Related papers: Reduced sensitivity to process, voltage and temper…
Temperature dependence of the tunnel magnetoresistance (TMR) was calculated in range of the quantum-ballistic model in the magnetic tunnel junctions (MTJs) with embedded nanoparticles (NPs). The electron tunnel transport through NP was…
There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation.…
The tunnel magnetoresistance (TMR) in the magnetic tunnel junction (MTJ) with embedded nanoparticles (NPs) was calculated in range of the quantum-ballistic model. The simulation was performed for electron tunneling through the insulating…
Understanding quantitatively the heating dynamics in magnetic tunnel junctions (MTJ) submitted to current pulses is very important in the context of spin-transfer-torque magnetic random access memory development. Here we provide a method to…
Low-energy random number generation is critical for many emerging computing schemes proposed to complement or replace von Neumann architectures. However, current random number generators are always associated with an energy cost that is…
Magnetic tunnel junctions (MTJs) play a crucial role in spintronic applications, particularly data storage and sensors. Especially as a non-volatile memory, MTJs have received substantial attention due to its CMOS compatibility, low power…
A new class of spin-transfer torque magnetic random access memory (STT-MRAM) is discussed, in which writing is achieved using thermally initiated magnonic current pulses as an alternative to conventional electric current pulses. The…
We have developed a tunneling theory to describe the temperature dependence of tunneling magnetoresistance (TMR) of the magnetic tunnel junctions (MTJs) with periodic grating barrier. Through the Patterson function approach, the theory can…
Compact and energy-efficient Synapse and Neurons are essential to realize the full potential of neuromorphic computing. In addition, a low variability is indeed needed for neurons in Deep neural networks for higher accuracy. Further,…
Voltage-induced dynamic switching in magnetic tunnel junctions (MTJs) is a writing technique for voltage-controlled magnetoresistive random access memory (VCMRAM), which is expected to be an ultimate non-volatile memory with ultra-low power…
Current-induced spin-transfer torques (STT) and spin-orbit torques (SOT) enable the electrical switching of magnetic tunnel junctions (MTJs) in nonvolatile magnetic random access memories. In order to develop faster memory devices, an…
Graphical probabilistic circuit models of stochastic computing are more powerful than the predominant deep learning models, but also have more demanding requirements. For example, they require "programmable stochasticity", e.g. generating…
Efficient generation of spin currents from charge currents is of high importance for memory and logic applications of spintronics. In particular, generation of spin currents from charge currents in high spin-orbit coupling metals has the…
We investigate switching of magnetic tunnel junctions (MTJs) driven by the thermal effect of the transport current through the junctions. The switching occurs in a specially designed composite free layer, which acts as one of the MTJ…
GaMnAs-based magnetic tunnel junction (MTJ) devices are characterized by in-plane and perpendicular-toplane magnetotransport at low temperatures. Perpendicular-to-plane transport reveals the typical tunneling magnetotransport (TMR) signal.…
Spin filtering and its back-action spin transfer torque (STT) are key ingredients of latest spintronic devices based on magnetic tunnel junctions (MTJs). Resonant tunneling (RT), implemented by design or occurring as parasitic effects, is…
Since it is undesirable to require an external magnetic field for on-chip memory applications, we investigate the use of a Rashba effective field alternatively for assisting the electric-field-induced switching operation of a three terminal…
The balance between low power consumption and high efficiency in memory devices is a major limiting factor in the development of new technologies. Magnetic random access memories (MRAM) based on CoFeB/MgO magnetic tunnel junctions (MTJs)…
Binary stochastic neurons (BSNs) are excellent activators for machine learning. An ideal platform for implementing them are low- or zero-energy-barrier nanomagnets (LBMs) possessing in-plane anisotropy (e.g. circular or slightly elliptical…
Recently there is considerable interest to realize efficient and low-cost true random number generators (RNGs) for practical applications. One important way is through the use of bistable magnetic tunnel junctions (MTJs). Here we study the…