Spintronic oscillators have gained important interest in recent years. However, a both fast and accurate model describing their dynamics is currently lacking. Here we propose an unconventional semi-analytical model capable of predicting the fundamental properties of the oscillators, when subjected to a spin-polarized current. Using data-driven corrections, the steady-state and transient regimes of oscillations can be simulated quantitatively. This data-driven model is more than two billion times faster than micromagnetic simulations. Such method paves the way for high-throughput simulation campaigns in any prospective applications, such as neuromorphic spintronics.
Over the past decades, artificial intelligence (AI) has made significant technological advances with the prospect of increased computer capabilities (e.g., automation in decision making and data processing) and acquired an increasingly important role in our everyday technological environment (Dall-E, ChatGPT, etc.). The main issue is that the digital silicon-based computing technologies are very energy-intensive while solving cognitive tasks such as speech or image recognition. We propose to tackle this issue by combining condensed matter physics (spintronics) and artificial intelligence to design nanoscale neuromorphic computing hardware to solve machine learning tasks.
A semi-analytical method (data-driven model) is used to predict the dynamics of a Spin-Torque Vortex Oscillator (STVO). This model relies on an improved analytical model based on the Thiele equation approach and micromagnetic simulations. The improved analytical model shows that the Ampère-Oersted field cannot be neglected and it describes quantitatively the STVO dynamics only in the resonant regime when the data-driven model allows to describe it in the steady-state oscillating regime as well. In addition, the model is 2.1 million time faster than simulations. It can be used to simulate the spin-diode effect and functionalize the STVOs for neuromorphic applications.
We report on microwave oscillations induced by spin-transfer-torque in metallic spin-valves obtained by electrodeposition
of Co-Cu-Co trilayer structures in nanoporous alumina templates. Using micromagnetic calculations
performed on similar spin-valve structures it was possible to identify the magnetization dynamics associated
with the experimentally determined microwave emission. Furthermore it appears that in our particular geometry
the microwave emission is generated by the vortex gyrotropic motion which occurs in, at least, one of the two
magnetic layers of our spin-valve structures. Microwave emission was obtained in the absence of any external
magnetic field with the appropriate magnetization configuration.
Conference Committee Involvement (1)
Spintronics XVII
18 August 2024 | San Diego, California, United States
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