Bibliographic record
Abstract
High-end smartphone cameras suffer from large size, high power consumption, and slow performance. These issues are mainly due to the poor performance of the voice coil motors (electromagnetic actuators) used to achieve autofocus (AF) and Optical Image Stabilization (OIS) features in these cameras. Due the superior performance of Micro-Electro-Mechanical-Systems (MEMS) electrostatic actuators over that of other actuation technologies in terms of achieving low power consumption and fast response, micro-electrostatic actuators are being pursued to achieve AF and OIS in smartphone cameras. The maximum mass load displaced by MEMS electrostatic actuators reported in the literature has been limited to 2 mg (corresponds to the mass of a single lens). However, the required mass load to be displaced in order to achieve AF and OIS is in the order of 62 mg mass which represents the mass of a typical lens barrel containing 5 lenses. In this thesis, a novel design of a MEMS piston-tube electrostatic actuator was developed to meet the actuation requirements for AF and OIS in smartphone cameras. The new design overcomes the limitations of the initial design of the piston-tube electrostatic actuator, previously developed by the author. These limitations include the generation of an insufficient out-of-plane translation stroke which is limited to only 24 µm and a low output force also limited to displacing only a 1 mg mass. Two versions of the new design were developed, fabricated, and tested. The latest version was specifically developed to meet the actuation requirements for AF and OIS in smartphone cameras. A new fabrication process, i.e. the MMDL fabrication process, was developed at the university of Toronto cleanrooms to meet the fabrication requirements for this version. The MMDL-fabricated actuator provides for 3 degrees of freedom motion and was able to translate and rotate a 62-mg lens barrel a stroke of 65.5 µm and an angle of rotation of ±0.4°, respectively. The actuator was integrated within a camera module to evaluate how well the actuator meets the requirements of the AF. The actuator achieved autofocus form 15 cm to infinity within 0.5 s, whereas high-end smartphone cameras achieve autofocus within 0.68 s.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".