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Record W4401124880 · doi:10.1002/sdtp.17632

54‐2: Diffraction Issues of Under Display IR Sensor in AMOLED Displays

2024· article· en· W4401124880 on OpenAlexaff
Zhibin Wang, Longxing Chi, Yi‐Lu Chang, Xiaofeng Xu, Jacky Qiu, Michael G. Helander

Bibliographic record

VenueSID Symposium Digest of Technical Papers · 2024
Typearticle
Languageen
FieldEngineering
TopicSurface Roughness and Optical Measurements
Canadian institutionsOTI Lumionics (Canada)
Fundersnot available
KeywordsAMOLEDMaterials scienceDiffractionOptoelectronicsComputer graphics (images)Computer scienceOpticsThin-film transistorNanotechnologyActive matrixPhysicsLayer (electronics)

Abstract

fetched live from OpenAlex

This paper investigates diffraction issues in Under Display Infrared (UDIR) sensor applications for 3D facial recognition. While conventional design wisdom emphasizes infrared transmittance, this study introduces a new figure of merit, Zero‐Order Transmittance (ZOT) as a crucial parameter. Patterning the cathode significantly improves ZOT, highlighting its importance in UDIR applications. This emphasizes the need for careful consideration of pixel and aperture layout to achieve high ETZ without compromising performance such as resolution and lifetime of the display.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.249
Teacher spread0.238 · 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
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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueSID Symposium Digest of Technical PapersSame topicSurface Roughness and Optical MeasurementsFrench-language works237,207