A method for fabricating CMOS back-end-of-line-compatible solid-state nanopore devices
Bibliographic record
Abstract
Abstract Solid-state nanopores (ssNPs), nm-sized holes in thin, freestanding membranes, are powerful single-molecule sensors capable of interrogating a wide range of target analytes, from small molecules to large polymers. Interestingly, due to their high spatial resolution, nanopores can also identify tags on long polymers, making them an attractive option as the reading element for molecular information storage strategies. To fully leverage the compact and robust nature of ssNPs, however, they will need to be packaged in a highly parallelized manner with on-chip electronic signal processing capabilities to rapidly and accurately handle the data generated. Additionally, the membrane itself must have specific physical, chemical, and electrical properties to ensure sufficient signal-to-noise ratios are achieved, with the traditional membrane material being SiN x . Unfortunately, the typical method of deposition, low-pressure vapour deposition, requires temperatures beyond the thermal budget of complementary metal–oxide semiconductor back-end-of-line (BEOL) integration processes, limiting the potential to generate an on-chip solution. To this end, we explore various lower-temperature deposition techniques that are BEOL-compatible to generate SiN x membranes for ssNP use, and successfully demonstrate the ability for these alternative methods to generate low-noise nanopores that are capable of performing single-molecule experiments.
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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 | 0.000 |
| 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) | 0.001 | 0.001 |
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".