Integration of Electronics and Planar Waveguide Photonics in the Silicon-on-Insulator Platform
Bibliographic record
Abstract
The overall goal of this research project is to develop new techniques for the monolithic integration of electronic and optical waveguide devices in the SOI platform.We have focused on designing and demonstrating monolithic integrated waveguide photodetectors and transimpedance amplifiers since these components will be vital to most systems.In the absence of a commercial foundry SOI technology offering conventional CMOS electronic devices integrated with photonic components, we have taken two different approaches towards the integration of amplifiers and photodetectors.First, we implemented lateral bipolar junction transistors (LBJTs) and Junction Field Effect Transistors (JFETs) in a widely used commercial foundry SOI photonics technology lacking MOS devices but offering a variety of n-and p-type ion implants intended to provide waveguide modulators and photodetectors.Based on knowledge of device doping and geometry, simple compact LBJT and JFET device models for circuit simulation were developed.These models were then used to design basic transimpedance amplifiers integrated with optical waveguides, which were fabricated along with a suite of test devices.Experimental test results for the completed structures are reported and used to refine the compact device models.The second approach has been to show how low-loss optical waveguides can be integrated in a s imple fully-depleted SOI CMOS technology used for in-house student project fabrication in the Carleton University Microfabrication Facility.In particular experimental and theoretical results are given for loss and photoresponse for Schottky diode photodetectors integrated with these waveguides.A CMOS transimpedance amplifier is also demonstrated in this technology.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".