Consumer electronic optics: how small can a lens be: the case of panomorph lenses
Bibliographic record
Abstract
In 2014, miniature camera modules are applied to a variety of applications such as webcam, mobile phone, automotive, endoscope, tablets, portable computers and many other products. Mobile phone cameras are probably one of the most challenging parts due to the need for smaller and smaller total track length (TTL) and optimized embedded image processing algorithms. As the technology is developing, higher resolution and higher image quality, new capabilities are required to fulfil the market needs. Consequently, the lens system becomes more complex and requires more optical elements and/or new optical elements. What is the limit? How small an injection molded lens can be? We will discuss those questions by comparing two wide angle lenses for consumer electronic market. The first lens is a 6.56 mm (TTL) panoramic (180° FOV) lens built in 2012. The second is a more recent (2014) panoramic lens (180° FOV) with a TTL of 3.80 mm for mobile phone camera. Both optics are panomorph lenses used with megapixel sensors. Between 2012 and 2014, the development in design and plastic injection molding allowed a reduction of the TTL by more than 40%. This TTL reduction has been achieved by pushing the lens design to the extreme (edge/central air and material thicknesses as well as lens shape). This was also possible due to a better control of the injection molding process and material (low birefringence, haze and thermal stability). These aspects will be presented and discussed. During the next few years, we don’t know if new material will come or new process but we will still need innovative people and industries to push again the limits.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".