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).
UR - http://www.scopus.com/inward/record.url?scp=85142932184&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85142932184&partnerID=8YFLogxK
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 -