Current TRAM: A Circuit Scheme for Highly-Linear, Tunable V/I Conversion with Ultra-Low Transconductance
Bibliographic record
Abstract
This paper reports on an innovative circuit scheme for process-invariant biasing, which works based on a regenerative current steering mechanism. Named as the current translation and mirroring (Current TRAM) scheme, the proposed circuit is capable of providing bias currents established using the current copying ratios of two complementary current mirrors. Then, the circuit is evolved into a voltage-to-current (V/I) converter, allowing for highly-linear control of the bias current using an input control voltage. The proposed V/I converter is then used in a cross-coupled structure in order to design a fully-integrated pseudo-differential operational transconductance amplifier (OTA) circuit with a linearly-tunable transconductance. This OTA is dedicated to the integrated circuit-sensor systems where the analog processing (e.g., filtering) of signals with extremely low cut-off frequencies (such as in bioelectronic sensor interfaces) is required. Designed in a standard 0.13 μm CMOS process, the proposed OTA exhibits a transconductance tunable within the range of 0.39 ~ 0.69 nA/V with a maximum total harmonic distortion of 1%. The circuit dissipated 8 nW at a supply voltage of 1.0 V.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".