Giant spin-torque diode sensitivity in the absence of bias magnetic field.

Microwave detectors based on the spin-torque diode effect are among the key emerging spintronic devices. By utilizing the spin of electrons in addition to charge, they have the potential to overcome the theoretical performance limits of their semiconductor (Schottky) counterparts. However, so far, practical implementations of spin-diode microwave detectors have been limited by the necessity to apply a magnetic field. Here, we demonstrate nanoscale magnetic tunnel junction microwave detectors, exhibiting high-detection sensitivity of 75,400 mV mW(-1) at room temperature without any external bias fields, and for low-input power (micro-Watts or lower). This sensitivity is significantly larger than both state-of-the-art Schottky diode detectors and existing spintronic diodes. Micromagnetic simulations and measurements reveal the essential role of injection locking to achieve this sensitivity performance. This mechanism may provide a pathway to enable further performance improvement of spin-torque diode microwave detectors.

Ultralow-current-density and bias-field-free spin-transfer nano-oscillator.

The spin-transfer nano-oscillator (STNO) offers the possibility of using the transfer of spin angular momentum via spin-polarized currents to generate microwave signals. However, at present STNO microwave emission mainly relies on both large drive currents and external magnetic fields. These issues hinder the implementation of STNOs for practical applications in terms of power dissipation and size. Here, we report microwave measurements on STNOs built with MgO-based magnetic tunnel junctions having a planar polarizer and a perpendicular free layer, where microwave emission with large output power, excited at ultralow current densities, and in the absence of any bias magnetic fields is observed. The measured critical current density is over one order of magnitude smaller than previously reported. These results suggest the possibility of improved integration of STNOs with complementary metal-oxide-semiconductor technology, and could represent a new route for the development of the next-generation of on-chip oscillators.

A new approach for investigating intracranial pressure signal: filtering and morphological features extraction from continuous recording.

Nowadays, the Intracranial Pressure (ICP) monitoring has become the most common method of investigation for both traumatic and chronic neural pathologies. ICP signals are typically triphasic, that is, in a single waveform, three subpeaks can be identified. This work outlines a new algorithm to identify subpeaks from the ICP recordings and to extract a number of 20 meaningful parameter trends. The validity of the implemented method has been proved through a comparison between the automatic subpeaks identification by the algorithm and the manually marked subpeaks by a neurosurgeon. The automatic marking system has identified subpeaks for the 63.74% (mean value) of pulse waves, providing the position and amplitude of each identified subpeak within a tolerance of ±7 samples. This automatic system provides a feature set to be used by classification software to obtain more precise and easier diagnosis in all those cases that involve brain damages or diseases.