Test results of various CMOS image sensor pixels
Bibliographic record
Abstract
Today, CMOS image sensors are increasingly used in consumer products. Digital cameras, Web cameras, optical mouses, and smart sensors are some examples. One of the most used CMOS image sensor architectures is based on the active pixel sensor (APS). In order to understand more design tradeoffs, a prototype chip was designed to evaluate different pixel architectures. This paper describes the prototype chip and the test results. The prototype chip is composed of 8 different pixels and a 3-to-8 row decoder. There are two different APS architectures, 3 transistor or 4 transistor. The 8 different pixels are based on the 2 APS architectures with some variations. These variations are 2 types of reset transistor, N/sub mos/ or P/sub mos/, and 2 types of photodiodes, n/sup +/ - p/sub sub/ and n/sub well/ - p/sub sub/. The prototype chip was manufactured in a standard 3.3 V 0.35 /spl mu/m 1-poly 4-metal CMOS process. The row decoder was designed using a pass transistor network, inverters, and pull-up transistors. The test results show that for white light, n/sup +/ - p/sub sub/ photodiodes and n/sub well/ - p/sub sub/ photodiodes have approximately the same response. Also, 4 transistor pixels have better response, but the voltage range for linear operation is smaller. 3 transistor pixels have larger voltage range for linear operation and less dark current. The results also confirm that the output of the pixels is inversely proportional to the distance of the light source.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".