An efficient design of 45-nm CMOS low-noise charge sensitive amplifier for wireless receivers
Bibliographic record
Abstract
Amplifiers are widely used in signal receiving circuits, such as antennas, medical imaging, wireless devices and many other applications. However, one of the most challenging problems when building an amplifier circuit is the noise, since it affects the quality of the intended received signal in most wireless applications. Therefore, a preamplifier is usually placed close to the main sensor to reduce the effects of interferences and to amplify the received signal without degrading the signal-to-noise ratio. Although different designs have been optimized and tested in the literature, all of them are using larger than 100 nm technologies which have led to a modest performance in terms of equivalent noise charge (ENC), gain, power consumption, and response time. In contrast, we consider in this paper a new amplifier design technology trend and move towards sub 100 nm to enhance its performance. In this work, we use a pre-well-known design of a preamplifier circuit and rebuild it using 45 nm CMOS technology, which is made for the first time in such circuits. Performance evaluation shows that our proposed scaling technology, compared with other scaling technology, extremely reduces ENC of the circuit by more than 95%. The noise spectral density and time resolution are also reduced by 25% and 95% respectively. In addition, power consumption is decreased due to the reduced channel length by 90%. As a result, all of those enhancements make our proposed circuit more suitable for medical and wireless devices.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".