Abstract

In this paper, a unified and open linear technology simulation program with integrated circuit emphasis (LTSPICE) memristor library is proposed. It is suitable for the analysis, design, and comparison of the basic memristors and memristor-based circuits. The library could be freely used and expanded with new LTSPICE memristor models. The main existing standard memristor models and several enhanced and modified models based on transition metal oxides such as titanium dioxide, hafnium dioxide, and tantalum oxide are included in the library. LTSPICE is one of the best software for analysis and design of electronic schemes. It is an easy to use, widespread, and free product with very good convergence. Memristors have been under intensive analysis in recent years due to their nano-dimensions, low power consumption, high switching speed, and good compatibility with traditional complementary metal oxide semiconductor (CMOS) technology. In this work, their behavior and potential applications in artificial neural networks, reconfigurable schemes, and memory crossbars are investigated using the considered memristor models in the proposed LTSPICE library. Furthermore, a detailed comparison of the presented LTSPICE memristor model library is conducted and related to specific criteria, such as switching speed, operating frequencies, nonlinear ionic drift representation, boundary effects, switching modes, and others.

Highlights

  • The resistance switching phenomenon observed in metal oxides such as aluminum oxide, titanium dioxide, tantalum oxide, and others has been analyzed since 1970 [1]

  • The proposed unified and open LTSPICE library models could be useful for the readers who are interested in the analysis and design of memristor-based schemes and devices

  • According to the intensive research on memristors and memristor-based circuits in recent years for application in neural networks, memories, reconfigurable devices, and for in-memory computing, a unified LTSPICE memristor library is described in this paper

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Summary

Introduction

The resistance switching phenomenon observed in metal oxides such as aluminum oxide, titanium dioxide, tantalum oxide, and others has been analyzed since 1970 [1]. If the boundary between the layers reaches the bottom border of the memristor nanostructure, the length of the doped region has a maximal value In this state, the memristor has a minimal resistance of 100 Ω [4]. If the boundary between the layers of the memristor is on the top border of the memristor nanostructure, its whole region is based on pure titanium dioxide material and the resistance of the memristor has a maximal value of 16 kΩ [4]. This state of the considered element is known as a fully open state. Its maximal resistance corresponding to this state is known as the OFF resistance (ROFF) [4]

Memristor Modeling
Titanium Dioxide Memristors’ Modeling
Standard Titanium Dioxide Memristor Models
Modified Titanium Dioxide Memristor Models
Hafnium Dioxide Memristors’ Modelling
Standard Hafnium Dioxide Memristor Models
Modified Hafnium Dioxide Memristor Models
Tantalum Oxide Memristor’s Modelling
Existing and Standard Tantalum Oxide Memristor Models
Modified tantalum oxide memristor models
LTSPICE Memristor Library Models—Generation and Analysis
Simulation and Analysis of Memristor-Based Circuits in LTSPICE Environment
Analysis of a Passive Memristor Memory Crossbar
Analysis of a Feed-Forward Memristor-Based Neural Network
Discussion and Conclusions
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