SiEPIC openEBL: Remote Silicon Photonic Design-Fabrication-Testing Enabled by Open-Source Process Design Kits and Design Tools
Bibliographic record
Abstract
We present the SiEPIC openEBL platform, an open-access, remote silicon photonic design-fabrication-testing workflow enabled by open-source process design kits (PDKs), electronic design automation (EDA) tools, a design submission and aggregation platform, and an automated electro-optic probe station. Design is supported using the open-source SiEPIC-Tools EDA, a Python-based tool that can operate as a standalone module for experienced designers or integrate with the KLayout layout viewer for ease of use. Additional support is provided for popular EDA tools such as gdsfactory and Luceda IPKISS. Designers utilize the mature SiEPIC EBeam PDK, which offers a variety of hardware-validated devices with accompanying measurement data and compact models, alongside experimental ones. Fabrication is performed using electron beam lithography, enabling rapid prototyping of photonic integrated circuits (PICs) and facilitating the transition from design to experimental validation within short turnaround times. Designs are aggregated through an open-source repository, allowing designers to submit and verify layouts. During the design and aggregation phase, designers can specify measurement sites and test routines for characterization using a PIC probe station. The probe station’s hardware configuration and control software are available in an open-source repository for replication in local facilities. The SiEPIC openEBL platform has been widely adopted, with over 3,000 students and researchers from 70 countries participating in the associated online edX course, Silicon Photonics Design, Fabrication, and Data Analysis. This paper presents an overview of the platform, its impact on silicon photonics education and research, and future directions to expand capabilities and improve accessibility within the silicon photonics ecosystem.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.041 |
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".