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The magnetic skyrmion-based spin transfer nano-oscillators (STNO) are the potential candidates for next-generation microwave signal generator and has gained popularity due to their performance, integrability and compatibility with existing CMOS technology. However, these devices suffer from the Joule heating problem that neglects their non-volatility advantage in spintronic devices. Therefore, it is necessary to investigate the alternative driving mechanisms for the development of energy-efficient skyrmion based nano-oscillators. In this paper, a skyrmion-based nano-oscillator has been designed that utilizes thermal power to drive skyrmion on a thermocoupled nanotrack. The thermocoupled nanotrack is designed in such a way that both the upper and lower nanotracks have different values of damping constants and a temperature difference is maintained between the extreme ends, in order to create a temperature gradient in the two nanotracks. By employing this technique, skyrmion is able to exhibit the periodic motion on the nanotrack with the maximum achievable frequency of 2.5GHz without any external stimuli. Moreover, the proposed device offers low thermal energy consumption of 0.84fJ/oscillation. Hence, this work provides the pathway for the development of energy-efficient future spintronic devices.

Multistate memory systems have the ability to store and process more data in the same physical space as binary memory systems, making them a potential alternative to existing binary memory systems. In the past, it has been demonstrated that voltage-controlled magnetic anisotropy (VCMA) based writing is highly energy-efficient compared to other writing methods used in non-volatile nano-magnetic binary memory systems. In this study, we introduce a new, VCMA-based and skyrmion-mediated non-volatile ternary memory system using a perpendicular magnetic tunnel junction (p-MTJ) in the presence of room temperature thermal perturbation. We have also shown that ternary states {-1, 0, +1} can be implemented with three magnetoresistance values obtained from a p-MTJ corresponding to ferromagnetic up, down, and skyrmion state, with 99% switching probability in the presence of room temperature thermal noise in an energy-efficient way, requiring ~3 fJ energy on an average for each switching operation. Additionally, we show that our proposed ternary memory demonstrates an improvement in area and energy by at least 2X and ~60X respectively, compared to state-of-the-art spin-transfer torque (STT)-based non-volatile magnetic multistate memories. Furthermore, these three states can be potentially utilized for energy-efficient, high-density in-memory quantized deep neural network implementation.

In the "Beyond Moore's Law" era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore's Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries., Comment: 80 pages accepted in Nano Futures

The development of energy-efficient neuromorphic hardware using spintronic devices based on antiferromagnetic (AFM) skyrmion motion on nanotracks has gained considerable interest. Owing to its properties such as robustness against external magnetic fields, negligible stray fields, and zero net topological charge, AFM skyrmions follow straight trajectories that prevent their annihilation at nanoscale racetrack edges. This makes the AFM skyrmions a more favorable candidate over the ferromagnetic (FM) skyrmion for future spintronic applications. This work proposes an AFM skyrmion-based neuron device exhibiting the leaky-integrate-fire (LIF) functionality by exploiting thermal gradient or alternatively perpendicular magnetic anisotropy (PMA) gradient in the nanotrack for leaky behavior by moving the skyrmion in the direction to minimize the system energy. Furthermore, it is shown that the AFM skyrmion couples efficiently to the soft ferromagnetic layer of a magnetic tunnel junction enabling efficient read-out of the skyrmion. The maximum change of 9.2% in tunnel magnetoresistance (TMR) is estimated for detecting the AFM skyrmion. Moreover, the proposed neuron device has the energy dissipation of 4.32 fJ per LIF operation thus, paving the path for developing energy-efficient devices in antiferromagnetic spintronics for neuromorphic computing.