RESUMEN
Based on ferromagnetic thin film systems, spintronic devices show substantial prospects for energy-efficient memory, logic, and unconventional computing paradigms. This paper presents a multilayer ferromagnetic spintronic device's experimental and micromagnetic simulation-based realization for neuromorphic computing applications. The device exhibits a temperature-dependent magnetic field and current-controlled multilevel resistance state switching. To study the scalability of the multilayer spintronic devices for neuromorphic applications, we further simulated the scaled version of the multilayer system read using the magnetic tunnel junction (MTJ) configuration down to 64 nm width. We show the device applications in hardware neural networks using the multiple resistance states as the synaptic weights. A varying pulse amplitude scheme is also proposed to improve the device's weight linearity. The simulated device shows an energy dissipation of 1.23 fJ for a complete potentiation/depression. The neural network based on these devices was trained and tested on the MNIST dataset using a supervised learning algorithm. When integrated as a weight into a 3-layer, fully connected neural network, these devices achieve recognition accuracy above 90% on the MNIST dataset. Thus, the proposed device demonstrates significant potential for neuromorphic computing applications.