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Performance and design validation of CMS Phase-2 pixel modules

2025· article· W7128215541 on OpenAlexaff

Bibliographic record

VenueJournal of Instrumentation · 2025
Typearticle
Language
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsChipDetectorProcess (computing)PixelTracking (education)Quality (philosophy)Sample (material)Process validation

Abstract

fetched live from OpenAlex

Abstract In view of the High Luminosity LHC, the current CMS tracking detector will have to be replaced during Long Shutdown 3 to cope with the higher radiation environment and to withstand an increased data rate. To prepare for the so-called CMS Phase-2 upgrade, multiple studies were carried out to characterize the pixel module design and its performance with a particular focus on the Quality Control (QC) and Assurance. For this purpose, different aspects were put together to establish a module full-performance test procedure, and novel techniques became part of the module design validation process for the full-size readout chip (CROCv1). Based on the results collected on CROCv1 prototype modules and according to the module selection criteria the community agreed on, some changes were introduced in the module design to improve the performance. This resulted in multiple prototype versions, including the production of the definitive chip (CROCv2). This study presents the quality control test flow performed, both for the dual and quad-chip module designs, on a big sample of CROCv1 prototypes and on several Kick-off and CROCv2 pre-production modules. In particular, the validation process includes measurements of the readout chip powering, sensor IV bias and open bump bonds identification. Thermal stress tests in extended temperature ranges were performed only on a subset of pixel modules to ensure the integrity of the sensor and to provide quick feedback on the quality of the bump bond connectivity after harsh temperature cycles.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.285
Teacher spread0.264 · 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 teacher head, not a consensus.

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

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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