An Open-Source Artificial Neural Network Model for Polarization-Insensitive Silicon-on-Insulator Subwavelength Grating Couplers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present an open-source deep artificial neural network (ANN) model for the accelerated design of polarization-insensitive subwavelength grating (SWG) couplers on the silicon-on-insulator platform. Our model can optimize SWG-based grating couplers for a single fundamental-order polarization, or both, by splitting them counter-directionally at the grating level. Alternating, SWG sections are adopted to reduce the reflections (loss) of standard, single-etch devices-further accelerating the design time by eliminating the need to process a second etch. The model of this device is trained by a dense uniform dataset of finite-difference time-domain (FDTD) optical simulations. Our approach requires the FDTD simulations to be made up front, where the resulting ANN model is made openly available for the rapid, software-free design of future standard photonic devices, which may require slightly different design parameters (e.g., fiber angle, center wavelength, and polarization) for their specific application. By transforming the nonlinear input-output relationship of the device into a matrix of learned weights, a set of simple linear algebraic and nonlinear activation calculations can be made to predict the device outputs 1830 times faster than numerical simulations, within 93.2% accuracy of the simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it