P. Tamilmozhi, Dr.K.Somu, Mrs.M.Meena
A built-in self-repair analyzer with the best repair rate for redundant memory arrays is used in this project. The prevailing methods made use of a stack and a finite-state machine for depth first search. The circuit's design enables use of the parallel prefix method, and it may be set up in a variety of ways to satisfy space and test time needs. The number of content addressable memory entries used to hold the defect addresses makes up the majority of the infrastructure overall, and it only increases quadratically as the number of repair elements increases. In the BIST architecture, the linear feedback shift register is utilised to count the subsequent state and also necessitates high transitions. To locate the defect address to store in mustrepair analyzer, the content addressable memory is used. The suggested approaches make use of pre computation content addressable memory and bit swapping linear feedback shift registers to minimise transition and power consumption, respectively, as well as to shorten the time delay in the analyzer that needs repair. Even in the worst situation, only one test is needed. It selectively stores fault addresses by carrying out the must-repair analysis during the test, and uses the saved fault addresses for the final analysis to provide a solution. Additionally, the architecture has been expanded to accommodate several word-focused memory types.
Article Details
Unique Paper ID: 160996

Publication Volume & Issue: Volume 10, Issue 2

Page(s): 278 - 283
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews