Optical emission spectroscopy of the plasma during sputter deposition of YBCO films for microwave applications

YBCO thin films are currently used in several HTS-based electronics applications. The performance of devices, which may include microwave passive components (filters, resonators), grain boundary junctions or spintronic multilayer structures, is determined by film quality, which in turn depends on the deposition technology used and growth parameters. We report on results from nonintrusive Optical Emission Spectroscopy of the plasma during YBCO thin film deposition in a high-pressure on-axis sputtering system under different conditions, including small trace gas additions to the sputtering gas. We correlate these results with the compositional and structural changes which affect the DC and microwave properties of YBCO films. Film morphology, composition, structure and in- and out-of-plane orientation were assessed; T, and microwave surface resistance measurements were made using inductive and resonator techniques. Comparison was made with films sputtered in an off-axis 2-opposing magnetron system.

Reconstruction of arbitrary biochemical reaction networks: A compressive sensing approach

Reconstruction of biochemical reaction networks (BRN) and genetic regulatory networks (GRN) in particular is a central topic in systems biology which raises crucial theoretical challenges in system identification. Nonlinear Ordinary Differential Equations (ODEs) that involve polynomial and rational functions are typically used to model biochemical reaction networks. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data quite difficult. In this paper, we present a network reconstruction algorithm that can deal with ODE model descriptions containing polynomial and rational functions. Rather than identifying the parameters of linear or nonlinear ODEs characterised by pre-defined equation structures, our methodology allows us to determine the nonlinear ODEs structure together with their associated parameters. To solve the network reconstruction problem, we cast it as a compressive sensing (CS) problem and use sparse Bayesian learning (SBL) algorithms as a computationally efficient and robust way to obtain its solution. © 2012 IEEE.