Current-mode techniques for the implementation of continuous- and discrete-time cellular neural networks

Abstract

A unified, comprehensive approach to the design of continuous-time (CT) and discrete-time (DT) cellular neural networks (CNNs) using CMOS current-mode analog techniques is presented. The net input signals are currents instead of voltages, which avoids the need for current-to-voltage dedicated interfaces in image processing tasks with photosensor devices. Outputs may be either currents or voltages. Cell design relies on exploiting current mirror properties for the efficient implementation of both linear and nonlinear analog operators. Basic design issues, the influence of nonidealities and advanced circuit design issues, and design for manufacturability considerations associated with statistical analysis are discussed. Experimental results are given for three prototypes designed for 1.6- mu m n-well CMOS technologies. One is discrete-time and can be reconfigured via local logic for noise removal, feature extraction (borders and edges), shadow detection, hole filling, and connected component detection (CCD) on a rectangular grid with unity neighborhood radius. The other two prototypes are continuous-time and fixed template: one for CCD and other for noise removal. >