Generation of accurate on-chip time constants and stable transconductances
Bibliographic record
Abstract
A method for generating accurately known on-chip time constants and less accurate but stable transistor transconductances over process, power-supply, and temperature variations is presented. The technique uses a constant-g/sub m/ bias circuit, which has a resistor that is tuned with a fully integrated CMOS phase-locked loop (PLL) locked to an external frequency reference (normally present in most systems). Other on-chip analog circuits biased using the same constant-g/sub m/ bias circuit are also stabilized. The PLL uses a charge-pump structure with three control loops (two digital and one analog) having overlapping ranges with hysteresis to minimize tuning glitches in the steady state. The PLL has a lock range of 135 to 300 MHz, and displays an RMS jitter of 15.6 ps. The transconductances generated from the circuit display a 2.2% variation for a 60/spl deg/C change in temperature, and a 1.3% variation for a 10% variation in power-supply voltage. The design has been fabricated in a 0.35-/spl mu/m CMOS process, using an active area of 1200/spl times/1200 /spl mu/m/sup 2/ and draws 5.8 mA from a 3.3-V supply.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".