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Record W4400219447 · doi:10.1515/nanoph-2024-0269

Inverse design of color routers in CMOS image sensors: toward minimizing interpixel crosstalk

2024· article· en· W4400219447 on OpenAlexaff
Sangbin Lee, Jaehyun Hong, J.S. Kang, Junjeong Park, Jae-Sung Lim, Tae‐Ho Lee, Min Seok Jang, Haejun Chung

Bibliographic record

VenueNanophotonics · 2024
Typearticle
Languageen
FieldEngineering
TopicCCD and CMOS Imaging Sensors
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersInstitute for Information and Communications Technology PromotionKorea Semiconductor Research ConsortiumMinistry of Culture, Sports and TourismMinistry of Science and ICT, South KoreaMinistry of Trade, Industry and Energy
KeywordsPixelMiniaturizationComputer scienceRouterCrosstalkArtificial intelligenceOptoelectronicsOpticsMaterials sciencePhysicsNanotechnologyComputer network

Abstract

fetched live from OpenAlex

Over the past decade, significant advancements in high-resolution imaging technology have been driven by the miniaturization of pixels within image sensors. However, this reduction in pixel size to submicrometer dimensions has led to decreased efficiency in color filters and microlens arrays. The development of color routers that operate at visible wavelengths presents a promising avenue for further miniaturization. Despite this, existing color routers often encounter severe interpixel crosstalk, around 70 %, due to the reliance on periodic boundary conditions. Here, we present interpixel crosstalk-minimized color routers that achieve an unprecedented in-pixel optical efficiency of 87.2 % and significantly reduce interpixel crosstalk to 2.6 %. The color routers are designed through adjoint optimization, incorporating customized incident waves to minimize interpixel crosstalks. Our findings suggest that our color router design surpasses existing color routing techniques in terms of in-pixel optical efficiency, representing a crucial step forward in the push toward commercializing the next generation of solid-state image sensors.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.236
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations20
Published2024
Admission routes1
Has abstractyes

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