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Record W3133603596 · doi:10.1016/j.isci.2021.102268

Nanophotonic color splitters for high-efficiency imaging

2021· article· en· W3133603596 on OpenAlex
Eric Johlin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueiScience · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanophotonicsNanotechnologyComputer scienceMaterials science

Abstract

fetched live from OpenAlex

Standard color imaging utilizes absorptive filter arrays to achieve spectral sensitivity. However, this leads to ∼2/3 of incident light being lost to filter absorption. Instead, splitting and redirecting light into spatially separated pixels avoids these absorptive losses. Herein we investigate the inverse design and performance of a new type of splitter which can be printed from a single material directly on top of a sensor surface and are compatible with 800 nm sensor pixels, thereby providing drop-in replacements for color filters. Two-dimensional structures with as few as four layers significantly improve fully color-corrected imaging performance over standard filters, with lower complexity. Being fully dielectric, these splitters additionally allow color-correction to be foregone, increasing the photon transmission efficiency to over 80%, even for sensors with fill-factors of 0.5. Performance further increases with fully 3D structures, improving light sensitivity in color-corrected imaging by a factor of 4 when compared to filters alone.

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.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.008
GPT teacher head0.257
Teacher spread0.249 · 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