Characterization of the surface contamination of deep X-ray lithography mirrors exposed to synchrotron radiation
Bibliographic record
Abstract
In deep X-ray lithography (DXRL), synchrotron radiation is applied to pattern polymer microstructures. At the Synchrotron Laboratory for Micro and Nano Devices (SyLMAND), Canadian Light Source, a chromium-coated grazing-incidence X-ray double-mirror system is applied as a tunable low-pass filter. In a systematic study, the surface conditions of the two mirrors are analyzed to determine the mirror reflectivity for DXRL process optimization, without the need for spectral analysis or surface probing: PMMA resist foils were homogeneously exposed and developed to determine development rates for mirror angles between 6 mrad and 12 mrad as well as for white light in the absence of the mirrors. Development rates cover almost five orders of magnitude for nominal exposure dose (deposited energy per volume) values of 1 kJ cm −3 to 6 kJ cm −3 . The rates vary from case to case, indicating that the actual mirror reflectivity deviates from that of clean chromium assumed for the experiments. Fitting the mirror-based development rates to the white-light case as a reference, reflectivity correction factors are identified, and verified by experimental and numerical results of beam calorimetry. The correction factors are related to possible combinations of a varied chromium density, chromium oxidation and a carbon contamination layer. The best fit for all angles is obtained assuming 7.19 g cm −3 nominal chromium density, 0.5 nm roughness for all involved layers, and an oxide layer thickness of 25 nm with a carbon top coat of 50 nm to 100 nm. A simulation tool for DXRL exposure parameters was developed to verify that the development rates for all cases do coincide within a small error margin (achieving a reduction of the observed errors by more than two orders of magnitude) if the identified mirror surface conditions are considered when calculating the exposure dose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".