Abstract-Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Devicelevel simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by ∼ 100× in comparison to a corresponding digital/ analog CMOS neuron implementation.

We demonstrate a flexible, transparent, and conductive composite electrode comprising silver nanowires (Ag NWs), and indium-doped zinc oxide (IZO) layers. IZO is sputtered onto an Ag NW layer, with the unique structural features of the resulting composite suitable as a flexible, transparent, conductive electrode. The IZO buffer layer prohibits surface oxidation of the Ag NW, and is thereby effective in preventing undesirable changes in electrical properties. The newly designed composite electrode is a promising alternative to conventional ITO films for the production of flexible and transparent electrodes to be applied in next-generation flexible electronic devices.

Probabilistic inference from real-time input data is becoming increasingly popular and may be one of the potential pathways at enabling cognitive intelligence. As a matter of fact, preliminary research has revealed that stochastic functionalities also underlie the spiking behavior of neurons in cortical microcircuits of the human brain. In tune with such observations, neuromorphic and other unconventional computing platforms have recently started adopting the usage of computational units that generate outputs probabilistically, depending on the magnitude of the input stimulus. In this work, we experimentally demonstrate a spintronic device that offers a direct mapping to the functionality of such a controllable stochastic switching element. We show that the probabilistic switching of Ta/CoFeB/MgO heterostructures in presence of spin-orbit torque and thermal noise can be harnessed to enable probabilistic inference in a plethora of unconventional computing scenarios. This work can potentially pave the way for hardware that directly mimics the computational units of Bayesian inference.

Fabrication of junction-free Ag fiber electrodes for flexible organic light-emitting diodes (OLEDs) is demonstrated. The junction-free Ag fiber electrodes are fabricated by electrospun polymer fibers used as an etch mask and wet etching of Ag thin film. This process facilitates surface roughness control, which is important in transparent electrodes based on metal wires to prevent electrical instability of the OLEDs. The transmittance and resistance of Ag fiber electrodes can be independently adjusted by controlling spinning time and Ag deposition thickness. The Ag fiber electrode shows a transmittance of 91.8% (at 550 nm) at a sheet resistance of 22.3 Ω □ , leading to the highest OLED efficiency. In addition, Ag fiber electrodes exhibit excellent mechanical durability, as shown by measuring the change in resistance under repeatable mechanical bending and various bending radii. The OLEDs with Ag fiber electrodes on a flexible substrate are successfully fabricated, and the OLEDs show an enhancement of EQE (≈19%) compared to commercial indium tin oxide electrodes.

Ising spin model is considered as an efficient computing method to solve combinatorial optimization problems based on its natural tendency of convergence towards low energy state. The underlying basic functions facilitating the Ising model can be categorized into two parts, “Annealing and Majority vote.” In this paper, we propose an Ising cell based on Spin Hall Effect (SHE) induced magnetization switching in a Magnetic Tunnel Junction (MTJ). The stochasticity of our proposed Ising cell based on SHE induced MTJ switching can implement the natural annealing process by preventing the system from being stuck in solutions with local minima. Further, by controlling the current through the Heavy-Metal (HM) underlying the MTJ, we can mimic the majority vote function which determines the next state of the individual spins. By solving coupled Landau-Lifshitz-Gilbert equations, we demonstrate that our Ising cell can be replicated to map certain combinatorial problems. We present results for two representative problems—Maximum-cut and Graph coloring—to illustrate the feasibility of the proposed device-circuit configuration in solving combinatorial problems. Our proposed solution using a HM based MTJ device can be exploited to implement compact, fast, and energy efficient Ising spin model.