Datasets:

ClarusC64
/

euv-stochastic-defect-drift-detection-v0.1

language:
  - en
license: mit
pretty_name: EUV Stochastic Defect Drift Detection v0.1
dataset_name: euv-stochastic-defect-drift-detection-v0.1
tags:
  - clarusc64
  - euv
  - lithography
  - stochastic
  - defects
  - drift
  - resist
  - photon-noise
task_categories:
  - tabular-to-text
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: test
        path: data/test.csv

What this dataset tests

Early stochastic failure is a drift event.

Not a single spike.

The signal is coherence decay across:

source noise
pulse stability
resist response
LER spread
defect rate rise

Task

Predict JSON:

drift_score 0..1
precursor_flag 0 or 1
dominant_axis one of

none
source
resist
coupled

Example

{"drift_score":0.52,"precursor_flag":1,"dominant_axis":"coupled"}

Inputs

All fields are deltas vs a known baseline window.

photon_flux_cv_delta
source_pulse_energy_cv_delta
resist_sensitivity_delta_pct
acid_diffusion_length_delta_nm
blur_length_delta_nm
ler_sigma_delta_nm
defect_rate_ppm_delta
coherence_score_delta

Output meaning

drift_score
How far the system moved from a coherent regime

precursor_flag
Whether defect emergence is imminent

dominant_axis
Where the drift originates first

Version

v0.1