From materials to systems: a multiscale analysis of nanomagnetic switching

Abstract

With the increasing demand for low-power electronics, nanomagnetic devices have emerged as strong potential candidates to complement present day transistor technology. A variety of novel switching effects such as spin torque and giant spin Hall offer scalable ways to manipulate nano-sized magnets. However, the low intrinsic energy cost of switching spins is often compromised by the energy consumed in the overhead circuitry in creating the necessary switching fields. Scaling brings in added concerns such as the ability to distinguish states (readability) and to write information without spontaneous backflips (reliability). A viable device must ultimately navigate a complex multi-dimensional material and design space defined by volume, energy budget, speed and a target read-write-retention error. In this paper, we review the major challenges facing nanomagnetic devices and present a multi-scale computational framework to explore possible innovations at different levels (material, device, or circuit), along with a holistic understanding of their overall energydelay-reliability tradeoff.

Spin and Charge Pumping by a Steady or Pulse-Current-Driven Magnetic Domain Wall: A Self-Consistent Multiscale Time-Dependent Quantum-Classical Hybrid Approach

“…The TD-NEGF+LLG framework requires just time-dependent quantum and classical Hamiltonians, together with device geometry, as an input for computing the time evolution of the interacting electron-localizedmagnetic-moments many-body system in numerically exact fashion. This can be contrasted with widely used classical micromagnetic simulations [2,3,7,8,20,21,[35][36][37][38][39][40][41][42][43]71], where propagating conduction electrons appear only indirectly through phenomenological spin torque terms inserted by hand into the LLG equation; or with previous steady-state-NEGF+LLG attempts [48][49][50][51][52] to couple quantum electrons to classical localized magnetic moments where fast electrons are assumed to instantaneously respond to slow dynamics of localized magnetic moments so that noncommutativity of the electronic quantum Hamiltonian at different times is neglected. Using DW motion driven by steady or pulse injected charge current as an example, we essentially demonstrate introduction (via TD-NEGF) of quantum spin pumping by the dynamics of localized magnetic moments and additional time-retarded damping characterized by a memory kernel [63,64] into classical micromagnetics.…”

“…A handful of studies [48][49][50][51][52] have also attempted to develop a multiscale combination of computational quantum (or even simpler semiclassical [53][54][55][56]) transport of conduction electrons with discretized LLG equation for the motion of localized magnetic moments described by the classical vectors M i (t). However, these attempts employ steady-state nonequilibrium density matrix, strictly applicable only to systems which do not evolve in time, FIG.…”

Machine learning nonequilibrium electron forces for adiabatic spin dynamics

“…where the lesser self-energy Σ < is related to the Σ r through the dissipation-fluctuation theorem. The electronic force on the spins, which is obtained from the generalized Hellmann-Feynman theorem, can then be computed from the lesser Green's function [64][65][66][67][68][69]]…”

Analog Signal Processing Using Stochastic Magnets

“…MTJs lie at the heart of the embedded Spin Transfer Torque based Magnetic Random Access Memory (STT-MRAM), a rapidly emerging non-volatile memory technology, and is built in semiconductor fabrication facilities available today [1]. Therefore, this unit is based on a successfully commercialized hardware platform.…”