Theoretical modeling of acousto-optic modulated stroboscopic interferometer
Bibliographic record
Abstract
A requirement to advance consistency and predictability of Micro-Electro Mechanical System (MEMS) device has triggered research to have precise measurements and visual means to characterize dynamic parameters. Time resolved measurements of entire surface in a microdevice to nanometer level accuracy are difficult using conventional metrology system such as optical interferometer and optical microscopy. Laser Doppler Vibrometer (LDV) has found their applications to some extent. Due to Single point technique, scanning is a must in LDV which sets drawback for characterization in Microsystems. In this paper we propose the use of Acousto Optic Modulator (AOM) as a strobing device with a continuous wave laser to develop a stroboscopic interferometer for static and dynamic characterization of out of plane motion. Due to high random access time (typical 150 nanoseconds) AOM improves the capability of the tool to test MEMS devices of higher frequencies. Detail study is done on the strobe frequency to correlate the pulsating frequency of the laser by the AOM and the driven frequency of the microdevice. Theoretical modeling of the stroboscopic interferometer is carried out by formulating the understanding between strobe frequency and the MEMS device.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".