Ultracompact RF Rectifier Circuit for Implantable Devices
Bibliographic record
Abstract
This study proposes an innovative design approach for an ultra-compact RF rectifier, emphasizing high power conversion efficiency (PCE). The rectifier design employs a dual-branch cell configuration, labeled as Section-1 (S1) and Section-2 (S2), to enhance its performance characteristics. To support biomedical implant applications, these branches are incorporated with a meandered line network, designated as (ML1 and ML2). A radial stub is employed in the S1 structure, while series inductors are additionally connected to S1 and S2 to achieve improved performance characteristics. To improve power delivery performance, the proposed rectifier is specifically optimized for enhanced transfer efficiency within the frequency range of 1.28 GHz to 1.52 GHz. This makes it highly suitable for integration into wireless power transfer systems (WPTs) designed for biomedical implants. Both the simulated (experimental) results confirmed a maximum RF-to-DC PCE of 78.80% (77.7%), achieved at an input powerPinlevel of 4 dBm. Moreover, the proposed design achieves an RF-to-DC conversion efficiency greater than 25% atPinlevel of -20 dBm, thereby demonstrating its suitability for efficient operation under low-power conditions. The rectifier is fabricated on an RT/Duroid substrate, resulting in a compact footprint measuring 7.8 mm by 9.3 mm. A single-series diode (SSrd) configuration is employed to achieve the desired rectification performance. To ensure a wide impedance bandwidth, a sequential matching technique is applied, effectively optimizing the device’s performance throughout the specified frequency spectrum. This work demonstrates the effectiveness of the proposed rectifier in enabling WPT for biomedical implant applications, with particular emphasis on scenarios that demand efficient harvesting of ambient energy.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".