Conceptual design of inductive magnetic sensors using photolithography processes
Bibliographic record
Abstract
Inductive magnetic sensors are needed for the operation of fusion devices to monitor high frequency (HF) fluctuations and as a back-up to the low-frequency (LF, still inductive) magnetic sensors used for the measurements leading to the reconstruction of the equilibrium. The technical specifications for these two types of inductive magnetic sensors are rather different: this is a major conceptual difficulty in the design of inductive magnetic sensors, and then most often different sets of inductive magnetic sensors are used, which significantly complicates R&D activities, prototyping and manufacturing. Most (∼500) of the inductive magnetic sensors currently being deployed in ITER have been produced with the Low-Temperature CoFired Ceramic (LTCC) technology. The LTCC technology is now at least 20 years old and new processes have been developed for industrial applications, essentially based on different photolithography (PL) processes. The main advantage of the PL processes is that a much smaller track width can be achieved: a smaller dd1 allows to “pack” more planar winding loops (=m) enclosing a larger area over a smaller geometrical surface, thus keeping the same overall effective are NA EFF ∝m while significantly reducing the sensors’ self-inductance L SELF ∝m 2 . Then, with PL techniques the same design could in principle be used for both high-frequency and low-frequency applications, the difference simply being the number of stacked-up layers (= n ) used to make-up the entire sensor. Continuing from our earlier work, in this paper we will present recent advances in our processes for producing inductive magnetic sensors using PL methods, most notably on the use of synthetic Sapphire wafers, on increasing the track thickness, on developing multi-layers sensors, on producing miniaturized saddle loops, and finally on developing packaging solutions for installing these inductive magnetic sensors in-vessel and ex-vessel.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".