Topological inverse design of nanophotonic devices with energy constraint
Bibliographic record
Abstract
In this paper, we introduce an energy constraint to improve topology-based inverse design. Current methods typically place the constraints solely on the device geometry and require many optimization iterations to converge to a manufacturable solution. In our approach the energy constraint directs the optimization process to solutions that best contain the optical field inside the waveguide core medium, leading to more robust designs with relatively larger minimum feature size. To validate our method, we optimize two components: a mode converter (MC) and a wavelength demultiplexer. In the MC, the energy constraint leads to nearly binarized structures without applying independent binarization stage. In the demultiplexer, it also reduces the appearance of small features. Furthermore, the proposed constraint improves the robustness to fabrication imperfections as shown in demultiplexer design. With energy constraint optimization, the corresponding spectrum shifts under ±10 nm dimensional variations are reduced by 17% to 30%. The proposed constraint is unique in simultaneously taking both geometry and electric field into account, opening the door to new ideas and insights to further improve the computationally intensive topology-based optimization process of nanophotonic devices.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".