Low-Voltage 96 dB Snapshot CMOS Image Sensor with 4.5 nW Power Dissipation per Pixel
Bibliographic record
Abstract
Modern "smart" CMOS sensors have penetrated into various applications, such as surveillance systems, bio-medical applications, digital cameras, cellular phones and many others. Reducing the power of these sensors continuously challenges designers. In this paper, a low power global shutter CMOS image sensor with Wide Dynamic Range (WDR) ability is presented. This sensor features several power reduction techniques, including a dual voltage supply, a selective power down, transistors with different threshold voltages, a non-rationed logic, and a low voltage static memory. A combination of all these approaches has enabled the design of the low voltage "smart" image sensor, which is capable of reaching a remarkable dynamic range, while consuming very low power. The proposed power-saving solutions have allowed the maintenance of the standard architecture of the sensor, reducing both the time and the cost of the design. In order to maintain the image quality, a relation between the sensor performance and power has been analyzed and a mathematical model, describing the sensor Signal to Noise Ratio (SNR) and Dynamic Range (DR) as a function of the power supplies, is proposed. The described sensor was implemented in a 0.18 um CMOS process and successfully tested in the laboratory. An SNR of 48 dB and DR of 96 dB were achieved with a power dissipation of 4.5 nW per pixel.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".