Fast statistical model of TiO 2 thin-film memristor and design implication

Published

Conference Paper

The emerging memristor devices have recently received increased attention since HP Lab reported the first TiO 2-based memristive structure. As it is at nano-scale geometry size, the uniformity of memristor device is difficult to control due to the process variations in the fabrication process. The incurred design concerns in a memristor-based computing system, e.g, neuromorphic computing, can be very severe because the analog states of memristors are heavily utilized. Therefore, the understanding and quantitative characterization of the impact of process variations on the electrical properties of memristors become crucial for the corresponding VLSI designs. In this work, we examined the theoretical model of TiO 2 thin-film memristors and studied the relationships between the electrical parameters and the process variations of the devices. A statistical model based on a process-variation aware memristor device structure is extracted accordingly. Simulations show that our proposed model is 3 4 magnitude faster than the existing Monte-Carlo simulation method, with only 2% accuracy degradation. A variable gain amplifier (VGA) is used as the case study to demonstrate the applications of our model in memristor-based circuit designs. © 2011 IEEE.

Full Text

Duke Authors

Cited Authors

  • Hu, M; Li, H; Pino, RE

Published Date

  • December 1, 2011

Published In

Start / End Page

  • 345 - 352

International Standard Serial Number (ISSN)

  • 1092-3152

International Standard Book Number 13 (ISBN-13)

  • 9781457713989

Digital Object Identifier (DOI)

  • 10.1109/ICCAD.2011.6105353

Citation Source

  • Scopus