Data-Driven Fault Detection for Wafer Scanner Cable Slabs using Koopman Operators
Bibliographic record
Abstract
The reliability of precision motion systems, such as semiconductor wafer scanners, is often influenced by nonlinear dynamics originating from components such as cable slabs. This paper introduces a data-driven framework for early fault diagnosis in these systems. Koopman operator theory is employed to derive a linear state-space model from experimental data, capturing the complex, hysteretic behavior of the cable slab. This model serves as a digital twin, and by comparing its predictions with real-time sensor measurements, operational anomalies can be detected. A systematic process for selecting observable functions yields a high-fidelity model with a tracking error of approximately ±1% across the operational range. When the proposed approach is tested against a state-of-the-art neural network model, it demonstrates a 75.4% reduction in reaction force prediction error. The framework successfully identifies an injected sensor noise fault (SNR of 20) in just 0.35 s using only force data, validating its potential to improve wafer scanner reliability.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".