We demonstrate strain-induced modulation of perpendicular magnetic anisotropy (PMA) in (001)-oriented [Pb(Mg1/3Nb2/3)O3](1−x)-[PbTiO3]x (PMN-PT) substrate/Ta/CoFeB/MgO/Ta structures using ferromagnetic resonance (FMR). An in-plane biaxial strain is produced by applying voltage between the two surfaces of the PMN-PT substrate, and is transferred to the ferromagnetic CoFeB layer, which results in tuning of the PMA of the CoFeB layer. The strain-induced change in PMA is quantitatively extracted from the experimental FMR spectra. It is shown that both first and second-order anisotropy terms are affected by the electric field, and that they have opposite voltage dependencies. A very large value of the voltage-induced perpendicular magnetic anisotropy modulation of ∼7000 fJ/V·m is obtained through this strain-mediated coupling. Using this FMR technique, the magnetostriction coefficient λ is extracted for the ultrathin 1.1 nm Co20Fe60B20 layer, and is found to be 3.7 × 10−5, which is approximately 4 times larger than the previously reported values for CoFeB films thicker than 5 nm. In addition, the effect of strain on the effective damping constant (αeff) is also studied and no obvious modulation of the αeff is observed. The results are relevant to the development of CoFeB-MgO magnetic tunnel junctions for memory applications.
Erratum: "Strain-induced modulation of perpendicular magnetic anisotropy in Ta/CoFeB/MgO structures investigated by ferromagnetic resonance" [Appl. Phys. Lett. 106, 072402 (2015)]
This paper constructs dynamical models and estimation algorithms for the concentration of target molecules in a fluid flow using an array of novel biosensors. Each biosensor is constructed out of protein molecules embedded in a synthetic cell membrane. The concentration evolves according to an advection-diffusion partial differential equation which is coupled with chemical reaction equations on the biosensor surface. By using averaging theory methods and the divergence theorem, an approximate model is constructed that describes the asymptotic behaviour of the concentration as a system of ordinary differential equations. The estimate of target molecules is then obtained by solving a nonlinear least squares problem. It is shown that the estimator is strongly consistent and asymptotically normal. An explicit expression is obtained for the asymptotic variance of the estimation error. As an example, the results are illustrated for a novel biosensor built out of protein molecules.
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