In this paper the perpendicular magnetic anisotropy (PMA) is tailored by changing the thickness of the free layer with the objective of producing MTJ nano-pillars with smooth linear resistance dependence with both in-plane magnetic field and DC bias. We furthermore demonstrate how this linear bias dependence can be used to create a zero-threshold broadband voltage rectifier, a feature which is important for rectification in wireless charging and energy harvesting applications. By carefully balancing the amount of PMA acting in the free layer the measured RF to DC voltage conversion efficiency can be made as large as 11%.

Magnetic tunnel junctions are nanoscale spintronic devices with microwave generation and detection capabilities. Here we use the rectification effect called "spin-diode" in a magnetic tunnel junction to wirelessly detect the microwave emission of another junction in the autooscillatory regime. We show that the rectified spin-diode voltage measured at the receiving junction end can be reconstructed from the independently measured auto-oscillation and spin diode spectra in each junction. Finally we adapt the auto-oscillator model to the case of spin-torque oscillator and spin-torque diode and we show that accurately reproduces the experimentally observed features. These results will be useful to design circuits and chips based on spintronic nanodevices communicating through microwaves.

Artificial neural networks are a valuable tool for radio-frequency (RF) signal classification in many applications, but the digitization of analog signals and the use of general purpose hardware non-optimized for training make the process slow and energetically costly. Recent theoretical work has proposed to use nano-devices called magnetic tunnel junctions, which exhibit intrinsic RF dynamics, to implement in hardware the multiply and accumulate (MAC) operation—a key building block of neural networks—directly using analog RF signals. In this article, we experimentally demonstrate that a magnetic tunnel junction can perform a multiplication of RF powers, with tunable positive and negative synaptic weights. Using two magnetic tunnel junctions connected in series, we demonstrate the MAC operation and use it for classification of RF signals. These results open a path to embedded systems capable of analyzing RF signals with neural networks directly after the antenna, at low power cost and high speed.

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