of these data, providing the user with the essential tools to extract information from the acquired images in a fast and intuitive manner. These data processing and visualization tools can be either imported as library components or accessed through a graphical user interface as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited. The software can automatically extract the relevant instrument angle settings from the input files' metadata. The currently available input file format is EDF and in future releases HDF5 will be incorporated.

Fourier ptychographic dark field x-ray microscopy.

Dark-field x-ray microscopy (DFXM) is an x-ray imaging technique for mapping three-dimensional (3D) lattice strain and rotation in bulk crystalline materials. At present, these maps of local structural distortions are derived from the raw intensity images using an incoherent analysis framework. In this work, we describe a coherent, Fourier ptychographic approach that requires little change in terms of instrumentation and acquisition strategy, and may be implemented on existing DFXM instruments. We demonstrate the method experimentally and are able to achieve quantitative phase reconstructions of thin film samples and maps of the aberrations in the objective lens. The method holds particular promise for the characterization of crystalline materials containing weak structural contrast.

Integration of LaMnO3+δ films on platinized silicon substrates for resistive switching applications by PI-MOCVD.

The next generation of electronic devices requires faster operation velocity, higher storage capacity and reduction of the power consumption. In this context, resistive switching memory chips emerge as promising candidates for developing new non-volatile memory modules. Manganites have received increasing interest as memristive material as they exhibit a remarkable switching response. Nevertheless, their integration in CMOS-compatible substrates, such as silicon wafers, requires further effort. Here the integration of LaMnO3+δ as memristive material in a metal-insulator-metal structure is presented using a silicon-based substrate and the pulsed injection metal organic chemical vapour deposition technique. We have developed three different growth strategies with which we are able to tune the oxygen content and Mn oxidation state moving from an orthorhombic to a rhombohedral structure for the active LaMnO3+δ material. Furthermore, a good resistive switching response has been obtained for LaMnO3+δ-based devices fabricated using optimized growth strategies.