Noise considerations in field-effect biosensors
Bibliographic record
Abstract
Field-effect sensors used to detect and identify biological species have been proposed as alternatives to other methods such as fluorescence deoxyribonucleic acid (DNA) microarrays. Sensors fabricated using commercial complementary metal-oxide-semiconductor technology would enable low-cost and highly integrated biological detection systems. In this paper, the small-signal and noise modeling of biosensors implemented with electrolyte-insulator-semiconductor structures is studied, with emphasis on design guidelines for low-noise performance. In doing so, a modified form of the general charge sheet metal-oxide-semiconductor field-effect transistor model that better fits the electrolyte-insulator-semiconductor structure is used. It is discussed how if the reference electrode and the insulator-electrolyte generate no noise associated with charge transport, then the main noise mechanisms are the resistive losses of the electrolyte and the low-frequency noise of the field-effect transistor. It is also found that for realistic sensor geometries and high electrolyte concentrations, the noise from the field-effect transistor (FET) dominates the thermal noise from the electrolyte resistance, and the optimal biasing point for the FET for minimum noise is found to be around moderate inversion.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".