Bibliographic record
Abstract
A low cost and large aperture scanning mirror with high surface quality is always highly desired. This is especially true for scanning LiDARs for autonomous vehicles. Polygon, galvanometer or MEMS mirrors can have a large aperture, but they are bulky, high power consumption and costly. This dissertation proposes a novel mirror technology for laser scanning, i.e., FPCB (flexible printed circuit board) masked one-step etching large aperture mirror, which simply etches a silicon wafer with a FPCB structure as the etching mask and the other side coated with a metal film as the etching stop layer and reflective surface. Meanwhile the FPCB structure embeds copper coils to generate Lorentz force to oscillate the mirror. The FPCB structure is fabricated using mature and commercially available FPCB process, which is 0.1 mm-resolution based photolithography, instead of 1 μm based photolithography micromachining used in traditional MEMS mirrors. In addition, only one-step DRIE etching is needed without requiring any critical alignment. Such that a low cost can be achieved, e.g., a few dollars per mirror. Moreover, the mirror plate is as thick as the silicon wafer (100~200 μm), which leads to a good flatness (ROC of 10~20 m). Prototypes are fabricated and tested. Achieved performances are: aperture of 12 x 12 mm and 24 x 24 mm, oscillation frequency from 160 Hz ~1 kHz, FOV (field of view) of 30o, 20o and 15o. A 2D and 3D scanning LiDARs based on this novel mirror technology are constructed. This dissertation also develops two electrostatic parallel plate based FPCB micromirrors with performance improved than the previously developed deigns in our group, e.g., FPCB ring-square electrode sandwiched micromirror and double-stage FPCB electrostatic micromirror. The improvements include: the rotation angle increased by 90%, the resonant frequency increased by 40-70%, and the performance variation reduced from 40% to 12%. A large aperture FPCB mirror and a 2D LiDAR based on it are developed with the following performances: aperture 25 x 50 mm, FOV of 60o, measurement distance 50 m, 500 points per frame.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".