| --- |
| language: |
| - en |
| license: mit |
| pretty_name: EUV Illumination Pupil Coherence Drift Detection v0.1 |
| dataset_name: euv-illumination-pupil-coherence-drift-detection-v0.1 |
| tags: |
| - clarusc64 |
| - euv |
| - lithography |
| - optics |
| - drift-detection |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train.csv |
| - split: test |
| path: data/test.csv |
| --- |
| |
| ## Purpose |
|
|
| Detect when illumination-pupil coupling begins to drift toward loss of critical dimension control. |
|
|
| This dataset maps coherence decay between: |
|
|
| pupil geometry |
| dose distribution |
| printed wafer metrics |
|
|
| The goal is to identify drift before feature collapse. |
|
|
| ## Task |
|
|
| Input system metrics. |
|
|
| Predict: |
|
|
| drift_score |
| drift_flag |
|
|
| Format: |
| float,int |
|
|
| Example: |
| 0.42,1 |
|
|
| ## Why this matters |
|
|
| Lithography systems rarely fail instantly. |
| They drift. |
|
|
| Early detection prevents yield loss and tool downtime. |
|
|
| ## Evaluation |
|
|
| Absolute error on drift_score |
| Classification bonus for drift_flag |
|
|
| Format validity scored when ground truth absent. |
|
|
| ## Version |
|
|
| v0.1 |
|
|
