EP06.04.04 : Drift-Diffusion Simulation of Coupled Ionic-Electronic Devices

5:00 PM–7:00 PM Apr 3, 2018 (America - Denver)

PCC North, 300 Level, Exhibit Hall C-E

Cem Bonfil1 2 Tom Smy1 Steven McGarry1

1, Carleton University, Ottawa, Ontario, Canada
2, Optiwave, Ottawa, Ontario, Canada

Conducting polymers have been investigated for use as ionically-controlled memory and decision-making neuromorphic devices [1,2]. Unfortunately, there has been little activity on the direct simulation of the operation of such devices that is necessary for the design of more complex circuits and systems. This work creates a numerical simulation scheme for a coupled ionic-electronic structure and uses it to simulate a memristor-like device based on the organic conductor poly(3,4 ethylenedioxythiophene):polystyrenesulfonate (PEDOT:PSS). The modeled device consists of a thin PEDOT:PSS strip with a metal contact on both sides covered with an electrolyte solution containing lithium and perchlorate ions. The conductivity of the device changes when the lithium ions in the electrolyte dedope the PEDOT:PSS by bonding with PSS polymers. A numerical drift diffusion and a Poisson solver was implemented with special features to model the physical properties of the memristor device. The simulator uses a novel method for modelling the ion movement where the saturation in the PEDOT:PSS was implemented by making the drift velocity and the diffusivity an inverse function of local ion concentration using a sigmoid function. The only scaling factors are used to adjust for the material drift and diffusion coefficients and the relative carrier density. The developed simulation algorithm was tested using analytical solutions to the drift diffusion equations and Poisson’s equation. 1-D and 2-D simulations were able to capture the essential physical effects. The comparison of 2-D simulations and the experimental results showed that proposed model worked as expected and produced results that were consistent with the operation of an actual fabricated device. This work can be used to further predict the operation of devices with varying geometries and more complex multi-device circuits and systems. The software is very flexible and additional effects can be added through enhanced modelling of the charge transport mechanisms.

[1] V. Erokhin, “Organic memristors : Basic principles”, Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 5–8 (2010).
[2] E. Barrera Ramirez, S. McGarry, “Geometric dependence in the electric response of electro-ionic polymer devices”, Organic Electronics, Vol. 15, Iss. 6, pp. 1131-1137 (2014).