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A Fully Energy-Autonomous Temperature-to-Time Converter Powered by a Triboelectric Energy Harvester for Biomedical Applications

Joanne Si Ying Tan, Jeong Hoan Park, Jiamin Li, Yilong Dong, Kwok Hoe Chan, Ghim Wei Ho, Jerald Yoo

2021IEEE Journal of Solid-State Circuits21 citationsDOIOpen Access PDF

Abstract

This article presents a fully energy-autonomous temperature-to-time converter (TTC), entirely powered up by a triboelectric nanogenerator (TENG) for biomedical applications. Existing sensing systems either consume too much power to be sustained by energy harvesting or have poor accuracy. Also, the harvesting of low-frequency energy input has been challenging due to high reverse leakage of a rectifier. The proposed dynamic leakage suppression full-bridge rectifier (DLS-FBR) reduces the reverse leakage current by more than 1000 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> , enabling harvesting from sparse and sporadic energy sources; this enables the TTC to function with a TENG as the sole power source operating at <1-Hz human motion. Upon harvesting 0.6 V in the storage capacitor, the power management unit (PMU) activates the low-power TTC, which performs one-shot conversion of temperature to pulsewidth. Designed for biomedical applications, the TTC enables a temperature measurement range from 15 °C to 45 °C. The energy-autonomous TTC is fabricated in 0.18- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1P6M CMOS technology, consuming 0.14 pJ/conversion with 0.014-ms conversion time.

Topics & Concepts

Energy harvestingTriboelectric effectCapacitorElectrical engineeringRectifier (neural networks)Leakage (economics)CMOSEnergy (signal processing)Computer sciencePower (physics)NanogeneratorMaterials scienceVoltageEngineeringPhysicsArtificial intelligenceQuantum mechanicsEconomicsArtificial neural networkStochastic neural networkComposite materialRecurrent neural networkMacroeconomicsAdvanced Sensor and Energy Harvesting MaterialsConducting polymers and applicationsInnovative Energy Harvesting Technologies