Microwave detection has a huge number of applications in physics and engineering. It has already been shown that biased spin torque diodes have performance overcoming the CMOS counterpart in terms of sensitivity. In this regard, the spin torque diodes are promising candidates for the next generation of microwave detectors. Here, we show that the optimization of the rectification process based on the injection locking mechanism gives an ultrahigh sensitivity exceeding 200 kV/W with an output resistance below 1 kΩ while maintaining the advantages over other mechanisms such as vortex expulsion or non-linear resonance, to work without a bias magnetic field.

Spin-torque diodes (STDs) offer the possibility of using spin torque to generate rectification voltage with promising applications in microwave detecting, energy harvesting, and neuromorphic computing. Here, we demonstrate a highly sensitive STD with ultralow current density based on a magnetic tunnel junction with perpendicular magnetic anisotropy. At zero magnetic field, a high sensitivity exceeding 3785 V/W is obtained with a low current of −20 μA, corresponding to a current density of ∼105 A/cm2, which is one order lower than the previously reported. When a weak external magnetic field is applied, the sensitivity can be further increased by five times to 20 000 V/W. Furthermore, we construct an artificial neural network with STD neurons to perform recognition of handwritten digits in the Mixed National Institute of Standards and Technology database, where a produced accuracy of up to 94.92% is obtained. Our work provides a route to develop low-power consumption high-sensitivity STDs for Internet of Things applications and neuromorphic computing.

We investigate the current induced magnetization switching properties in CoFeB/MgO/CoFeB magnetic tunnel junctions (MTJs) with the MgO cap layer. It is found that the spin-transfer-torque induced switching current density is inversely proportional to the thickness of the MgO cap layer. We attribute the origin of this behavior to the change in the effective demagnetizing field and damping factor in the free layer, which is verified by spin-torque ferromagnetic resonance measurements. Our experimental results suggest that the utilization of the MgO-cap layer in the MTJs may be useful for spintronic device designs, such as spin-transfer torque magnetic random access memories and spin torque oscillators.

We investigate the highly sensitive spin torque diode (STD) effect in a magnetic tunnel junction (MTJ) with an in-plane polarizer and an in-plane free layer. Under injection locking mechanisms, a high rectification voltage of 12 mV is obtained with an input radio frequency power of 1 μW under direct current bias current and a weak magnetic field, corresponding to a high sensitivity of 12 000 mV/mW. In addition, we use the nonlinear rectification characteristics of STD to mimic a neuron with a ReLU-like activation function to perform the recognition of handwritten digits in the Mixed National Institute of Standards and Technology database, where a produced accuracy of up to 93.53% is obtained. These findings suggest that the MTJ with in-plane magnetized electrodes holds promising potential in developing high sensitivity STDs for Internet of Things applications and neuromorphic computing.

Bio-inspired neuromorphic computing has aroused great interest due to its potential to realize on-chip learning with bio-plausibility and energy efficiency. Realizing spike-timing-dependent plasticity (STDP) in synaptic electronics is critical toward bio-inspired neuromorphic computing systems. Here, we report on stochastic artificial synapses based on nanoscale magnetic tunnel junctions that can implement STDP harnessing stochastic magnetization switching. We further demonstrate that both the magnitude and the temporal requirements for STDP can be modulated via engineering the pre- and post-synaptic voltage pulses. Moreover, based on arrays of binary magnetic synapses, unsupervised learning can be realized for neuromorphic computing tasks such as pattern recognition with great computing accuracy and efficiency. Our study suggests a potential route toward on-chip neuromorphic computing systems.

Flexible electronics or hybrid electronics exhibit great potential for widespread applications in future wearable electronics. In this work, we fabricated flexible nanoscale MgO-barrier magnetic tunnel junctions (MTJs) using a transfer printing process. The magnetic transport measurements reveal that the fabricated devices possess excellent performance with a tunnel magnetoresistance ratio of ∼130% under different strained conditions. In addition, we also studied the spin-torque diode effect under different strained conditions and found that the resonant frequency and rectified voltage remain almost unchanged. These results demonstrate that the nanoscale MTJs have good strain endurance, which provides the feasibility to flexible spintronic storage and microwave applications.

Neuroscience studies have shown that population coding in biological systems can carry out resilient information processing with ensemble of neurons. Such strategy is valuable for the future development of electronics, particularly as the downscaling of transistors is reaching atomic limits and causing problems of large device-to-device variability and even device failure. In this work, we propose that nanoscale spin-torque diode (STD) based on a magnetic tunnel junction can be used to implement population coding. We also demonstrate that a basis set obtained from a single STD by time multiplexing can realize the generation of cursive letters. Furthermore, different activation functions of an artificial neural network have been acquired. In addition, high recognition rates of the Mix National Institute of Standards and Technology handwritten digits up to 94.88% are achieved using an output function constructed from the experimental data. Our work may provide inspiration for designing neuromorphic computing systems.