School of Engineering Department of Electronic and Computer Engineering 146 Spintronic Dynamics for Reservoir Computing Supervisor: SHAO Qiming / ECE Student: LIU Yifan / ELEC Course: UROP 4100, Spring Recently, chirality of magnons, representing the handedness of spin precession, has been identified as an independent degree of freedom for information transmission and processing in computing, offering new possibilities for low-power spintronics and magnonics development. In this report, we proposed a magnetic heterostructure with free-tunable magnon chirality protected by a topological surface state. The simulation result shows all four different combinations of chirality in the two-layer magnetic hetero structure, consistent with theoretical prediction. They are possessed by the magnonic Chern bands in non-equilibrium state held by spin-orbit torque (SOT), lowering the frequency to few GHz range. Furthermore, we observe that the topological order is also related to the chirality of magnon and proposed growing magnon cavity to directly detect the states. Our work demonstrates a viable magnonic platform that harnesses magnon chirality, setting the stage for the development of chirality-based spintronics. Spintronic Dynamics for Reservoir Computing Supervisor: SHAO Qiming / ECE Student: PENG, Junjie / PHYS-IRE Course: UROP 1000, Summer In this article, I introduce the foundation of MuMax3, including the dynamical terms and the solving method. Furthermore, I have translated successfully some snippets of the MuMax3 code into Python modules to make more accessible and usable the original MuMax3 code for setting up simulations. I have also converted the MuMax3 code into Python modules for easy preparation and running of simulations; it became userfriendly and adapted to a broader domain of uses. The Python modules developed as part of the project provide a smooth interface to the advanced features of Python for simulation to users who are familiar with MuMax3. Spintronic Dynamics for Reservoir Computing Supervisor: SHAO Qiming / ECE Student: YU Qingrong / PHYS-IRE Course: UROP 1100, Summer Chaotic dynamics are common in real-world systems and have wide applications in engineering. However, predicting and designing them can be challenging due to their high sensibility to initial conditions. In this report, we investigate the performance of a single-domain spin-torque oscillator (a magnetic tunnel junction) on predicting its own chaotic dynamics under the scheme of physical reservoir computing.
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