9 citations · 19 across the 7 of their papers we have counts for
8 papers
Acoustic Tweezers for Magnetic Skyrmions
Chongzhou Wang, Weichao Yu
Current methods for driving magnetic skyrmions predominantly translate ensembles as a whole, lacking single-particle selectivity. Here, we propose an "acoustic tweezer" that determ…
A micromagnetic model with bidirectional magneto-thermal coupling
Peiru Yi, Zian Xia, Weichao Yu
Most conventional micromagnetic frameworks in spin caloritronics rely on a unidirectional coupling approximation, wherein thermal fluctuations drive magnetization dynamics while th…
Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire
Yadi Wang, Bin Chen, Wenping Gao +7
Probabilistic computing has emerged as a viable approach to treat optimization problems. To achieve superior computing performance, the key aspect during computation is massive sam…
A self-learning magnetic Hopfield neural network with intrinsic gradient descent adaption
Chang Niu, Huanyu Zhang, Chuanlong Xu +14
Physical neural networks using physical materials and devices to mimic synapses and neurons offer an energy-efficient way to implement artificial neural networks. Yet, training phy…
Probing disorder-induced time-reversal symmetry breaking in Josephson junctions
Yu Wu, Daiqiang Huang, Huanyu Zhang +13
The relation between superconductivity and time-reversal symmetry (TRS) is one of the most fascinating problems in condensed matter physics. Although most superconductors inherentl…
Magnetostatic effect on spin dynamics properties in antiferromagnetic Van der Waals material CrSBr
Hongyue Xu, Nan Jiang, Haoran Chen +11
Van der Waals (vdW) antiferromagnets are exceptional platforms for exploring the spin dynamics of antiferromagnetic materials owing to their weak interlayer exchange coupling. In t…
Frequency modulation on magnons in synthetic dimensions
Meng Xu, Yan Chen, Weichao Yu
Magnons are promising candidates for next-generation computing architectures, offering the ability to manipulate their amplitude and phase for information encoding. However, the fr…
Physical Neural Networks with Self-Learning Capabilities
Weichao Yu, Hangwen Guo, Jiang Xiao +1
Physical neural networks are artificial neural networks that mimic synapses and neurons using physical systems or materials. These networks harness the distinctive characteristics…