DocumentCode
1602612
Title
Robust CMOS CNN implementation with respect to manufacturing inaccuracies
Author
Paasio, Ari ; Dawidziuk, Adam ; Halonen, Kari ; Porra, Veikko
Author_Institution
Electron. Circuit Design Lab., Helsinki Univ. of Technol., Espoo, Finland
fYear
1996
Firstpage
381
Lastpage
385
Abstract
A new idea of cellular neural network VLSI implementation based on a high gain sigmoid function is shown. It has been proved in this paper that this method is robust to the parameter inaccuracies. Conclusions lead to some suggestions and restrictions for template tolerances. An example of VLSI implementation-the test chip consisting of 16×16 cells in 1.2 μm CMOS with 19 fully programmable coefficients has been fabricated. Measurements indicate good efficiency of this implementation
Keywords
CMOS integrated circuits; VLSI; cellular neural nets; neural chips; cellular neural network VLSI implementation; high gain sigmoid function; manufacturing inaccuracies; robust CMOS CNN implementation; template tolerances; CMOS technology; Cellular neural networks; Electronic circuits; Electronic mail; Laboratories; Manufacturing; Robustness; Semiconductor device measurement; Testing; Very large scale integration;
fLanguage
English
Publisher
ieee
Conference_Titel
Cellular Neural Networks and their Applications, 1996. CNNA-96. Proceedings., 1996 Fourth IEEE International Workshop on
Conference_Location
Seville
Print_ISBN
0-7803-3261-X
Type
conf
DOI
10.1109/CNNA.1996.566604
Filename
566604
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