New CMOS digital pixel sensor architecture dedicated to a visual cortical implant
Bibliographic record
Abstract
A CMOS image sensor with pixel level analog to digital conversion is presented. Each 16µm x 16µm pixel area contains a photodiode, with a fill factor of 22%, a comparator and an 8-bit DRAM, resulting in a total of 44 transistors per pixel. A digital to analog converter is used to deliver a voltage reference to compare with the pixel voltage for the analog to digital conversion. This sensor is required by a visual cortical stimulator, primarily to capture the image which is dedicated to stimulate the visual cortex of a blind patient. An active range finder system will be added to the implant, requiring the difference information between two images, in order to obtain the 3D information useful to the patient. For this purpose, three selectable operation modes are combined in the same pixel circuit. The linear integration, resulting from image capture at multiple exposure times, allows a high intrascene dynamic range. Random accessibility, in space and time, of the array of sensors is possible with the logarithmic mode. And the new differential mode makes the difference between two consecutive images. The circuit of a pixel has been fabricated in CMOS 0.18µm technology and it is under test to validate the full operation of the 3 modes. Also, a matrix of 45 x 90 pixels is currently being implemented for fabrication.
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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 | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".