Fast Free-Form Phase Mask Design for Three-Dimensional Photolithography Using Convergent Born Series
Bibliographic record
Abstract
Advancements in three-dimensional (3D) photolithography are crucial for enhancing the performance of devices in applications ranging from energy storage and sensors to microrobotics. Proximity-field nanopatterning (PnP), which utilizes light-shaping phase masks, has emerged as a promising method to boost productivity. This study presents a swift and effective strategy for the design of phase masks tailored to the PnP process. Conventional design methodologies, grounded in basic optical theories, have been constrained by the simplicity and limited contrast of the resulting nanopatterns. Our approach, which merges the use of a frequency-domain electromagnetic solver─termed the convergent Born series─with gradient-based optimization and GPU acceleration, successfully addresses these shortcomings. The proposed solver outperforms CPU-intensive commercial FDTD software in our 2D test case by approximately 30 times, and its computational advantage increases in 3D simulations. This approach facilitates the creation of complex, high-contrast nanostructures within practical timeframes. We validate our method’s effectiveness by engineering phase masks to produce distinct hologram patterns, such as single and double helices, thereby underscoring its utility for pioneering nanophotonic devices. Our findings propel the PnP process forward, ushering in novel avenues for the creation of sophisticated 3D nanostructures with superior optical and mechanical features.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".