Nonlinearity and parameterization of Schottky diodes-based battery-free harmonic transponder for millimeter-wave 5G applications
Bibliographic record
Abstract
Deployment of 5G network infrastructure is a timely opportunity for millimeter-sized battery-free sensors. However, millimeter-wave (mmW) devices often suffer from high conversion loss and path loss that are heavily limiting their communication/detection distance, especially for the case of harmonic transponders based on Schottky diodes. A deep and comprehensive parametric understanding of the second-harmonic generation mechanism of Schottky diodes in the mmW 5G bands can help us to identify suitable diodes or guide diode fabrication to reduce transponder conversion loss. This work reveals that both diode nonlinear junction resistance and capacitance contribute to the second-harmonic generation across the mid-band (sub-7 GHz) and high-band (mmW) 5G frequency bands. However, the nonlinear junction capacitance dominates the second-harmonic generation in the mmW bands. Without Joule heating during the conversion process, the capacitive nonlinearity is more efficient than the resistive nonlinearity, which means that a Schottky diode with a lower junction capacitance will efficiently reduce its associated conversion loss. The VDI GaAs zero bias diode with a low zero bias nonlinear junction capacitance (19.19 fF) shows superior conversion loss performance, which indicates that it can be employed to enhance the detection distance of battery-free harmonic transponders in the mmW 5G bands.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".