Haoyan Liu and Takashi Ohsawa
General-Purpose STT-MTJ Device Model Based on the Fokker-Planck Equation
IEEE Transactions On Nanotechnology, VOL. 22, 2023 659 A
DOI: 10.1109/TNANO.2023.3322468.
General-Purpose STT-MTJ Device Model Based on the Fokker-Planck Equation
IEEE Transactions On Nanotechnology, VOL. 22, 2023 659 A
DOI: 10.1109/TNANO.2023.3322468.
Abstract: A thermally agitated device model of spin-transfer torque magnetic tunnel junction (STT-MTJ) based on the Fokker-Planck equation is proposed which is implemented into HSPICE by using Verilog-A. We compared different techniques of finite difference method (FDM) and analyzed the impact of the solvers on computational efficiency and accuracy. A framework is proposed which traces dynamics of a particular STT-MTJ’s angle between the magnetic moments of the free and the pinned layers and makes the model applicable to a wide range of circuits. The model was applied to the 4T2MTJ memory cell array and a leaky integrate and-fire (LIF) neuron circuit to validate the stochastic switching characteristic and the angle prediction function. In the memory array simulations, the CPU time consumption for this model is 1/30 of the model which is based on the stochastic Landau-Lifshitz Gilbert-Slonczewski equation.
Fig: (a) Structure of 1T1MTJ synapse. (b) Binary weights in 10 neurons and an input digit ‘9’ of spiking neural network (surrounded by the dotted square) used for the experiment shown. Each digit is a 28×28 matrix. Each figure shows two output spikes fired in the neurons representing ‘0-9’. The total spike numbers of the neurons which represent 0-9 are 2, 3, 4, 3, 4, 4, 4, 4, 4 and 9.
Acknowledgement: This work was supported in part by Synopsys Corporation, in part by JSPS KAKENHI under Grant JP20K04626, in part by VLSI Design and Education Center (VDEC), University of Tokyo with collaboration with Cadence Corporation, and in part by the cooperation of organization between Kioxia Corporation and Waseda University.
