6‐2: Holographic Display Enabled with Light Modulation in both Amplitude and Phase in A Single LCoS‐Based Spatial Light Modulator
Bibliographic record
Abstract
Holographic display enabled by performing both amplitude modulation (AM) and phase modulation (PM) in a single Liquid Crystal on Silicon (LCoS)‐based Spatial Light Modulator (SLM) is introduced. Light propagation is manipulated in two different directions to perform both AM and PM via liquid crystals alignment layer which can be based on technologies by using photoalignment, nano‐imprint lithography (NIL), or unique over‐driving across Liquid Crystal (LC) layer of Vam‐Vpm method which are described and proved to be effective to tune incident light, control the polarization and propagation of the light entering the liquid crystals of a single LCoS‐based SLM (LCoS‐SLM) device. These methods enable holographic display and to alleviate the issues in using two separated SLMs trying to achieve the same. Further, the design techniques and methods to suppress adjacent pixels’ crosstalk due to fringe field effect (FFE) [1] in 2D array are amenable to ensure higher resolution with pixel pitch down to 1¼m for compact size and higher pixel numbers beyond 8K×4K. It mitigates to reduce chores of pixel‐to‐pixel alignment, and to enhance image clarity and quality within a single LCoS‐SLM. It will be applicable to enable holographic display with such unique LCoS‐SLM particularly for AR/VR/MR smart glasses applications [18] [19] [20] once being fabricated in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".