Direct nanopatterning of complex 3D surfaces and self-aligned superlattices via molecular-beam holographic lithography
Bibliographic record
Abstract
Conventional lithography methods involving pattern transfer through resist templating face challenges of material compatibility with various process solvents. Other approaches of direct material writing often compromise pattern complexity and overlay accuracy. Here we explore a concept based on the Moiré interference of molecular beams to directly pattern complex three-dimensional (3D) surfaces made by any evaporable materials, such as metals, oxides and organic semiconductors. Our proposed approach, termed the molecular-beam holographic lithography (MBHL), relies on precise control over angular projections of material flux passing through nanoapertures superimposed on the substrate, emulating the interference of coherent laser beams in interference lithography. Incorporating with our computational lithography (CL) algorithm, we have demonstrated self-aligned overlay of multiple material patterns to yield binary up to quinary superlattices, with a critical dimension and overlay accuracy on the order of 50 and 2 nm, respectively. The process is expected to substantially expand the boundary of materials combination for high-throughput fabrication of complex superstructures of translational symmetry on arbitrary substrates, enabling emerging nanoimaging, sensing, catalysis, and optoelectronic 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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".