A Calibration-Free Energy-Efficient IC for Link-Adaptive Real-Time Energy Storage Optimization of CM Inductive Power Receivers
Bibliographic record
Abstract
The design, development, and experimental characterization of an integrated circuit (IC) for maximizing the energy storage efficiency in resonant inductive current-mode (CM) power receivers are presented. The IC is designed to monitor the receiver’s incident energy and to determine and employ an optimal timing scheme for controlling its operation to maximize energy storage for a given period of time. Thanks to continuous monitoring of incident waveform dynamics, the IC automatically adapts its optimal solution on-the-fly to any change in the inductive link’s physical or electrical parameters (e.g.,$Q$-factor variations and coils movement). To minimize the IC’s power consumption, all the high-speed blocks for monitoring, optimization point computation, and system control are implemented using analog circuits, making the solution needless of a high-speed analog-to-digital converter (ADC)/digital-to-analog converter (DAC). The IC is fabricated in a standard 0.18$\mu \text{m}$CMOS process with an active area of 0.45 mm2. Its efficacy in real-time optimization of energy storage efficiency is experimentally validated for two different inductive links, showing perfect agreement with empirical measured data and theoretical predictions. Our measurement results show that by using the presented IC, the energy storage efficiency is improved by 53% and 67% for the two tested links, compared to the conservative schemes used for the receiver’s operation control. It has also been shown that the IC’s power consumption in the worst case scenario is two orders of magnitude smaller than the energy it saves through optimization. To the best of our knowledge, this is the first reported integrated link-adaptive calibration-free solution for optimizing the energy storage efficiency in CM inductive receivers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".