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Magnetic skyrmions, as scalable and nonvolatile spin textures, can dynamically interact with fields and currents, making them promising for unconventional computing. This paper presents a neuromorphic device based on skyrmion manipulation chambers to implement spike-timing-dependent plasticity (STDP), a mechanism for unsupervised learning in brain-inspired computing. STDP adjusts synaptic weights based on the timing of pre-synaptic and post-synaptic spikes. The proposed three-chamber design encodes synaptic weight in the number of skyrmions in the center chamber, with left and right chambers for pre- and post-synaptic spikes, respectively. Micromagnetic simulations demonstrate that the timing between applied currents across the chambers controls the final skyrmion count (weight). The device exhibits adaptability and learning capabilities by manipulating chamber parameters, mimicking Hebbian and dendritic location-based plasticity. The device's ability to maintain state post-write highlights its potential for advancing adaptable neuromorphic devices. 

Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks. 

Mechanical strain provides a knob for controlling the magnetization of the magnetostrictive-free layer of magnetic tunnel junctions (MTJs), with many applications for energy-efficient memory and computing. This requires integrating materials with high magnetostriction coefficient into MTJs, while still preserving the CoFeB-MgO tunnel barrier for high tunnel magnetoresistance (TMR). One way to accomplish this is to replace the CoFeB free layer of the MTJ with an exchange-coupled bilayer of CoFeB and a highly magnetostrictive ferromagnet like Galfenol (FeGa). Here, FeGa, a thermally stable magnetostrictive material, is integrated into CoFeB-based MTJs. We show that engineering a thin layer of CoFeB and FeGa provides a means of controlling the magnetic properties and switching field in FeGa-based MTJs, and that the exchange-coupled FeGa-CoFeB layer can be used as both a free layer and a fixed layer in the MTJ stack with TMR as high as 100%.