Spintronic Nanodevices for Bioinspired Computing

Abstract

Bioinspired hardware holds the promise of low-energy, intelligent, and highly adaptable computing systems. Applications span from automatic classification for big data management, through unmanned vehicle control, to control for biomedical prosthesis. However, one of the major challenges of fabricating bioinspired hardware is building ultra-high-density networks out of complex processing units interlinked by tunable connections. Nanometer-scale devices exploiting spin electronics (or spintronics) can be a key technology in this context. In particular, magnetic tunnel junctions (MTJs) are well suited for this purpose because of their multiple tunable functionalities. One such functionality, non-volatile memory, can provide massive embedded memory in unconventional circuits, thus escaping the von-Neumann bottleneck arising when memory and processors are located separately. Other features of spintronic devices that could be beneficial for bioinspired computing include tunable fast nonlinear dynamics, controlled stochasticity, and the ability of single devices to change functions in different operating conditions. Large networks of interacting spintronic nanodevices can have their interactions tuned to induce complex dynamics such as synchronization, chaos, soliton diffusion, phase transitions, criticality, and convergence to multiple metastable states. A number of groups have recently proposed bioinspired architectures that include one or several types of spintronic nanodevices. In this paper, we show how spintronics can be used for bioinspired computing. We review the different approaches that have been proposed, the recent advances in this direction, and the challenges toward fully integrated spintronics complementary metal-oxide-semiconductor (CMOS) bioinspired hardware.

Keywords: Bioinspired computing; magnetic tunnel junctions (MTJs); spintronics.

Fig. 1

Principle and multifunctionality of spin torque nanodevices. (Top) A direct current (dc) injected through a magnetic tunnel junction creates a spin torque acting on the magnetization. The resulting magnetization dynamics generate resistance variations which can help mimic important functionalities of synapses and neurons. (Bottom) Different types of responses can be obtained by varying the geometry of the tunnel junction and the bias conditions, such as applied field or current. Here four different responses are shown. Binary memories and memristors are interesting for emulating synapses, while harmonic and stochastic oscillators can mimic some properties of neurons or assemblies of neurons.

Fig. 2

Schematic view of a spin-torque random access memory. To address a particular magnetic tunnel junction, a voltage is applied to the word line, creating a connection via the transistor below between the associated source line and all of the bit lines. A current is passed through the appropriate bit line to the selected source line to either read the state of the magnetic tunnel junction (small current) or set its magnetic state (large current). The transistors are necessary to avoid a large contribution between the selected source line and bit line through more complicated connections, so-called “sneak paths.”

Fig. 3

Neural network. (a) Four layers of neurons (circles) all take inputs, which they nonlinearly process to produce an output signal. The output signal is passed to the next layer of neurons through the synapses (straight lines) weighted by the synaptic weight w_ij. Signals flow from left to right. (b) A simple neuron that takes the input values (x and y values) for different possible inputs and aims to produce an output that is different for triangles and squares. There is no linear function of the inputs that can do this separation, but nonlinear functions (neurons) can. (c) A nonlinear function of x and y produces higher output values for squares, allowing classification and reducing the information sent to the next layer.

Fig. 4

Simulations of learning through probabilistic switching of magnetic tunnel junctions [82]. The synaptic crossbar array (center schematic) consists of magnetic tunnel junctions for which the probability to switch depends on the programming pulse duration and amplitude (right graph). Here, for learning, pulses are chosen so that junctions have only a slight probability to switch. Input pulses code for each pixel amplitude in a video of cars on a highway taken with a bioinspired artificial retina (left image). Output pulses are generated by the output neurons N_i if the input pulses weighted by the junctions’ conductances in each column exceed a threshold. The switching of junctions depending on input and output pulses evolves according to STDP. The junctions’ states are initially random but after the input video has run for some time, the weights stabilize to a configuration such that each output neuron specializes to recognize cars in each lane of the highway (images at the bottom). In other words, the neural network made of stochastic magnetic tunnel junctions has autonomously learned to count cars in each lane.

Fig. 5

Principle of stochastic resonance applied to magnetic tunnel junctions. The dashed curve shows the input signal, which does not reach the thresholds for switching (heavy solid curves labeled +I_c and μI_c). When an appropriate level of noise is added (solid curve) the current does cross the critical currents and the device switches. Even though the noise fluctuates below the critical current, the device stays in the desired state because the current never crosses the threshold for switching in the other direction. The bottom panel gives the resistance of the device due to the switching caused by the noise plus the signal. The resistance closely matches the input signal.

Fig. 6

Magnetic domain wall. The arrows indicate the direction of the magnetization. For typical thin films, the energy is lower when the magnetization is parallel to the side of the structure, so in thin film wires, it tends to lie in the plane along the wire. There are two possible directions for domains. Where they meet is a domain wall, where the magnetization rotates continuously from one direction to the other.

Fig. 7

Principle of the spintronic memristor based on magnetic domain-wall motion. The position x of a domain wall in a magnetic trilayer determines the fraction of parallel and antiparallel domains and sets the resistance of the junction. When a current pulse is injected, the domain wall is expected to move by a quantity Δx proportional to the pulse duration and amplitude, in other words, to the charge. In addition, the direction of the domain-wall motion is set by the sign of injected current. The trilayer resistance depends on the charge that was previously injected, making it a memristor device.

Fig. 8

Neural integration based on magnetic domain-wall motion [113]. A domain wall is initially positioned at the end of a magnetic stripe farther away from the magnetic tunnel junction. After each pulse injected in the magnetic stripe, the domain wall moves toward the junction by a given amount. During the integration phase (a) and (b), the motion of the magnetic domain wall does not modify the junction resistance. When the domain wall passes below the junction, the magnetization configuration of the junction switches from parallel to antiparallel, and its resistance jumps to the high state: this is the firing phase (c). After firing, the configuration has to be reset to (a).

Fig. 9

Different magnetic solitons seen from a top view. Arrows are larger when they are in plane. The background color reflects the local out-of-plane component of magnetization. Domain walls, bubbles, skyrmions, and waves are all solitons in continuous media. On the other hand, the monopole is a point of frustrated interactions between bar magnets in an artificially fabricated lattice, frequently referred to as an artificial spin ice lattice. The magnetization state is one of two configurations found in nanomagnetic logic and all-spin-logic devices.

Fig. 10

Principle of Hopfield networks. Hopfield networks are distinct from networks with synapses that transmit information in one direction in that they have symmetric connections between pairs of neurons. With these symmetric connections, it is possible to define an energy of the system when the state of the system is mapped onto a position. When the system is trained to recognize particular patterns, like the four on the right, the energy of that state is a local minimum. That means that when something close to a four, like the pattern on the left, is presented to the network, it relaxes to the closest local minimum, which is the four on the right.