Optimum Temperatures for Enhanced Power Conversion Efficiency (PCE) of Zero-Bias Diode-Based Rectifiers
Bibliographic record
Abstract
Ambient power rectifiers are pivotal for batteryless or self-powered internet of things architectures and self-sustained communication/sensor platforms. The core of rectifiers, a nonlinear device, has been subject to significant research and improvements recently, especially at a low-power level, where rectifying efficiency is so limited. Current responsivity (the key rectification parameter describing the nonlinearity) is known to be impacted by the operating temperature, as predicted by William Shockley's law. However, no work has been carried out to quantify the impact of temperature in the RF rectification process, nor to relate existing diodes with their optimum operating temperature range. To address those missing links, this article first develops an analytical method to predict power conversion efficiency (PCE) of rectifiers from approximate milli-watt down to nano-watt level. Next, it identifies the optimum operating temperature of rectifiers corresponding to peak PCE. It also reports that the previous PCE ceiling of 15% (at an input power level below -30 dBm) can be broken when rectifiers operate at their optimum temperatures. Enhanced PCE results are then validated experimentally on SMS7630 and HSMS-2850-based rectifiers when operating at their optimum temperatures. Noticeably, the SMS7630-based rectifier delivers efficiency of 17.5% at -30 dBm and 41.7% at -20 dBm, showing respectively comparable and better PCE than that of tunnel diode-based counterparts at similar power levels. This article also indicates that rectifier design should consider the operating temperature when selecting diodes to maximize rectifying potential.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".