Paper Info
Title
Memristor SPICE model and crossbar simulation based on devices with nanosecond switching time
Abstract
This paper presents a memristor SPICE model that is able to reproduce current-voltage relationships of previously published memristor devices. This SPICE model shows a stronger correlation to various published device data when compared to existing SPICE models. Furthermore, switching characteristics of published memristor devices with switching times in the nanosecond scale were modeled. Therefore, this model can be used to accurately simulate neural systems based on these high-speed memristors. This paper also demonstrates how this model can be used to accurately calculate switching energy of these high-speed devices, leading to more accurate power calculations in memristor based neural systems. Memristor crossbar circuits provide a potential method for developing very high density neural classifiers. This model was able to simulate crossbar circuits containing up to 256 memristors. It is significantly less likely to cause convergence errors when operating in the nanosecond switching regime with a large number of devices when compared with existing SPICE models.
Year
DOI
Venue
2013
10.1109/IJCNN.2013.6706773
Neural Networks
Keywords
DocType
ISSN
SPICE,memristors,neural nets,crossbar simulation,current-voltage relationships,high-speed memristors,memristor SPICE model,memristor based neural systems,memristor crossbar circuits,memristor devices,nanosecond switching time,neural classifiers,switching energy,switching times
Conference
2161-4393
ISBN
Citations 
PageRank 
978-1-4673-6128-6
25
1.47
References 
Authors
5
4
Name
Order
Citations
PageRank
Chris Yakopcic114013.10
Tarek M. Taha228032.89
Guru Subramanyam3615.52
Robinson E. Pino413013.14