Z-pinch Dynamics in a Xenon Gas Jet Type 13.5 nm Extreme Ultraviolet Plasma Source
Bibliographic record
Abstract
Extreme Ultraviolet (EUV) lithography is the leading candidate of the next generation lithography technology.To date, insufficient source power remains a critical issue for the High Volume Manufacturing (HVM) of EUV lithography.Z-pinch is an efficient method for producing the 13.5 nm EUV radiation.However, it is inherently susceptible to Magneto-Rayleigh-Taylor (MRT) instabilities, which causes the non-uniformity of the EUV radiation and degrades the output.In this thesis, both experimental and numerical investigations on the Z-pinch dynamics of an gas jet type Xenon ( Xe) Discharge Produced Plasma (DPP) EUV source are presented.The EUV radiation characteristics, time-resolved visible plasma imaging, time-integrated EUV pinhole imaging, electron density evolution, and ion kinetics in decay phase are studied experimentally.The EUV radiation fluctuation caused by MRT instability with a wavelength 1 mm is observed by EUV pinhole imaging.To investigate the Z-pinch dynamics and the MRT instabilities in the DPP EUV source, a Magneto-Hydrodynamics (MHD) code is developed.Important plasma parameters for Z-pinch (e.g.electron density and electron temperature) are simulated.The evolutions of MRT instabilities with single mode, multi-mode and random mode initial perturbations are presented.The simulation shows that MRT instabilities tend to converge to a mm-scale wavelength around 1 mm, in consistence with experimental result.The MRT instabilities will cause the electron density and temperature fluctuations along Z axis at pinch stagnation, which finally lead to the non-uniformity of the EUV radiation.Preferred initial conditions and possible MRT mitigation methods are also proposed to optimize the EUV source.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".