Heat Load Experiments at CAMD and CLS
Bibliographic record
Abstract
Joint research described in this paper includes partners from the Canadian Light Source and LSU (CAMD, Chemical Engineering) and is built on previous work conducted by researchers at CAMD and BESSY [1]. Heat load and its impact on the patterning accuracy in deep X-ray lithography has been studied for many years and reported in a number of publications [2,3,4,5,6,7,8,9,10,11]. Investigations combine simulation and experimental results and show that thermal load can cause temperature increases ranging from a few degrees to several 10°C depending upon light source and exposure parameters. Building upon previous experiments [1] and further improvements systematic studies with a ‘worst-case scenario ’ x-ray mask (100 % Au coverage resulting in maximum heat load on the mask) and in-situ monitoring of temperature changes on mask and substrate during exposures were conducted. Simulation results achieved with a lumped model representing the arrangement of mask and substrate inside the scanner chamber are in good agreement and complement our experiments. Introduction and Background One of the major steps in ultra-deep x-ray lithography is exposure of thick photoresists using high power synchrotron radiation such as the CAMD wiggler beamline or the SyLMAND bending
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".