An analog-design assistant tool and an example of its application
Bibliographic record
Abstract
In this paper, we introduce an analog-design assistant tool (ADA), which was used to generate a database of 56,280 three-MOSFET circuit topologies. Having setup ADA to provide analog designers with amplifier topologies, it identified 5,103 three-MOSFET amplifiers based on the selection criteria given to the software. As an illustration of ADA capability of helping with circuit optimization, the transconductances of the transistors of each amplifier are automatically optimized by solving a nonlinear optimization problem that is set to maximize gains of circuits. In addition, ADA provides closed-form expressions of gain, input impedance, output impedance and reverse gain. To demonstrate that ADA can be used to identify and optimize new circuits, a previously unknown three-transistor amplifier with a high DC gain of ~40 dB was randomly chosen and fully designed in a 0.13-μm standard CMOS technology. The simulation results obtained with BSIM models show that the circuit topology provided by ADA is accurate and realizable. Using ADA will enable designers to save hours of time and provide them with range of circuits that are customized for their need.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".