Always-On 12-nW Acoustic Sensing and Object Recognition Microsystem for Unattended Ground Sensor Nodes

TLDR: An algorithm-circuit cross optimization is introduced to realize a 12-nW stand-alone microsystem that integrates the analog frontend with the digital backend signal classifier and replaces a conventional high-power/area-consuming parallel feature extraction using the fast Fourier transform.

Abstract: This paper presents an ultra-low power acoustic sensing and object recognition microsystem for Internet of Things applications. The microsystem is targeted for unattended ground sensor nodes where long-term (decades) life time is desired without the need for battery replacement. The system incorporates an microelectromechanical systems microphone as a frontend sensor along with active circuitry to identify target objects. We introduce an algorithm-circuit cross optimization to realize a 12-nW stand-alone microsystem that integrates the analog frontend with the digital backend signal classifier. The frequency-domain analysis of target audio signals reveals that the system can operate with a relatively low bandwidth ( 3 dB) which significantly relaxes power constraints on both analog frontend and digital backend circuits. To further relax the current requirement of the preceding amplifier, we propose an 8-bit SAR-analog-to-digital converter that is designed to have a highly reduced sampling capacitance ( 95% reliability and consumes only 12 nW with continuous monitoring.