Linearity Improvement in Vernier Delay Chain Based Time Difference Amplifiers for Fluorescence Spectroscopy
Bibliographic record
Abstract
Time difference amplifiers (TDAs) are critical for fluorescence spectroscopy, requiring high linearity to amplify picosecond-scale time differences. This work presents a vernier delay chain-based TDA with a differential architecture, achieving an application targeted gain of$2.25 \text{pS} / \text{pS}$across a wide$10 \text{ps}-1 \text{ns}$input range. Fabricated in$0.13 \mu \mathrm{m}$CMOS, the TDA demonstrates a low 3.78 % RMS gain error and robust$\pm 8 \%$gain variation across PVT corners, with only 4 mW power consumption. By using discrete-time delay selection via multiplexers, it eliminates analog tuning, enhancing PVT stability and layout simplicity. Validated through simulation and post-silicon measurements, this TDA offers high linearity for high-precision fluorescence spectroscopy, with potential for closed-loop gain stabilization. Linearity was validated via simulation down to 10 ps; measurements were limited by ESD constraints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".