A Deep Learning-Based Approach for Predicting the Performances of CMOS Voltage-Controlled Oscillator with Optimized Component Sizes
Bibliographic record
Abstract
While design automation plays a crucial role in contemporary large-scale digital systems, the automation of the transistor-level circuit design process continues to pose significant challenges.Recent studies indicate that deep learning algorithms may be utilized to determine optimal transistor dimensions in compact circuitry, such as voltage-controlled oscillators.However, achieving robust and efficient analog circuit design automation in integrated circuit field remains challenging.A deep neural network architecture is introduced for the automatic sizing of analog circuit components, specifically targeting radio frequency applications within the 2 to 5-GHz range.A novel deep learning model designed to simulate voltage-controlled oscillators for microwave applications.This work introduces four algorithms: DNN, CNN, RNN, and SCINet.Two characteristics have been evaluated: output power and phase noise.The models achieved an accuracy of 96%-97% and exhibited a loss ranging from 0.0024 to 0.0036.The prediction of the required features demonstrates outstanding performance across all utilized models.We aim to determine the most effective deep learning model suitable for a specific dataset and computational setting.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".