Freeform thin‐film lithium niobate mode converter for photon‐pair generation
Bibliographic record
Abstract
Thin-film lithium niobate (TFLN) has emerged as a promising platform for integrated photonics due to its exceptional material properties. The application of freeform topology optimization to TFLN devices enables the realization of compact designs with complex functionalities and high efficiency. However, the stringent fabrication constraints of TFLN present significant challenges for optimization, particularly in nonlinear photonic devices. In this work, we propose an inverse design methodology that successfully addresses these challenges and demonstrates the development of an efficient freeform TFLN mode converter. The numerically optimized mode converter achieves a transmission efficiency of 67.60 % and a mode purity of 84.58 %. Experimental validation through nonlinear processes, including second harmonic generation and spontaneous parametric down-conversion, shows that the fabricated devices improve the efficiency of these processes by factors of two and three, respectively, compared to devices without freeform designs. The proposed inverse design framework provides a powerful tool for advancing the development of TFLN-based devices, with broad applicability to nonlinear and quantum photonics.
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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 | 0.000 |
| Open science | 0.000 | 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".