Hardware and algorithms for the functional evaluation of cellular neural networks and analog arrays

Abstract

Analog arrays are a generalization of cellular neural networks (CNN) which consist of a regular array of nonlinear analog processors at each node and nearest neighbor interactions. Analog arrays can incorporate nonlinearities in both the input and output functions and, contrasted with CNN arrays, have continuous-valued outputs in the equilibrium state. The general analog array, though more powerful than the CNN, presents a functional test nightmare. Since the output is continuous-valued (even at equilibrium) and the dynamics can be complicated, evaluating whether a fabricated VLSI array complies with the intended processing function for a wide range of inputs can be very difficult and time consuming. The number of analog inputs and outputs also strains most modern analog VLSI automatic test equipment (ATE). The authors present both a new hardware design for massive analog circuit testing and algorithms for functional test of analog arrays.<>