Bibliographic record
Abstract
A regulator design model is studied and simulated by using a dynamic bias technique.A dynamic bias boosting circuit consists of three parts: detector, amplifier and bias boosting circuit.The above Regulator is presented using CMOS 90 nm process and the obtained results will be examined with other existing technologies.The function of the diagram will be considered in this way that as soon as the slightest change is seen in output voltage; these changes will be transferred to the detector circuit through nodes Vn and Vp.The detector circuit makes sense of these changes and according to the type of detection, holds its output in the supply voltage situation or ground situation and will transfer it to the input of amplifier circuit.The amplifier circuit will work as an inverter and the detector's output will be appeared reversely in its output.The output of amplifier circuit is transmitted to the input of bias boosting circuit.This circuit, by increasing current, results in an increase in the regulated current in a short moment and returns the changes of regulator output voltage to the normal mode.In the discussed regulator, we reduced the power consumption using dynamic bias current to .5 mw and to some extent improved the line and load-transient response.The Technique of increasing the dynamic bias current in LDO design effectively improves the linear and load transmission response and leads to the creation of a precise and affecting voltage in the regulator's output and will be very effective in portable applications where an accurate and noiseless power supply is require.
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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) | 0.991 | 0.964 |
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; both teacher heads agree on what is shown here.
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".