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. In this article, we will show that truly vertical heterostructures do exhibit much smaller rectification ratios and that the reported results on atomically sharp p/n-junctions can be readily understood within the framework of the gate and drain voltage response of Schottky barriers that are involved in the lateral transport.

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., Cr2Ge2Te6, is studied using the anomalous Hall effect in Cr2Ge2Te6/tantalum heterostructures. The thinner the flakes, hysteresis and remanence in the magnetization loop with out‐of‐plane magnetic fields become more prominent. In order to manipulate the magnetization in such thin flakes, a combination of an in‐plane magnetic field and a charge current flowing through Ta—a heavy metal exhibiting giant spin Hall effect—is used. In the presence of in‐plane fields of 20 mT, charge current densities as low as 5 × 105 A cm–2 are sufficient to switch the out‐of‐plane magnetization of Cr2Ge2Te6. This finding highlights that current densities required for spin‐orbit torque switching of Cr2Ge2Te6 are about two orders of magnitude lower than those required for switching nonlayered metallic ferromagnets such as CoFeB. The results presented here show the potential of 2D ferromagnets for low‐power memory and logic applications.

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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. However, the few hardware implementations of Bayesian networks presented in literature rely on deterministic CMOS devices that are not efficient in representing the stochastic variables in a Bayesian network that encode the probability of occurrence of the associated event. This work presents an experimental demonstration of a Bayesian network building block implemented with inherently stochastic spintronic devices based on the natural physics of nanomagnets. These devices are based on nanomagnets with perpendicular magnetic anisotropy, initialized to their hard axes by the spin orbit torque from a heavy metal under-layer utilizing the giant spin Hall effect, enabling stochastic behavior. We construct an electrically interconnected network of two stochastic devices and manipulate the correlations between their states by changing connection weights and biases. By mapping given conditional probability tables to the circuit hardware, we demonstrate that any two node Bayesian networks can be implemented by our stochastic network. We then present the stochastic simulation of an example case of a four node Bayesian network using our proposed device, with parameters taken from the experiment. We view this work as a first step towards the large scale hardware implementation of Bayesian networks.

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. In our experiment, spin orbit torque (SOT) is employed to “drive” nano-magnets with perpendicular magnetic anisotropy (PMA) -to their metastable state, i.e. in-plane hard axis. Next, the probability of relaxing into one magnetization state (+mi) or the other (−mi) is controlled using an Oersted field generated by an electrically isolated current loop, which acts as a “charge” input to the device. The final state of the magnet is read out by the anomalous Hall effect (AHE), demonstrating that the magnetization can be probabilistically manipulated and output through charge currents, closing the loop from charge-to-spin and spin-to-charge conversion. Based on these building blocks, a two-node directed network is successfully demonstrated where the status of the second node is determined by the probabilistic output of the previous node and a weighted connection between them. We have also studied the effects of various magnetic properties, such as magnet size and anisotropic field on the stochastic operation of individual devices through Monte Carlo simulations of Landau Lifshitz Gilbert (LLG) equation. The three-terminal stochastic devices demonstrated here are a critical step towards building energy efficient spin based neural networks and show the potential for a new application space.