Advanced sub 0.13µm Cu Devices – Failure Analysis and Circuit Edit With Improved FIB Chemical Processes and Beam Characteristics
Bibliographic record
Abstract
Abstract Advances in FIB (focused ion beam) chemical processes and in the Ga (gallium) beam profile are discussed; these advances are necessary for the successful failure analysis, circuit edit and design verification of advanced, sub-0.13µm Cu devices. Included in this article are: a novel FIB method (CopperRx) for smoothly milling thick, large grained Cu lines; H2O and O2 processes for cleanly cutting thin, smaller grained Cu lines, thereby forming electrically open interconnects; a XeF2 GAE (gas assisted etching) process for etching low k, CVD dielectrics such as F and C doped SiO2; H2O and XeF2 GAE processes for etching low k, spin-on, organic dielectrics such as SiLK; a recently developed recipe for the deposition of SiO2 based material with intermediate resistivity (106 µohm·cm) which is useful in the design verification of frequency sensitive, high speed analog and SOC (system on chip) circuits; an improved, more Gaussian Ga beam with less current density in the beam tails (VisION column) which provides higher resolution, real time images needed for end-point detection on sub 0.13µm features during milling.
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 | 0.001 |
| 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) | 0.002 | 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".