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.

A voltage controlled MEMS oscillator (VCMO) fabricated in 65nm CMOS process is proposed in this paper. The piezoelectric aluminum nitride (AlN) Contour-Mode MEMS resonator based oscillator can be operated under multiple frequencies from 204MHz, to 517MHz, to 850MHz. A maximum overall tuning range of 611ppm is achieved at a center frequency of 204MHz. The proposed oscillator operates in the sub-threshold region, resulting in a power consumption of 47μW measured under a 0.55V power supply, which is the lowest reported in literature for an application in the 200MHz range. The measured oscillator phase noise is -77dBc/Hz at 1kHz offset from the 204MHz carrier.

We present a two-dimensional, vision-based force sensor, capable of sensing muN level forces. There are currently no reliable, off-the-shelf, commercially-available force sensors to measure forces at this scale, that can be easily integrated into standard microrobotic test-beds. Our design consists of a planar, elastic mechanism with known force-deflection characteristics. A CCD camera is used to track the deformation of the mechanism as it is used to manipulate objects in a micro/meso-scale robotic manipulation test-bed. By observing the displacements of select points in the mechanism, the manipulation forces can be estimated. The modeling, design, microfabrication, calibration and experimental validation of the force sensor are presented with a brief discussion of an application to a robotic manipulation tasks such as microassembly.