Title :
CMOS implementation of an analogically programmable cellular neural network
Author :
Betta, G. F Dalla ; Graffi, S. ; Kovács, Zs M. ; Masetti, G.
Author_Institution :
Dept. of Electron., Bologna Univ., Italy
fDate :
3/1/1993 12:00:00 AM
Abstract :
The criteria for designing the basic building blocks of an analogically programmable cellular neural network (CNN) in a 1.5-μm CMOS technology are reported. The simulated electrical performances of a 10×10 CMOS CNN, consisting of about 8000 MOS transistors, are presented and discussed. It is shown that the designed CNN can be successfully used to perform such useful functions as noise removal, edge detection, hole filling, shadow detection, and connected component recognition
Keywords :
CMOS integrated circuits; analogue processing circuits; edge detection; neural chips; 1.5 micron; CMOS technology; CNN; analogically programmable cellular neural network; connected component recognition; edge detection; hole filling; noise removal; shadow detection; simulated electrical performances; Artificial neural networks; CMOS process; CMOS technology; Cellular neural networks; Circuits; Image edge detection; Image processing; MOSFETs; Signal processing; Space technology;
Journal_Title :
Circuits and Systems II: Analog and Digital Signal Processing, IEEE Transactions on
