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Zhi Jackie Yao

Publications and source records attributed to Zhi Jackie Yao.

2 recordsLinked to original sources

A Magnon-Based Electric Field Controlled Magnetoelectric Device for Energy-Efficient Logic-in-Memory

We demonstrate a non-volatile magnetoelectric magnonic memory (MEMM) that enables fully electrical write/read via direct magnon-driven sensing in an insulating antiferromagnet. A fabricated SrIrO3/La-BiFeO3/SrIrO3 trilayer exhibits sub-100 ps switching, a remnant polarization of 20 uC/cm2, and a readout voltage contrast close to 1mV between high and low-resistance states. To connect device physics to circuit behavior, we develop and experimentally validate a compact circuit model that captures spin Hall injection and spin transport. Simulations with optimized material parameters predict output voltages > 100mV, enabling cascading without external amplification. Using this framework, we design MEMM-based memory and logic blocks, including a 1T1R array, two inverter implementations (complementary two-device and single-device), and a three-input majority gate, and evaluate deep-pipelined operation. The model projects switching energies down to 1 aJ per operation and logic propagation delays of 30-60 ps, indicating MEMM as a promising platform for energy-constrained, high throughput computing.

cond-mat.mtrl-sci↗

MagneX: A High-Performance, GPU-Enabled, Data-Driven Micromagnetics Solver for Spintronics

In order to comprehensively investigate the multiphysics coupling in spintronic devices, it is essential to parallelize and utilize GPU-acceleration to address the spatial and temporal disparities inherent in the relevant physics. Additionally, the use of cutting-edge time integration libraries as well as machine learning (ML) approaches to replace and potentially accelerate expensive computational routines are attractive capabilities to enhance modeling capabilities moving forward. Leveraging the Exascale Computing Project software framework AMReX, as well as SUNDIALS time-integration libraries and python-based ML workflows, we have developed an open-source micromagnetics modeling tool called MagneX. This tool incorporates various crucial magnetic coupling mechanisms, including Zeeman coupling, demagnetization coupling, crystalline anisotropy interaction, exchange coupling, and Dzyaloshinskii-Moriya interaction (DMI) coupling. We demonstrate the GPU performance and scalability of the code and rigorously validate MagneX's functionality using the mumag standard problems and widely-accepted DMI benchmarks. Furthermore, we demonstrate the data-driven capability of MagneX by replacing the computationally-expensive demagnetization physics with neural network libraries trained from our simulation data. With the capacity to explore complete physical interactions, this innovative approach offers a promising pathway to better understand and develop fully integrated spintronic and electronic systems.

cs.CE↗