Dynamical Analysis and Modeling of Experimental Data from Nonlinear Circuit Elements ("DYNAMODE")

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One important aim of this project is to derive and analyze new nonlinear memristor models by applying methods of circuit and system theory for a range of physical systems as presented in the project description. Although there is meanwhile a considerable bulk of literature on memristors, quantitative compact models with equations that properly define memristors, i.e. a state-dependent Ohm’s Law along with the defining state equations, have been proposed and analyzed in a few cases only. Here it is important to note that the application of the memristor formalism has dramatically accelerated research in nonvolatile memories and various forms of neuromorphic computation. For the future development of new innovative memristor circuits replacing partly conventional CMOS technology, it is necessary, to apply methods from physics and chemistry and to perform accurate and detailed measurements in order to obtain circuit theoretic device models allowing an accurate and stable simulation of memristor elements and circuits in standard designer software. Our investigations will be focused on the derivation of new dynamical models with appropriate tuning parameters to enable the models to be generalized to other systems by other researchers. This planned work is focused on a new very comprehensive experimental data base for the NbO2 memristor created at HP Labs and at the Lawrence Berkeley National Laboratory, it is already provided by Dr. Stanley Williams to the working group in Dresden where additional measurements will be performed in cooperation with the Namlab gGmbH. As compared to our ongoing work, here we need comprehensive investigations based on these data monitoring the device dynamics in a detailed form not being available so far. We are also planning to determine dynamical nonlinear models of a wider sets of mem-elements including various types of inductors and capacitors, giving new capabilities to existing circuit elements that could spur even more progress in sensing, signal analysis and local computation, i.e. computing on non-von Neumann architectures with local memory. Especially, we will apply derived models to explore a variety of applications for nonlinear dynamical circuit elements, such as signal transduction and analysis, amplification, data transmission and local computation.