CMOS buried multi-junction (BMJ) detector for bio-chemical analysis
Bibliographic record
Abstract
The CMOS buried multi-junction (BMJ) detector with multiple outputs has distinct spectral responses that may be exploited for applications such as bio-chemical analysis. We tackle here dark current issue by identifying different components inside the detector structure. The identification methods are based on the observation of bias and temperature dependence, as well as measurements of test detector chip integrating different design variations. Surface thermal generation may become predominant when the detector size shrinks, thus causing dark current degradation. To prevent this effect, we propose a low-sized detector structure with passivation of all its surrounding Si/SiO2 interface areas. Also for the detector readout, we present a multi-channel charge-amplifier architecture with noise analysis. Effects of noise coming from amplifiers and related to the coupled detector’s dynamic conductances are illuminated. To pick up weak signals, synchronous detection can be implemented. A BDJ (Buried Double Junction) detector chip designed with a switched-phase architectural approach gives a minimum detectable signal of 15μlx@555nm or 1μlx@555nm at 27°C or – 10°C, for an integration time of 3s or 45s respectively.
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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) | 0.001 | 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".