By Topic

Optimizing Parametric BIST Using Bio-inspired Computing Algorithms

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

4 Author(s)
Nemati, N. ; Electr. & Comput. Eng. Dept., Univ. of Tehran, Tehran, Iran ; Simjour, A. ; Ghofrani, A.-A. ; Navabi, Z.

Optimizing the BIST configuration based on the characteristics of the design under test is a complicated and challenging work for test engineers. Since this problem has multiple optimization factors, trapping in local optimums is very plausible. Therefore, regular computing algorithms cannot efficiently resolve this problem and utilization of some algorithms is required. In this work, by applying genetic algorithm (GA) and particle swarm optimization (PSO) - which are two well-known bio-inspired computing algorithms -, reconfiguring an optimum parametric BIST is exercised. These methods are applied to configure a parametric BIST for some ISCAS benchmarks, and the efficiency of the resulted configuration is evaluated by means of Verilog HDL procedural language interface (PLI).Using HDL environment along with bio-inspired algorithms, significant advantages over previous works are obtained.

Published in:

Defect and Fault Tolerance in VLSI Systems, 2009. DFT '09. 24th IEEE International Symposium on

Date of Conference:

7-9 Oct. 2009