Hardware implementation of Bayesian network building blocks with stochastic spintronic devices.

Bayesian networks are powerful statistical models to understand causal relationships in real-world probabilistic problems such as diagnosis, forecasting, computer vision, etc. For systems that involve complex causal dependencies among many variables, the complexity of the associated Bayesian networks become computationally intractable. As a result, direct hardware implementation of these networks is one promising approach to reducing power consumption and execution time.

Demonstration of a pseudo-magnetization based simultaneous write and read operation in a CoFeB/Pb(MgNb)TiO heterostructure.

Taking advantage of the magnetoelectric and its inverse effect, this article demonstrates strain-mediated magnetoelectric write and read operations simultaneously in CoFeB/Pb(MgNb)TiO heterostructures based on a pseudo-magnetization µ ≡ m- m. By applying an external DC-voltage across a (011)-cut PMN-PT substrate, the ferroelectric polarization is re-oriented, which results in an anisotropic in-plane strain that transfers to the CoFeB thin film and changes its magnetic anisotropy H. The change in H in-turn results in a 90° rotation of the magnetic easy axis for sufficiently high voltages.

Efficient Spin-Orbit Torque Switching of the Semiconducting Van Der Waals Ferromagnet Cr Ge Te.

Being able to electrically manipulate the magnetic properties in recently discovered van der Waals ferromagnets is essential for their integration in future spintronics devices. Here, the magnetization of a semiconducting 2D ferromagnet, i.e.

Spin-torque devices with hard axis initialization as Stochastic Binary Neurons.

Employing the probabilistic nature of unstable nano-magnet switching has recently emerged as a path towards unconventional computational systems such as neuromorphic or Bayesian networks. In this letter, we demonstrate proof-of-concept stochastic binary operation using hard axis initialization of nano-magnets and control of their output state probability (activation function) by means of input currents. Our method provides a natural path towards addition of weighted inputs from various sources, mimicking the integration function of neurons.

Vertical versus Lateral Two-Dimensional Heterostructures: On the Topic of Atomically Abrupt p/n-Junctions.

The key appeal of two-dimensional (2D) materials such as graphene, transition metal dichalcogenides (TMDs), or phosphorene for electronic applications certainly lies in their atomically thin nature that offers opportunities for devices beyond conventional transistors. It is also this property that makes them naturally suited for a type of integration that is not possible with any three-dimensional (3D) material, that is, forming heterostructures by stacking dissimilar 2D materials together. Recently, a number of research groups have reported on the formation of atomically sharp p/n-junctions in various 2D heterostructures that show strong diode-type rectification.