A High-Gain Power-Matching Technique for Efficient Radio-Frequency Power Harvest of Passive Wireless Microsystems
Bibliographic record
Abstract
This paper proposes a transformer power-matching and gain-boosting technique to improve the efficiency of power harvesting of passive wireless microsystems. The proposed method utilizes a step-up transformer inserted between the antenna and voltage multiplier of passive wireless microsystems to perform both impedance transformation for power matching and voltage amplification prior to rectification. The series resistance of the primary winding is minimized using multiple metal layers connected using vias, while the width of the spiral of the secondary winding is made much smaller as compared with that of the primary winding to maximize the turn ratio and minimize its shunt capacitive losses. The detailed analysis of the proposed method and simulation results from Spectre of Cadence Design Systems are presented. The proposed power-matching and gain-boosting network, together with voltage multipliers, has been implemented in TSMC 0.18-m 1.8-V six-metal CMOS technology with thick-metal options. For the purpose of comparison, an LC-power-matching and gain-boosting network with the identical voltage multiplier has also been implemented on the same chip. Measurement results demonstrate that the proposed transformer power-matching and gain-boosting technique greatly improves the power sensitivity and efficiency as compared with widely used LC matching approaches.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".