A Comprehensive study on RF energy harvesters: modelling, design, and implementation

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2016
Gharehbaghi, Kaveh
The Dickson and the threshold self-compensated UHF rectifier architectures are investigated in detail. At first, the Dickson architecture is studied by aiming to find an accurate yet general input-output relationship. It is revealed that the ratio of peak forward current over the load current could be well approximated for broad range of incoming RF amplitudes. For threshold self-compensated UHF rectifiers, a behavioral model is presented for core unit of the structure to ease the analytical discussions. Then, a model is elaborated by noticing the steady-state waveforms at different nodes. Then the coherence between the dependence of the load current and power conversion efficiency over the variation of the generated DC voltage is used for finding the optimum design parameters of the structure. In circuit design level, two novel architectures are introduced. The first one is threshold-compensated UHF rectifier with “self-calibration technique” which avoids the reduction of power conversion efficiency over the increment of the incoming RF signal. The idea is to separate the compensation-generator from the main rectifier by adding two auxiliary rectifiers. Second circuit for UHF rectifiers is “switched-gate” architecture which increases the peak power conversion efficiency. The method is based on adaptive threshold-compensation which establish different gate-source voltage for the diodes during the forward and reverse conduction phases. Analysis of conjugate matching and passive Q-boosting methods for interfacing between antenna and rectifier is also conducted. At the end, a novel reference block is designed by reducing the power consumption of the voltage reference down to 89 nW while providing temperature/supply independent reference. 

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Citation Formats
K. Gharehbaghi, “A Comprehensive study on RF energy harvesters: modelling, design, and implementation,” Ph.D. - Doctoral Program, Middle East Technical University, 2016.