Simulation of electron beam lithography of nanostructures
Bibliographic record
Abstract
The authors report a numeric simulation tool that they developed for the modeling and analysis of electron beam lithography (EBL) of nanostructures employing a popular positive tone resist polymethylmethacrylate (PMMA). Modeling and process design for EBL fabrication of 5–50 nm PMMA structures on solid substrates is the target purpose of the simulator. The simulator is functional for exposure energies from 1 to 50 keV with arbitrary writing geometries. The authors employ a suite of kinetic models for the traveling of primary, secondary, and backscattered electrons in the resist, compute three-dimensional (3D) distributions of the yield of main-chain scission in PMMA, and convert these into the local volume fractions of fragments of various sizes. The kinetic process of development is described by the movement of the resist-developer interface with the rate derived from the mean-field theory of polymer diffusion. The EBL simulator allows the computation of detailed 3D distributions of the yield of main-chain scission in PMMA for various conditions of exposure, the corresponding volume fractions of small fragments, and the clearance profiles as functions of the development in time and temperature. This article describes the models employed to simulate the EBL exposure and development, reports examples of the computations, and presents comparisons of the predicted development profiles with experimental cross-sectional resist profiles in dense gratings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".