Functional Model of a Nanoparticle Organic Memory Transistor for Use as a Spiking Synapse

Abstract

Emerging synapse-like nanoscale devices such as memristive devices and synaptic transistors are of great interest to provide adaptability, high density, and robustness for the development of new bio-inspired circuits or systems. We have recently reported the nanoparticle organic memory field-effect transistor (NOMFET), which exhibits behaviors similar to a biological spiking synapse in neural network. It is considered as a promising nanocomponent to design neuromorphic adaptive computing circuits and systems. A functional model of NOMFET is presented in this paper, which allows the reliable conception and simulation of hybrid nano/complimentary metal-oxide-semiconductor circuits and architectures. Spice simulations of the model have demonstrated good agreement with the experimental results. By using the model, some complex neuromorphic functions such as synaptic gain control have been simulated. The model is developed in Verilog-A language and implemented on Cadence Virtuoso platform with Spectre 5.1.41 simulator. An iterative physical model and a number of experimental parameters have been integrated to improve the simulation accuracy. Special techniques and methods for dynamic behavior modeling have been developed, which could be extended to other nanodevices.