Current-mode building blocks for CMOS-VLSI design of chaotic neural networks

Abstract

This paper presents two nonlinear CMOS current-mode circuits that implement neuron soma equations for chaotic neural networks, and another circuit to realize programmable current-mode synapse using CMOS-compatible BJTs. They have been fabricated in a double-metal, single-poly 1.6 /spl mu/m CMOS technology and their measured performance reached the expected function and specifications. The neuron soma circuits use a novel, highly accurate CMOS circuit strategy to realize piecewise-linear characteristics in current-mode domain. Their prototypes obtain reduced area and low voltage power supply (down to 3 V) with clock frequency of 500 kHz. As regard to the synapse circuit, it obtains large linearity and continuous, linear, weight adjustment by exploitation of the exponential-law operation of CMOS-BJTs. The full accordance observed between theory and measurements supports the development of future analog VLSI chaotic neural networks to emulate biological systems and advanced computation.