Electron beam lithography on nonplanar and irregular surfaces
Bibliographic record
Abstract
E-beam lithography is a powerful tool for generating nanostructures and fabricating nanodevices with fine features approaching a few nanometers in size. However, alternative approaches to conventional spin coating and development processes are required to optimize the lithography procedure on irregular surfaces. In this review, we summarize the state of the art in nanofabrication on irregular substrates using e-beam lithography. To overcome these challenges, unconventional methods have been developed. For instance, polymeric and nonpolymeric materials can be sprayed or evaporated to form uniform layers of electron-sensitive materials on irregular substrates. Moreover, chemical bonds can be applied to help form polymer brushes or self-assembled monolayers on these surfaces. In addition, thermal oxides can serve as resists, as the etching rate in solution changes after e-beam exposure. Furthermore, e-beam lithography tools can be combined with cryostages, evaporation systems, and metal deposition chambers for sample development and lift-off while maintaining low temperatures. Metallic nanopyramids can be fabricated on an AFM tip by utilizing ice as a positive resistor. Additionally, Ti/Au caps can be patterned around a carbon nanotube. Moreover, 3D nanostructures can be formed on irregular surfaces by exposing layers of anisole on organic ice surfaces with a focused e-beam. These advances in e-beam lithography on irregular substrates, including uniform film coating, instrumentation improvement, and new pattern transferring method development, substantially extend its capabilities in the fabrication and application of nanoscale structures.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 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".