Memristor Model/ Stochastic Memristor Model OverviewFor the Memristor model: Sample output (Feel free to use/modify these codes as you see fit. Any publications (codes, papers, technical reports,..) in which our codes (in their original or a modified format) have been used should should cite the following references.) A. Radwan, M. A Zidan, K. N. Salama, “HP Memristor Mathematical Model for Periodic Signals and DC,” IEEE International Midwest Symposium on Circuits and Systems, pp. 861-864, Seattle, USA, August 2010. A. Radwan, M. A Zidan, K. N. Salama, “On the Mathematical Modeling of Memristors,” 22nd International Conference on Microelectronics (ICM 2010), pp. 284-287, Cairo, Egypt, December 2010. A Verilog model of the memristor is provided. Its more stable than using spice models, very fast and uses the fundamental charge equations rather than trying to mimic a specific implementation. A MATLAB function that returns the numerical solution of the resistance of the memristor for a arbitrary voltage and time vectors.Archive include: m file and example file. Copyright (c) 2011, M. Affan Zidan, A. G. Radwan and K. N. Salama King Abdullah University of Science and Technology All rights reserved. For the Stochastic Memristor model: We developed a stochastic model of memristor devices. Innate stochasticity is modeled in a circuit compatible format and incorporated into models of threshold based memristors covering a wide set of designs. Experimental fitting to fabricated devices highlights the modeling accuracy and the generalized form of behavior. Feel free to use/modify these codes as you see fit. Any publications (codes, papers, technical reports,..) in which our codes (in their original or a modified format) have been used should cite the following references. Rawan Naous, Maruan Al-Shedivat, and Khaled Nabil Salama, Stochasticity Modeling in Memristors, IEEE Transactions on Nanotechnology (TNANO), vol. 15, no. 1, pp. 15-28, 2016 . DOI:10.1109/TNANO.2015.2493960 Maruan Al-Shedivat, Rawan Naous,Gert Cauwenberghs, and Khaled Nabil Salama, Memristors Empower Spiking Neurons with Stochasticity, IEEE Journal of Emerging technologies in circuits and systems, VOL. 5, NO. 2, 242-253, JUNE 2015 Copyright (c) 2015, Rawan Naous, Maruan Al-Shedivat, Khaled Nabil Salama King Abdullah University of Science and Technology All rights reserved. Last Update: 9-Nov-2015; run using run using spectre version 11.1.0.509.isr cadence version 2012.09 For the developed tools, click here.
