No abstract

Experimental demonstration of resistive neural networks has been the recent focus of hardware implementation of neuromorphic computing. Capacitive neural networks, which call for novel building blocks, provide an alternative physical embodiment of neural networks featuring a lower static power and a better emulation of neural functionalities. Here, we develop neuro-transistors by integrating dynamic pseudo-memcapacitors as the gates of transistors to produce electronic analogs of the soma and axon of a neuron, with “leaky integrate-and-fire” dynamics augmented by a signal gain on the output. Paired with non-volatile pseudo-memcapacitive synapses, a Hebbian-like learning mechanism is implemented in a capacitive switching network, leading to the observed associative learning. A prototypical fully integrated capacitive neural network is built and used to classify inputs of signals.

M agnetic   skyrmions are particle-like spin textures that have been observed in chiral bulk magnets 1-4 and asymmetric magnetic multilayers 5-14. Electrical currents and current-induced spin-orbit torques (SOTs) can be used to manipulate skyrmions in various metallic systems 2,7,8,10,14 , and such capabilities could be useful in the development of energy-efficient spintronic devices. Thermal effects can also be used to generate and manipulate skyrmions 15,16 , which could lead to the development of unconventional computing 17 and energy-harvesting 18 applications. These thermal effects are, however, difficult to observe in bulk samples and large-area films; therefore, microstructured devices need to be employed. Furthermore, the generation of skyrmions via a pure thermal effect 19-21 has not been experimentally demonstrated so far; moreover, whether the skyrmion motion driven by thermal gradients follows the direction of thermal diffusion or, oppositely, the direction of magnonic spin torque 15,20,22,23 remains an open question.   approach allows us to study the dynamics of skyrmions induced by a perpendicular magnetic field (μ 0 H ⊥), electrical current (j e), temperature (T) and temperature gradient (ΔT(x)). The magnetic imaging was conducted at the Fe L 3 edge Q6