Bibliographic record
Abstract
The explosive growth in Wireless Sensor Networks, ranging from the Internet of Things to embedded sensors for smart infrastructures and biomedical implants, continues to motivate the design and development of low-power energy harvesting systems that can either remotely charge a battery in the sensor unit or completely self-power the sensor from harvested energy. This thesis focuses on designing new benchmarks for monolithic RF rectifiers implemented in CMOS technology. A design procedure based on the theory of time-reversal duality is applied to transform power amplifier circuits into self-synchronous rectifier circuits. The methodology is distinctly different from other CMOS RF rectifier designs which use voltage-multiplier techniques. An advantage of transforming amplifier into rectifier circuits is that output matching in the amplifier is transformed to input matching in the rectifier. All the rectifier circuits described in this work include single-ended RF input ports matched to 50 Ω. Also, the circuits are self-biased and completely powered from the RF signal. Terminating rectifiers with an optimum load is important to maximize the RF to dc power conversion efficiency. The optimum load resistance can vary as a function of input power. Therefore, an adjustable load that tracks changes in RF power is proposed to maximize efficiency. As a way of implementing an adjustable load circuit, a discontinuous mode dc to dc converter that is controlled by an input loop to regulate the load impedance of the rectifier is presented. The analysis, design and experimental results of the adjustable load circuit are described and the performance is verified with 10 W GaN Class-F RF rectifier. In the final part of this thesis, a bidirectional and reconfigurable class-DE circuit is proposed to support the implementation of embedded sensor nodes that are self-powered and using a time-division duplexing architecture. The circuit reconfigures the same RF front-end circuit into either a high-efficiency rectifier or a highly efficient power oscillator that can be modulated to transmit sensor data. The two modes can be time multiplexed to switch between RF energy harvesting and transmission. The design was implemented in 65 nm CMOS technology and experimental results for both modes of operation are presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".