Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 12:00 PM ET (12:00 - 16:00 UTC). We apologize for the inconvenience.
By Topic

Fault diagnosis of VLSI circuits with cellular automata based pattern classifier

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Sikdar, B.K. ; Dept. of Comput. Sci. & Technol., Bengal Eng. & Sci. Univ., West Bengal, India ; Ganguly, N. ; Pal Chaudhuri, P.

This paper reports a fault diagnosis scheme for very large scale integrated (VLSI) circuits. A special class of cellular automata (CA) referred to as multiple attractor CA (MACA) is employed for the design. State transition behavior of MACA has been analyzed to build a model that can efficiently classify the test responses of a VLSI circuit to diagnose its faulty subcircuit. The MACA-based model, in effect, provides an implicit storage for voluminous test response data and replaces the traditional fault dictionary used for diagnosis of VLSI circuits. The proposed diagnosis scheme employs significantly lesser memory to store the MACA parameters and performs faster diagnosis. Experimental results establish the efficiency of the model in respect of memory overhead, execution speed and percentage of diagnosis.

Published in:

Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on  (Volume:24 ,  Issue: 7 )