Efficiency-Driven Design and Implementation of a Reconfigurable 12-Bit ADC Using 14 nm FinFET Technology
Bibliographic record
Abstract
The demand for efficient and high-performance Analog-to-Digital Converters (ADCs) in modern systems, such as image sensors, continues to grow.Conventional methods, like Successive Approximation Register (SAR) ADCs and Sigma-Delta (Σ-Δ) ADCs, often face challenges in power efficiency, adaptability, and chip area, particularly at smaller technology nodes.These methods struggle with power dissipation and lack flexibility in dynamic applications.This research presents an efficiency-driven reconfigurable 12-bit Analog-to-Digital Converter (R-ADC) using 14 nm FinFET technology.The reconfigurable design allows the ADC to adapt dynamically to system requirements, addressing the drawbacks of conventional designs.FinFET technology, known for reducing leakage and improving short-channel effects, enhances the power efficiency and performance of the proposed ADC.The R-ADC achieves a 0.35% reduction in power consumption, a 0.20% improvement in sampling rate, and a 0.15% reduction in chip area compared to SAR ADCs.Additionally, key dynamic parameters such as Signal-to-Noise Ratio (SNR) and Total Harmonic Distortion (THD) are improved, with SNR increasing by 0.10% and THD decreasing by 0.12%.The proposed R-ADC offers significant improvements in efficiency and performance, making it a strong candidate for low-power, high-performance applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".