We published a paper entitled “Subterahertz collective spin-resonance modes and field-adaptive reservoir computing in the chiral helimagnet Cr_1/3TaS₂” in [Phys. Rev. Applied 24, 054022 (2025)]. Monoaxial chiral helimagnets (CHMs) host rich helical spin textures, including chiral soliton lattices (CSLs) with tunable periods. However, the spin resonance modes of existing CHMs lie in the gigahertz range, limiting their potential for high-speed signal processing. In this work, by combining ferromagnetic resonance, electron spin resonance, and magneto-Raman spectroscopy, we uncovered subterahertz CSL phonon modes in the CHM Cr_1/3TaS₂. Near the critical field, nontrivial CSL phonon modes reach 0.15 THz, while a uniform ferromagnetic resonance mode emerges at 0.375 THz in the forced ferromagnetic phase under 9 T. The CSL phonon frequency in Cr_1/3TaS₂ is five to six times higher than that of isostructural Cr_1/3NbS₂, owing to the larger spin–orbit-coupling-induced Dzyaloshinskii–Moriya interaction. Micromagnetic simulations further resolve the frequency spectrum and the spatial distributions of amplitudes, phases, and precession trajectories of each CSL resonance mode. Moreover, we demonstrate that physical reservoir computing exploiting the nonlinear collective spin dynamics and field-controlled hysteresis of these nontrivial spin textures achieves exceptional performance in time-series prediction tasks. Our findings pave the way for CHM materials toward subterahertz signal processing and neuromorphic computing applications. Congratulations to Zishuang Li, Shuai Zhang (equal contribution), and Co-workers!

We published an invited review entitled “自旋类脑神经形态计算” (Spin-based Brain-like Neuromorphic Computing) in [Journal of Sichuan Normal University (Natural Science) 48(2), 176-191 (2025)]. Brain-inspired neuromorphic computing aims to simulate and implement the information processing and learning capabilities of the biological brain, with one of its key ideas being to mimic the behavior of biological neurons and synapses to achieve information transmission, processing, and storage. Owing to their non-volatility, high speed, low power consumption, nearly infinite endurance, and inherent nonlinearity, spintronic devices have been widely explored and have shown remarkable performance in neuromorphic computing. In this invited review, we systematically introduce and summarize various spintronic effects, including magnetoresistance effects, spin-transfer torque and spin-orbit torque effects, voltage-controlled magnetic anisotropy, and nonlinear magnetization dynamics. Taking the applications of various spintronic devices in reservoir computing, Ising machines, spiking neural networks, and true random number generators as examples, we present an outlook on the prospects and trends of spin-based neuromorphic computing hardware for future artificial-intelligence chips. Congratulations to Shuai Zhang and Co-workers!