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Alternate characterization technique for static random‐access memory static noise margin determination
Author(s) -
Merino Jose Luis,
Bota Sebastia Antoni,
Picos Rodrigo,
Segura Jaume
Publication year - 2013
Publication title -
international journal of circuit theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.364
H-Index - 52
eISSN - 1097-007X
pISSN - 0098-9886
DOI - 10.1002/cta.1832
Subject(s) - margin (machine learning) , noise (video) , computer science , static random access memory , adaptive neuro fuzzy inference system , random access memory , electronic engineering , fuzzy logic , algorithm , engineering , computer hardware , fuzzy control system , artificial intelligence , machine learning , image (mathematics)
The cell static noise margin (SNM) is widely used as a stability criterion for static random‐access memory cells design. This parameter is typically determined through electrical simulations since direct experimental characterization of SNM is not achievable. In this work, we present a methodology that provides an indirect measurement of the SNM on a per‐cell basis for six‐transistor SRAMs. It is based on combining an Adaptive Neuro‐Fuzzy Inference System (ANFIS) with circuit‐level cell experimentally measurable parameters as input variables to the tool. We show that it is possible to obtain the SNM for individual memory cells using the same experimental setup and data than that required for shmoo plot measurements. Results confirm that the SNM can be experimentally estimated with a relative error compared with electrical simulations that is below 0.5%. Copyright © 2012 John Wiley & Sons, Ltd.