TY - GEN

T1 - Spin-Transfer Torque Magnetic Tunnel Junction Model Based on Fokker-Planck Equation for Stochastic Circuit Simulations

AU - Liu, Haoyan

AU - Ohsawa, Takashi

N1 - Funding Information:
This work was supported by VLSI Design and Education Center (VDEC), the University of Tokyo with collaboration with Cadence Corporation and Synopsys Corporation and by JSPS KAKENHI Grant Number JP20K04626. It was partly executed under the cooperation of organization between Kioxia Corporation and Waseda University.
Publisher Copyright:
© 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - A spin-transfer torque magnetic tunnel junction (STT-MTJ) model is proposed which is based on Fokker-Planck equation and described by Verilog-A for stochastic circuit simulations by SPICE. A new framework to calculate the magnetization angle makes the model usable widely in STT-MT J applications. The model can be applied to general situations in which the electrical current flowing through an MTJ changes its strength and direction in time. The CPU time for simulation of a large-scale circuit is shown to be much shorter than the model which is based on stochastic Landau-Lifshitz-Gilbert-Slonczewsky (s-LLGS) equation with a Langevin field. The model is applied to a leaky integrate-and-fire (LIF) neuron circuit for spiking neural networks (SNNs).

AB - A spin-transfer torque magnetic tunnel junction (STT-MTJ) model is proposed which is based on Fokker-Planck equation and described by Verilog-A for stochastic circuit simulations by SPICE. A new framework to calculate the magnetization angle makes the model usable widely in STT-MT J applications. The model can be applied to general situations in which the electrical current flowing through an MTJ changes its strength and direction in time. The CPU time for simulation of a large-scale circuit is shown to be much shorter than the model which is based on stochastic Landau-Lifshitz-Gilbert-Slonczewsky (s-LLGS) equation with a Langevin field. The model is applied to a leaky integrate-and-fire (LIF) neuron circuit for spiking neural networks (SNNs).

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U2 - 10.1109/NANO54668.2022.9928695

DO - 10.1109/NANO54668.2022.9928695

M3 - Conference contribution

AN - SCOPUS:85142932184

T3 - Proceedings of the IEEE Conference on Nanotechnology

SP - 287

EP - 290

BT - 2022 IEEE 22nd International Conference on Nanotechnology, NANO 2022

PB - IEEE Computer Society

T2 - 22nd IEEE International Conference on Nanotechnology, NANO 2022

Y2 - 4 July 2022 through 8 July 2022

ER -