Temperature dependence of the emission linewidth in MgO-based spin torque nano-oscillators

Spin transfer driven excitations in magnetic nanostructures are characterized by a relatively large microwave emission linewidth (10 -100 MHz). Here we investigate the role of thermal fluctuations as well as of the non-linear amplitude-phase coupling parameter and the amplitude relaxation rate to explain the linewidth broadening of in-plane precession modes induced in planar nanostructures. Experiments on the linewidth broadening performed on MgO based magnetic tunnel junctions are compared to the linewidth obtained from macrospin simulations and from evaluation of the phase variance. In all cases we find that the linewidth varies linearly with temperature when the amplitude relaxation rate is of the same order as the linewidth and when the amplitude-phase coupling parameter is relatively small. The small amplitude-phase coupling parameter means that the linewidth is dominated by direct phase fluctuations and not by amplitude fluctuations, explaining thus its linear dependence as a function of temperature

Identification by mass spectrometry of a hemoglobin variant with an elongated beta-globin chain.

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Amplitude and phase noise of magnetic tunnel junction oscillators

International audienceThe microwave emission linewidth of spin transfer torque nano-oscillators is closely related to their phase and amplitude noise that can be extracted from the magnetoresistive voltage signal V(t) using single shot time domain techniques. Here we report on phase and amplitude noise studies for MgO based magnetic tunnel junction oscillators. The analysis of the power spectral densities allows one to separate the linear and nonlinear contributions to the phase noise, the nonlinear contribution being due to the coupling between phase and amplitude. The coupling strength as well as the amplitude relaxation rate can be directly extracted

Neural networks in modeling of CNC milling of moderate slope surfaces

Computer numerical control (CNC) allows achieving a high degree of automation of machine tools by pre-programmed numerical commands. CNC milling process is widely used in industry for machining of complex parts. The need of a description of the CNC milling process is necessary for production of precise parts. This paper introduces artificial neural network based modeling, while the CNC milling of moderate slope shapes is studied. The developed neural models consist of two inputs and two outputs. The created neural models were experimentally tested on the real data. Then, the evaluation and comparison of all models were performed