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A Self-Matching Rectifier Based on an Artificial Transmission Line for Enhanced Dynamic Range

Taejoo Oh, Taejun Lim, Yong‐Shik Lee

2022IEEE Transactions on Circuits and Systems I Regular Papers17 citationsDOI

Abstract

In this paper, self-matching rectifiers for enhanced input dynamic range are demonstrated. The method is based on an artificial transmission line, which consists of a stepped-impedance transmission line loaded with shunt varactors. The output DC voltage of the rectifier is fed back to the varactors to control the effective electrical length and characteristic impedance of the line. Because this bias voltage changes as the power input to the rectifier changes, a matching network that maintains impedance matching regardless of the input power can be developed without additional circuits, thus substantially improving the input dynamic range. The experimental results for prototype rectifiers show a dynamic range as wide as 25 dB with only a single Schottky diode, an improvement of 11 dB over the rectifier with a conventional matching scheme, in which the conversion efficiency remains above 50%. The effectiveness of the proposed method is also verified with the experimental results for rectennas based on the demonstrated rectifiers.

Topics & Concepts

Rectifier (neural networks)Impedance matchingTransmission lineElectrical impedanceSchottky diodeElectronic engineeringVoltageDynamic rangePrecision rectifierComputer scienceElectrical engineeringDiodePower factorEngineeringRecurrent neural networkMachine learningArtificial neural networkStochastic neural networkEnergy Harvesting in Wireless NetworksInnovative Energy Harvesting TechnologiesWireless Power Transfer Systems
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