Autors: Tsenov, G. C., Kirilov, S. M., Mladenov, V. M. Title: SPICE memristor model based on estimate from measured data Keywords: Circuits and systems, Memristors, Modelling and simulation, Parameter model identificationAbstract: Purpose: The real production memristor devices produce a modeling challenge due to having a memristor variance and henceforth a measured difference between breadboard made circuits and LTSPICE or MATLAB simulated circuits with classical models, resulting in variances owed to difference in existing models and real hardware. When a new memristor is studied, usually no model is provided and it’s useful if there is a tool that automatically update models with production parameters. The purpose of this paper is to implement a procedure in MATLAB that takes measured data, approximates parameters and provides a MATLAB and LTSPICE models for precise representation of real memristor devices. Design/methodology/approach: The optimal values of production level memristor model’s coefficients can be estimated for various existing models by applying the measured voltage-current relationship and by using optimization procedure to match the coefficients to existing model. With graphical user interface (GUI) in MATLAB environment a user can select measured data and which model to be used as some are good for high frequency and others for low or mid frequency memristors. Findings: The analyses, which were performed in MATLAB and LTSPICE, validate the efficiency and accuracy of the proposed memristor model matching procedure. The analysis case utilizes a comparison with some very commonly used standard and modified memristor models. Originality/value: A GUI with optimization procedure for parameter estimation of real-world production memristors into simple memristor models or with parasitic parameters is applied in MATLAB and then transferred in LTSPICE with amplitude-frequency responses and voltage-current relations analyzed for different frequencies. References - Aggarwal, C. (2018), Neural Networks and Deep Learning, Springer International Publishing AG, ISBN 978-3-319-94463-0, p. 541, doi: 10.1007/978-3-031-29642-0.
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