Open AccessParameters for Stability of Reconfigurable Memory and 6T SRAM Cell
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i1143.0789s19
Subject(s) - static random access memory , margin (machine learning) , computer science , electronic engineering , stability (learning theory) , voltage , noise margin , computer hardware , engineering , transistor , electrical engineering , machine learning
As the technology is improving, channel length of MOSFET is scaling down. In this environment stability of SRAM becomes the major concern for future technology. Static noise margin (SNM) plays a vital role in stability of SRAM. This paper gives an introduction to the reconfigurable memory and 6T SRAM cell. It includes the implementation, characterization and analysis of reconfigurable memory cell and its comparison with the conventional 6T SRAM cell for various parameters like read margin, write margin, data retention voltage, temperature and power supply fluctuations and depending upon these analysis we find SNM for 6T and 8T SRAM cell. The tool used for simulation purpose is IC Station by Mentor Graphics using 350nm technology at supply voltage of 2.5volts.