Neurocomputing and associative memories based on ovenized aluminum nitride resonators

Abstract

Neurocomputing has been regarded as an intriguing alternative to the von Neumann architecture for computing systems, especially for such applications as pattern recognition, image processing, and associative memory. However, implementations using CMOS technology have largely been considered impractical due to the required circuit complexity and corresponding power consumption. In this paper we propose a novel configuration for a recently-developed ovenized aluminum nitride (AlN) resonator that is used as a thermally-tunable analog impedance for implementation of artificial neurons and synapses. We demonstrate and elaborate on our building blocks for artificial neurons and synapses using such resonators. Localized impedance tuning via multiple heaters on a single device enables a compact DAC (digital-to-analog converter) for programming artificial synapses and a simple-yet-efficient means for implementing artificial neurons. We also show the functionality of our proposed circuits using two pattern recognition examples based on compact circuit simulation models for ovenized AlN resonators. The resonator device models are characterized from measurement data.