Mechanical-Resonance-Enhanced Thin-Film Magnetoelectric Heterostructures for Magnetometers, Mechanical Antennas, Tunable RF Inductors, and Filters

The strong strain-mediated magnetoelectric (ME) coupling found in thin-film ME heterostructures has attracted an ever-increasing interest and enables realization of a great number of integrated multiferroic devices, such as magnetometers, mechanical antennas, RF tunable inductors and filters. This paper first reviews the thin-film characterization techniques for both piezoelectric and magnetostrictive thin films, which are crucial in determining the strength of the ME coupling. After that, the most recent progress on various integrated multiferroic devices based on thin-film ME heterostructures are presented. In particular, rapid development of thin-film ME magnetometers has been seen over the past few years. These ultra-sensitive magnetometers exhibit extremely low limit of detection (sub-pT/Hz1/2) for low-frequency AC magnetic fields, making them potential candidates for applications of medical diagnostics. Other devices reviewed in this paper include acoustically actuated nanomechanical ME antennas with miniaturized size by 1-2 orders compared to the conventional antenna; integrated RF tunable inductors with a wide operation frequency range; integrated RF tunable bandpass filter with dual H- and E-field tunability. All these integrated multiferroic devices are compact, lightweight, power-efficient, and potentially integrable with current complementary metal oxide semiconductor (CMOS) technology, showing great promise for applications in future biomedical, wireless communication, and reconfigurable electronic systems

Electrical and electronic devices and components: A compilation

Components and techniques which may be useful in the electronics industry are described. Topics discussed include transducer technology, printed-circuit technology, solid state devices, MOS transistors, Gunn device, microwave antennas, and position indicators

Novel high frequency electrical characterization technique for magnetic passive devices

Integrated magnetic components are key elements of the Power Supply on Chip modules. Due to the application requirements, these magnetic devices work at very high frequency and have low inductances. Conventional small-signal tests do not provide all the required information about the magnetic device. Hence, it is important to develop new set-ups to apply large signals to accurately measure the performance of devices under realistic operating conditions, including non-linear core effects. The proposed experimental set-up is suitable to measure the device impedance under different large-signal test conditions, similar to those in the actual converter, since the excitation current can be configured through every winding: ac current up to 0.5 A at frequencies up to 120 MHz and dc bias current up to 2 A through one or both windings. Voltage and current are measured using commercial instrumentation. Due to the characteristics of the probes and the high frequency of the test, the attenuation and delay due to the probes and the experimental set-up have to be taken into account when processing the voltage and current waveforms to calculate the impedances. The compensation test to calculate this attenuation and delay is described. Finally, the proposed set-up is validated by measuring a two-phase coupled inductors micro-fabricated on silicon

A ferrofluid based neural network: design of an analogue associative memory

We analyse an associative memory based on a ferrofluid, consisting of a system of magnetic nano-particles suspended in a carrier fluid of variable viscosity subject to patterns of magnetic fields from an array of input and output magnetic pads. The association relies on forming patterns in the ferrofluid during a trainingdphase, in which the magnetic dipoles are free to move and rotate to minimize the total energy of the system. Once equilibrated in energy for a given input-output magnetic field pattern-pair the particles are fully or partially immobilized by cooling the carrier liquid. Thus produced particle distributions control the memory states, which are read out magnetically using spin-valve sensors incorporated in the output pads. The actual memory consists of spin distributions that is dynamic in nature, realized only in response to the input patterns that the system has been trained for. Two training algorithms for storing multiple patterns are investigated. Using Monte Carlo simulations of the physical system we demonstrate that the device is capable of storing and recalling two sets of images, each with an accuracy approaching 100%.Comment: submitted to Neural Network