The development of an efficient neuromorphic computing system requires the use of nanodevices that intrinsically emulate the biological behavior of neurons and synapses. While numerous artificial synapses have been shown to store weights in a manner analogous to biological synapses, the challenge of developing an artificial neuron is impeded by the necessity to include leaking, integrating, firing, and lateral inhibition features. In particular, previous proposals for artificial neurons have required the use of external circuits to perform lateral inhibition, thereby decreasing the efficiency of the resulting neuromorphic computing system. This work therefore proposes a leaky integrate-andfire neuron that intrinsically provides lateral inhibition, without requiring any additional circuitry. The proposed neuron is based on the previously proposed domain-wall magnetic tunnel junction devices, which have been proposed as artificial synapses and experimentally demonstrated for nonvolatile logic. Single-neuron micromagnetic simulations are provided that demonstrate the ability of this neuron to implement the required leaking, integrating, and firing. These simulations are then extended to pairs of adjacent neurons to demonstrate, for the first time, lateral inhibition between neighboring artificial neurons. Finally, this intrinsic lateral inhibition is applied to a ten-neuron crossbar structure and trained to identify handwritten digits and shown via direct large-scale micromagnetic simulation for 100 digits to correctly identify the proper signal for 94% of the digits.

In neuromorphic computing, artificial synapses provide a multi‐weight (MW) conductance state that is set based on inputs from neurons, analogous to the brain. Herein, artificial synapses based on magnetic materials that use a magnetic tunnel junction (MTJ) and a magnetic domain wall (DW) are explored. By fabricating lithographic notches in a DW track underneath a single MTJ, 3–5 stable resistance states that can be repeatably controlled electrically using spin‐orbit torque are achieved. The effect of geometry on the synapse behavior is explored, showing that a trapezoidal device has asymmetric weight updates with high controllability, while a rectangular device has higher stochasticity, but with stable resistance levels. The device data is input into neuromorphic computing simulators to show the usefulness of application‐specific synaptic functions. Implementing an artificial neural network (NN) applied to streamed Fashion‐MNIST data, the trapezoidal magnetic synapse can be used as a metaplastic function for efficient online learning. Implementing a convolutional NN for CIFAR‐100 image recognition, the rectangular magnetic synapse achieves near‐ideal inference accuracy, due to the stability of its resistance levels. This work shows MW magnetic synapses are a feasible technology for neuromorphic computing and provides design guidelines for emerging artificial synapse technologies.

Black phosphorus (BP) is a promising two-dimensional (2D) material for nanoscale transistors, due to its expected higher mobility than other 2D semiconductors. While most studies have reported ambipolar BP with a stronger p-type transport, it is important to fabricate both unipolar p- and n-type transistors for low-power digital circuits. Here, we report unipolar n-type BP transistors with low work function Sc and Er contacts, demonstrating a record high n-type current of 200 μA/μm in 6.5 nm thick BP. Intriguingly, the electrical transport of the as-fabricated, capped devices changes from ambipolar to n-type unipolar behavior after a month at room temperature. Transmission electron microscopy analysis of the contact cross-section reveals an intermixing layer consisting of partly oxidized metal at the interface. This intermixing layer results in a low n-type Schottky barrier between Sc and BP, leading to the unipolar behavior of the BP transistor. This unipolar transport with a suppressed p-type current is favorable for digital logic circuits to ensure a lower off-power consumption.

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

We investigate the valley Hall effect (VHE) in monolayer WSe2 field-effect transistors using optical Kerr rotation measurements at 20 K. While studies of the VHE have so far focused on n-doped MoS2, we observe the VHE in WSe2 in both the n- and p-doping regimes. Hole doping enables access to the large spin-splitting of the valence band of this material. The Kerr rotation measurements probe the spatial distribution of the valley carrier imbalance induced by the VHE. Under current flow, we observe distinct spin-valley polarization along the edges of the transistor channel. From analysis of the magnitude of the Kerr rotation, we infer a spin-valley density of 44 spins/μm, integrated over the edge region in the p-doped regime. Assuming a spin diffusion length less than 0.1 μm, this corresponds to a spin-valley polarization of the holes exceeding 1%.