A Novel Approach for Enhancing Critical FIB Imaging for Failure Analysis and Circuit Edit Applications
Bibliographic record
Abstract
Abstract As semiconductor device features continue to decrease in size from merely sub micron to below 100 nanometers it becomes necessary to mill smaller and higher aspect ratio FIB vias with reduced ion beam current. This significantly reduces the number of secondary electrons and ions available for endpoint detection and imaging. In addition FIB gas assisted etching introduces a gas delivery nozzle composed of conductive material. This component is grounded to prevent charge build up during ion beam imaging or milling. The proximity of the nozzle to the sample surface creates a shielding effect which reduces the secondary electron detection level as well [1]. The ability to enhance secondary electron imaging for end point detection is required for successful FIB circuit edit and failure analysis applications on advanced technologies. This paper reviews the results obtained using FIB Assist, an image and signal enhancement product for the FEI / Micrion platform, for critical FIB endpoint determination. Examples of FIB fabricated probe points with 30 x 30 nm FIB vias and circuit edit applications endpointing on metal 1 with high aspect ratio holes are presented.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".