Nano-scale measurement of sub-micrometer MEMS in-plane dynamics using synchronized illumination
Bibliographic record
Abstract
A method for measuring the sub-micrometer in-plane dynamics of MEMS devices with nano-scale precision using a CCD camera and synchronized pulsating illumination is presented. Typical MEMS actuators have fast responses (generally in the 1–200 kHz range), much faster than typical cameras which record a time averaged motion. Under constant illumination the average displacement is steady state and independent of dynamic amplitude or phase. Methods such as strobe illumination use short light pulses to freeze the motion. This paper develops the use of longer pulses of illumination that do not freeze the image, but make the average displacement depend on dynamic amplitude and phase; thus allowing both properties to be extracted. The expected signal is derived as a function of light pulse width and delay, and short versus longer pulses are compared. Measurements using a conventional microscope with replacement of the lamp with LEDs confirmed the derived equations. The system was used to measure sub-micrometer motion of MEMS actuators with ~5 nm precision. The time constant of a thermal actuator was measured and found to be 48 µ s. A resonant peak of a MEMS device was measured at 123.30 kHz with an amplitude of 238 nm.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".