Control of temporal and spatial proximity effects in two-photon lithography
Bibliographic record
Abstract
Two-photon lithography (TPL) enables the fabrication of high-resolution three-dimensional (3D) nanostructures, but its precision is often limited by proximity effects, leading to feature broadening and filamentary defects, both of which can depend on the timing between adjacent feature writing. This work experimentally explores the influence of process parameters, resist chemistry, and deposition techniques on temporal and filamentary proximity effects in TPL, in both lateral and vertical dimensions. We demonstrate that resist composition plays a crucial role in resolution, with TMPTA-based resists achieving superior fidelity. Exposure conditions, including outline and infill power, significantly affect temporal proximity effects as well. Simulations demonstrate that a competition of oxygen and initiator diffusion gives rise to complex interactions as their relative influence changes with spacing, timing, and laser power. Spin-coating is seen to improve uniformity but exacerbates proximity effects due to altered diffusion dynamics. Design modifications, such as strategic erosion of printed features and optimized scan paths, can further improve feature resolution. Notably, here we achieve hole sizes below 200 nm and gap separations under 150 nm in single-beam TPL without secondary processing–a resolution that, to our knowledge, surpasses prior reports for direct-written structures in infrared TPL without post-processing. These findings provide a pathway for fabricating high-density 3D nanostructures with improved fidelity, advancing next-generation photonic, biomedical, and metamaterial applications.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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".