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Record W4297222909 · doi:10.3390/photonics9100690

Optimization of Multi-Layer Data Recording and Reading in an Optical Disc

2022· article· en· W4297222909 on OpenAlexaff
Ashish Jain, M. G. Rahimian, V. R. Bhardwaj

Bibliographic record

VenuePhotonics · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPhase-change materials and chalcogenides
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsOpticsLaserOptical discMaterials scienceLayer (electronics)Optical recordingScatteringReading (process)Light scatteringDiffraction3D optical data storagePolycarbonateComputer scienceOptoelectronicsPhysicsNanotechnologyComposite material

Abstract

fetched live from OpenAlex

Storage capacity of a conventional optical disc can be enhanced significantly by recording data within the three-dimensional volume. However, spherical aberrations and light-scattering limit the number of layers that can be efficiently recorded and retrieved. In this paper, we show that by optimizing the multi-layer data recording and reading parameters, the number of fabricated layers could be tripled to 60 in commercial grade poly-methyl-methacrylate polycarbonate and reduce the read laser power by a factor of 7 by the 20th layer. Influence of spherical aberration on write laser spot size was studied by using vector diffraction theory and compared with experimental values obtained by monitoring the threshold energy of the write laser with depth. Scattering losses of the read laser were studied by simulating the propagation of light through multi-layered micro-structured material. The main advantage of our technique is its practical implementation, as it refrains from the use of index-matching liquids and specialized optics during the read–write process.

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 score0.696
Threshold uncertainty score0.495

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 communication0.0000.000
Open science0.0000.001
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 head0.126
GPT teacher head0.336
Teacher spread0.210 · 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
Published2022
Admission routes1
Has abstractyes

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