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 (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VI</i>) 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 simple <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VI</i> measurements. In this experiment, 72 BH lasers with three different mesa top layer doping levels were measured. Then, a curve fit was performed on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VI</i> measurements 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".