CMOS Nanopore-Based DNA Sequencing Systems: Recent Advancements and Future Prospects: A Review
Bibliographic record
Abstract
Recent advancements in electrochemical detection have created significant opportunities in biosensing, particularly in medical diagnostics and health monitoring, where real-time response, high sensitivity, and accuracy are paramount. Nanopore-based deoxyribonucleic acid (DNA) sequencing has emerged as a powerful tool for high-throughput genetic analysis, relying on precise sensing mechanisms and efficient readout electronics. This review provides a comprehensive overview of both nanopore sensor architectures and CMOS readout circuits used in modern DNA sequencing technologies. We discuss different nanopore structures, their advantages, limitations, and noise reduction techniques, alongside the critical role of CMOS readout circuits in converting weak electrochemical signals into actionable data. In addition, we explore the tradeoffs between different arrayed CMOS readout architectures, highlighting challenges in power consumption, noise performance, and scalability in multichannel nanopore arrays. By analyzing these advancements, this article underscores the importance of integrating innovative sensor designs with efficient readout electronics to enhance the accuracy and throughput of next-generation biosensing platforms.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".