High-speed complex field modulation using binary phase-engraved superpixels
Bibliographic record
Abstract
Complex field modulation (CFM) has found a plethora of applications in physics, biomedicine, and instrumentation. Among existing methods, superpixel-based CFM has been increasingly featured because of its advantages in high modulation accuracy and its compatibility with high-speed spatial light modulators (SLMs). Nonetheless, the mainstream approach based on binary-amplitude modulation confronts limitations in optical efficiency and dynamic range. To surmount these challenges, we develop binary phase-engraved (BiPE) superpixel-based CFM and implement it using the phase light modulator (PLM)—a new micro-electromechanical system-based SLM undergoing development by Texas Instruments in recent years. Using BiPE superpixels, we demonstrate high-accuracy spatial amplitude and phase modulation at up to 1.44 kHz. To showcase its broad utility, we apply BiPE-superpixel-based CFM to beam shaping, high-speed projection, and augmented-reality display.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".