Multivariable Optimal Learning Control of Wafer Temperatures in a Commercial RTP Equipment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A multivariable optimal iterative learning control technique called BMPC (Batch Model Predictive Control) has been implemented and evaluated in a commercial RTP (Rapid Thermal Processing) system fabricating 200 mm silicon wafers. The wafer temperature was controlled at multiple points along the radial direction by manipulating multiple tungsten-halogen lamp groups. The study has addressed the following two issues: feasibility of BMPC in a commercial RTP equipment and enhancement of temperature uniformity using redundant inputs. As a consequence, satisfactory tracking performance could be realized with BMPC with reduced efforts for design and implementation of the controller by the standardized identification and tuning procedure. Redundant inputs whose number is larger than that of the temperature measurements was attempted to relieve the directionality of the system. Experimental tests revealed that the approach can provide us with improved temperature uniformity as well as tracking performance. Une technique de contrôle d'apprentissage itérative optimale multivariable, appelée BMPC (contrôle prédictif de modèles discontinus) a été appliquée et évaluée dans un système RTP (traitement thermique rapide) commercial de fabrication de galettes de silicone de 200 mm. La température de galette est contrôlée en de multiples points dans la direction radiale en manipulant des groupes de lampes de tungstène-halogène. L'étude porte sur deux aspects : la faisabilité du BMPC dans un équipement RTP commercial et l'amélioration de l'uniformité de la température à l'aide d'entrées redondantes. De cette façon, on a pu réaliser une performance de traçage satisfaisante avec le BMPC, avec des efforts réduits pour la conception et l'implantation du contrôleur par la procédure d'identification et de réglage standardisée. Des entrées redondantes dont le nombre est plus grand que le nombre de mesures de température ont été essayées pour amoindrir la directionnalité du système. Les tests expérimentaux montrent que cette méthode peut fournir une meilleure uniformité de température et performance de traçage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it