@inproceedings{6b45e8a015a843dda518ac6e0381bec7,
title = "V-VTEAM: A Compact Behavioral Model for Volatile Memristors",
abstract = "Volatile memristors have recently gained popularity as promising devices for neuromorphic circuits, capable of mimicking the leaky function of neurons and offering advantages over capacitor-based circuits in terms of power dissipation and area. Additionally, volatile memristors are useful as selector devices and for hardware security circuits such as physical unclonable functions. To facilitate the design and simulation of circuits, a compact behavioral model is essential. This paper proposes V-VTEAM, a compact, simple, general, and flexible behavioral model for volatile memristors, inspired by the VTEAM nonvolatile memristor model and developed in MATLAB1. The validity of the model is demonstrated by fitting it to an ion drift/diffusion-based Ag/SiOx/C/W volatile memristor, achieving a relative root mean error square of 4.5\%.",
keywords = "behavioral model, compact model, neuromorphic computing, volatile memristor",
author = "Tanay Patni and Rishona Daniels and Shahar Kvatinsky",
note = "Publisher Copyright: {\textcopyright}2024 IEEE.; 6th IEEE International Flexible Electronics Technology Conference, IFETC 2024 ; Conference date: 15-09-2024 Through 18-09-2024",
year = "2024",
doi = "10.1109/IFETC61155.2024.10771870",
language = "الإنجليزيّة",
series = "6th IEEE International Flexible Electronics Technology Conference, IFETC 2024 - Proceedings",
booktitle = "6th IEEE International Flexible Electronics Technology Conference, IFETC 2024 - Proceedings",
}