Design parameters of a reluctance actuation system for stable operation conditions with applications of high‐precision motions in lithography machines
Bibliographic record
Abstract
Abstract Recently, the reluctance actuator has attracted great attention to replace the Lorentz actuator in the next generation of wafer scanners in semi‐conductor lithography machines. The reluctance actuator has a non‐linear position‐force characteristic, which may cause high oscillations and unstable operation. This study presents a linearisation technique by optimising the main parameters of the reluctance actuator to operate away from the saturation point. Also, in this study, the critical current is formulated for the stable dynamic behaviour of the reluctance actuator. Optimisation based on the Grey Wolf Optimiser is performed considering the high‐precision motion requirements and physical constraints. The high‐precision motion requirements include the desired work‐space displacement, natural frequency, and maximum force. The electromechanical dynamic model of the reluctance actuator motion system is formulated to characterise the interaction among electrical, magnetic, and mechanical parts. The simulation results show that the optimal design of the reluctance actuator works in the linear region, and for stable dynamic behaviour, the input current is limited by the critical current. Finally, a feedforward controller is designed based on the approximation of the force–current relationship to improve the tracking performances of the reluctance actuator motion system. The simulation results in time and frequency domains show an improvement in tracking performances using the feedforward controller.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".