Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. A Fast Modular Method for True Variation-Aware Separatrix Tracing in Nanoscaled SRAMs
 
research article

A Fast Modular Method for True Variation-Aware Separatrix Tracing in Nanoscaled SRAMs

Teman, Adam
•
Visotsky, Roman
2015
Ieee Transactions On Very Large Scale Integration (Vlsi) Systems

As memory density continues to grow in modern systems, accurate analysis of SRAM stability is increasingly important to ensure high yields. Traditional static noise margin metrics fail to capture the dynamic characteristics of SRAM behavior, leading to expensive over-design and disastrous under-design. One of the central components of more accurate dynamic stability analysis is the separatrix; however, its straightforward extraction is extremely time-consuming, and efficient methods are either non-accurate or extremely difficult to implement. In this paper, we propose a novel algorithm for fast separatrix tracing of any given SRAM topology, designed with industry standard transistor models in nano-scaled technologies. The proposed algorithm is applied to both standard 6T SRAM bitcells, as well as previously proposed alternative sub-threshold bitcells, providing up to three orders-of-magnitude speedup, as compared to brute force methods. In addition, for the first time, statistical Monte Carlo separatrix distributions are plotted.

  • Files
  • Details
  • Metrics
Loading...
Thumbnail Image
Name

14TVLSISeparatrix_final.pdf

Type

Preprint

Version

Submitted version (Preprint)

Access type

openaccess

Size

1.64 MB

Format

Adobe PDF

Checksum (MD5)

a8d01a81d213d157a216bbc6bc861d3b

Loading...
Thumbnail Image
Name

2014 - TVLSI - A Fast Modular Method for True Variation-Aware.pdf

Type

Publisher's Version

Version

Published version

Access type

openaccess

Size

2.9 MB

Format

Adobe PDF

Checksum (MD5)

8c037d61a339049470ad5d2ee254a169

Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés