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Specifications and pre-production of n<sup>+</sup>-in-p large-format strip sensors fabricated in 6-inch silicon wafers, ATLAS18, for the Inner Tracker of the ATLAS Detector for High-Luminosity Large Hadron Collider

2023· article· en· W4353060976 on OpenAlexaff
Y. Unno, S. H. Abidi, A. A. Affolder, K. Affolder, P. P. Allport, S. Beaupre, H. P. Beck, J. Bernabéu, A. J. Bevan, A. S. Chisholm, B. M. Ciungu, I. Dawson, A. Dowling, V. Fadeyev, P. Federičová, J. Fernández-Tejero, C. Fleta, A. Fournier, W. F. George, M. Gignac, L. Gonella, G. Greig, J. Gunnell, K. Hara, S. Hirose, Bart Hommels, Tatsuya Ishii, C. E. Jessiman, J. Johnson, D. M. Jones, S. Kachiguin, N. J. Kang, J. Keller, C. Klein, T. Koffas, I. Kopsalis, J. Kroll, J. Kvasnička, C. Lacasta, V. Latoňová, J. D. Lomas, F. Martinez-Mckinney, M. Mikeštíková, P. S. Miyagawa, R. S. Orr, L. Poley, D. Rousso, Aashaq Shah, C. Solaz, E. J. Staats, T.L. Stack, B. Stelzer, M. Ullán, J. Yarwick, S. Zenz

Bibliographic record

VenueJournal of Instrumentation · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsSimon Fraser UniversityCarleton UniversityTRIUMFUniversity of Toronto
FundersAgencia Estatal de InvestigaciónScience and Technology Facilities CouncilCERNUniverzita Karlova v PrazeU.S. Department of Energy
KeywordsSTRIPSWaferBarrel (horology)GeometryDetectorPhysicsMaterials scienceSiliconOpticsOptoelectronicsComposite materialMathematics

Abstract

fetched live from OpenAlex

Abstract The ATLAS experiment is constructing new all-silicon inner tracking system for HL-LHC. The strip detectors cover the radial extent of 40 to 100 cm. A new approach is adopted to use p-type silicon material, making the readout in n+-strips, so-called n+-in-p sensors. This allows for enhanced radiation tolerance against an order of magnitude higher particle fluence compared to the LHC. To cope with varying hit rates and occupancies as a function of radial distance, there are two barrel sensor types, the short strips (SS) for the inner 2 and the long strips (LS) for the outer 2 barrel cylinders, respectively. The barrel sensors exhibit a square, 9.8 × 9.8 cm2, geometry, the largest possible sensor area from a 6-inch wafer. The strips are laid out in parallel with a strip pitch of 75.5 μm and 4 or 2 rows of strip segments. The strips are AC-coupled and biased via polysilicon resistors. The endcap sensors employ a “stereo-annulus” geometry exhibiting a skewed-trapezoid shapes with circular edges. They are designed in 6 unique shapes, R0 to R5, corresponding to progressively increasing radial extents and which allows them to fit within the petal geometry and the 6-inch wafer maximally. The strips are in fan-out geometry with an in-built rotation angle, with a mean pitch of approximately 75 μm and 4 or 2 rows of strip segments. The eight sensor types are labeled as ATLAS18xx where xx stands for SS, LS, and R0 to R5. According to the mechanical and electrical specifications, CAD files for wafer processing were laid out, following the successful designs of prototype barrel and endcap sensors, together with a number of optimizations. A pre-production was carried out prior to the full production of the wafers. The quality of the sensors is reviewed and judged excellent through the test results carried out by vendor. These sensors are used for establishing acceptance procedures and to evaluate their performance in the ATLAS collaboration, and subsequently for pre-production of strip modules and stave and petal structures.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.003

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.

Opus teacher head0.023
GPT teacher head0.260
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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".

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Citations37
Published2023
Admission routes1
Has abstractyes

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