Behavioral testing of cellular neural networks

Author

Willis, John ; De Gyvez, José Pineda

Author_Institution

Dept. of Electr. Eng., Texas A&M Univ., College Station, TX, USA

Volume

6

fYear

1994

fDate

30 May-2 Jun 1994

Firstpage

229

Abstract

This paper addresses the functional behavior of Cellular Neural Networks (CNN). The impact of variable convergence times on the proper operation of the network is discussed A test method is presented to determine the functionality of the network. The function fault models assume that the cells are unable to switch between limiting states. The proposed method attains 100% stuck-at fault coverage without any extra hardware for its implementation. Moreover, the required number of test vectors is constant and independent of the array size which makes it suitable for practical implementations. The paper discusses the new fault model, presents the algorithmic procedures and shows simulated testing results

Keywords

cellular neural nets; convergence; fault diagnosis; testing; algorithmic procedures; behavioral testing; cellular neural networks; fault model; functional behavior; stuck-at fault coverage; variable convergence times; Cellular neural networks; Circuit faults; Convergence; Fault detection; Hardware; Neural networks; Nonlinear circuits; Switches; Testing; Voltage;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1994. ISCAS '94., 1994 IEEE International Symposium on

Conference_Location

London

Print_ISBN

0-7803-1915-X

Type

conf

DOI

10.1109/ISCAS.1994.409569

Filename

409569