$1.62\mu \mathrm{m}$ Global Shutter Quantum Dot Image Sensor Optimized for Near and Shortwave Infrared
Bibliographic record
Abstract
We have developed a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.62\mu \mathrm{m}$</tex> pixel pitch global shutter sensor optimized for imaging in the near infrared (NIR) and shortwave infrared (SWIR) regions of the light spectrum. This breakthrough was made possible through the use of our colloidal Quantum Dot (QD) thin film technology, which we have named Quantum Film (QF). We have scaled up this new platform technology to our 300mm manufacturing toolset. The challenges associated with the introduction of solution-processed, colloidally grown lead sulfide (PbS) QDs in an industrial 300mm fab environment were successfully overcome. The QF photodiodes, leveraging either NIR or SWIR sensitive QDs, were optimized for high quantum efficiency (QE), low dark current and immunity to operating stress. Global shutter pixel arrays, with pixel pitch of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.2\mu \mathrm{m}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.62\mu \mathrm{m}$</tex> exhibit unprecedented QE of >50% and MTF @ Nyquist/2 of 0.75 and 0.6, respectively. The robustness of our 300mm Quantum Film technology was fully assessed and reliability in terms of meeting all required lifetime specifications for consumer electronics and other potential applications has been demonstrated.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".