Size and dimensionality effects in superconducting Mo thin films

Molybdenum is a low Tc, type I superconductor whose fundamental properties are poorly known. Its importance as an essential constituent of new high performance radiation detectors, the so-called transition edge sensors (TESs) calls for better characterization of this superconductor, especially in thin film form. Here we report on a study of the basic superconducting features of Mo thin films as a function of their thickness. The resistivity is found to rise and the critical temperature decreases on decreasing film thickness, as expected. More relevant, the critical fields along and perpendicular to the film plane are markedly different, thickness dependent and much larger than the thermodynamic critical field of Mo bulk. These results are consistent with a picture of type II 2D superconducting films, and allow estimates of the fundamental superconducting lengths of Mo. The role of morphology in determining the 2D and type II character of the otherwise type I molybdenum is discussed. The possible consequences of this behaviour on the performance of radiation detectors are also addressed

Characterization of a Mo/Au thermometer for ATHENA

The first dark characterization of a thermometer fabricated with our Mo/Au bilayers to be used as a transition edge sensor is presented. High-quality, stress-free Mo layers, whose thickness is used to tune the critical temperature (TC ) down to 100 mK, are deposited by sputtering at room temperature (RT ) on Si3N4 bulk and membranes, and protected from degradation with a 15-nm sputtered Au layer. An extra layer of high-quality Au is deposited by ex situ e-beam to ensure low residual resistance. The thermometer is patterned on a membrane using standard photolithographic techniques and wet etching processes, and is contacted through Mo paths, displaying a sharp superconducting transition (α ≈ 600). Results show a good coupling between Mo and Au layers and excellent TC reproducibility, allowing to accurately correlate dM o and TC . Since dAu is bigger than ξM for all analyzed samples, bilayer residual resistance can be modified without affecting TC . Finally, first current to voltage measurements at different temperatures are measured and analyzed, obtaining the corresponding characterization parameters.

Fabrication process of Mo/Au TES at RT

We report on the fabrication details of TES based on Mo/Au bilayers. The Mo layer is deposited by radio frequency (RF) sputtering and capped with a sputter deposited thin Au protection layer. Afterwards, a second Au layer of suitable (lower) resistivity is deposited ex‐situ by e‐beam evaporation, until completion of the total desired Au thickness. The deposition was performed at room temperature (RT) on LPCVD Si3 N4 membranes. Such a deposition procedure is very reproducible and allow controlling the critical temperature (Tc) and normal electrical resistance (RN ) of the Mo/Au bilayer. The process is optimized to achieve low stress bilayers, thus avoiding the undesirable curvature of the membranes. Bilayers are patterned using photolithographic techniques and wet etching procedures. Mo superconducting paths are used to contact the Mo/Au bilayers, thus ensuring good electrical conductivity and thermal isolation. The entire fabrication process let to stable and reproducible sensors with required and tunable functional properties

Laminar-turbulent transition induced by a discrete roughness element in a supersonic boundary layer

The linear instability and breakdown to turbulence induced by an isolated roughness element in a boundary layer at Mach 2:5, over an isothermal flat plate with laminar adiabatic wall temperature, have been analysed by means of direct numerical simulations, aided by spatial BiGlobal and three-dimensional parabolized (PSE-3D) stability analyses. It is important to understand transition in this flow regime since the process can be slower than in incompressible flow and is crucial to prediction of local heat loads on next-generation flight vehicles. The results show that the roughness element, with a height of the order of the boundary layer displacement thickness, generates a highly unstable wake, which is composed of a low-velocity streak surrounded by a three-dimensional high-shear layer and is able to sustain the rapid growth of a number of instability modes. The most unstable of these modes are associated with varicose or sinuous deformations of the low-velocity streak; they are a consequence of the instability developing in the three-dimensional shear layer as a whole (the varicose mode) or in the lateral shear layers (the sinuous mode). The most unstable wake mode is of the varicose type and grows on average 17% faster tan the most unstable sinuous mode and 30 times faster than the most unstable boundary layer mode occurring in the absence of a roughness element. Due to the high growthrates registered in the presence of the roughness element, an amplification factor of N D 9 is reached within 50 roughness heights from the roughness trailing edge. The independently performed Navier–Stokes, spatial BiGlobal and PSE-3D stability results are in excellent agreement with each other, validating the use of simplified theories for roughness-induced transition involving wake instabilities. Following the linear stages of the laminar–turbulent transition process, the roll-up of the three-dimensional shear layer leads to the formation of a wedge of turbulence, which spreads laterally at a rate similar to that observed in the case of compressible turbulent spots for the same Mach number.

Self-Learning Embedded System for Object Identification in Intelligent Infrastructure Sensors

The emergence of new horizons in the field of travel assistant management leads to the development of cutting-edge systems focused on improving the existing ones. Moreover, new opportunities are being also presented since systems trend to be more reliable and autonomous. In this paper, a self-learning embedded system for object identification based on adaptive-cooperative dynamic approaches is presented for intelligent sensor’s infrastructures. The proposed system is able to detect and identify moving objects using a dynamic decision tree. Consequently, it combines machine learning algorithms and cooperative strategies in order to make the system more adaptive to changing environments. Therefore, the proposed system may be very useful for many applications like shadow tolls since several types of vehicles may be distinguished, parking optimization systems, improved traffic conditions systems, etc.