Plasma FIB DualBeam Delayering for Atomic Force NanoProbing of 14 nm FinFET Devices in an SRAM Array
Bibliographic record
Abstract
Abstract The result of applying normal xenon ion beam milling combined with patented DX chemistry to delayer state-of-theart commercial-grade 14nm finFETs has been demonstrated in a Helios Plasma FIB DualBeam™. AFM, Conductive-AFM and nano-probing with the Hyperion Atomic Force nanoProber™ were used to confirm the capability of the Helios PFIB DualBeam to delayer samples from metal-6 down to metal-0/local interconnect layer and in under two hours produce a sample that is compatible with the fault isolation, redetection, and characterization capabilities of the AFP. IV (current-voltage) curves were obtained from representative metal-0 contacts exposed by the PFIB+DX delayering process and no degradation to device parameters was uncovered in the experiments that were run. Compared to mechanically delayering samples, the many benefits of using the PFIB+DX process to delayer samples for nano-probing were conclusively demonstrated. Such benefits, include sitespecificity, precise control over the amount of material removed, >100μm square DUT (device under test) area, nm-scale flatness over the DUT area, nm-scale topography between contacts and the surrounding ILD, uniform conductivity across the DUT area, all with no obvious detrimental effects on typical DC device parameters measured by nano-probing.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".