On-chip Ge, InGaAs, and colloidal quantum dot photodetectors: comparisons for application in silicon photonics
Bibliographic record
Abstract
The past twenty years have seen explosive growth in silicon photonics technology. It has revolutionized numerous fields such high-speed optical interconnects in data centers. A photodetector (PD) is one of the key building blocks in silicon photonics, enabling on-chip light detection. Here a comprehensive study has been demonstrated in which three materials, germanium (Ge), indium gallium arsenide (InGaAs), and colloidal quantum dots (CQD), are compared for a PD integrated with a waveguide in silicon photonics. Comparisons are conducted by assuming InGaAs and CQD PDs have the same interface quality as mature Ge PD technology. With this premise, we intend to predict future InGaAs and CQD PD performances. Figures of merit such as dark current, responsivity, and RF bandwidth are compared using simulations. With the premise that epitaxial InGaAs on silicon is as of high quality as epi-Ge, results found that the InGaAs PD is advantageous over the Ge PD with higher-efficiency bandwidth product and lower dark current. CQD PD, on the other hand, is slow but has the lowest dark current, which is suitable for medium-speed applications where ultralow noise is required.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".