Characterization of Leakage Current in Buried Heterostructure Semiconductor InGaAsP Lasers Using a Diode-Resistor Electrical Model
Bibliographic record
Abstract
In buried heterostructure (BH) lasers, leakage current becomes a problem at high input currents, wasting power and reducing optical efficiency. This article introduces an electrical model that represents the voltagecurrent (VI) characteristics of forward-biased BH lasers. This model can be used to explain and predict leakage current. It also gives a diagnostic tool to compare experimental BH lasers, using simpleVImeasurements. In this experiment, 72 BH lasers with three different mesa top layer doping levels were measured. Then, a curve fit was performed on theVImeasurements using the proposed model, extracting the model parameters for each device. Nearly all the extracted model parameters, which were resistances and diode properties such as ideality factor and saturation current, had clear trends that help explain the impact of varying mesa top layer doping levels. The results showed, as discussed in previous literature, that high mesa top layer doping level reduces laser leakage current, but increases cavity loss. It was also found that by using only the fit on electrical characteristics, the model can roughly predict the measured drop in optical efficiency that the BH lasers experience at high input currents. The better understanding of leakage current that comes from this model can be used to further the development of BH lasers. As well, the model and curve fit can be used as a diagnostic tool to aid in the testing of these experimental devices.
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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) | 0.000 | 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) | 0.001 | 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".