Automating Photonic Design with Machine Learning
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 propose and demonstrate the first end-to-end artificial neural network (ANN) modeler for the automated design of photonic systems and devices. This approach gathers an initial range-restricted batch of numerically solved electromagnetic data and maps the nonlinear input-output relationship into a linear model of learned weights. This model is used to predict the output of different device variations for orders-of-magnitude faster optimization or system-level simulations. Our implementation uses the MATLAB numerical computing environment with the finite-difference time-domain electromagnetic solver from Lumerical to acquire the device data, create and train the ANN model, and optimize for a desired device output. In this demonstration, we create a model for a silicon grating coupler, which computes 56,000X faster than the numerical simulation, with an accuracy greater than 97% of the numerical results. Using a parametric sweep or an inverted ANN, the device parameters can be immediately found for a desired output.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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