Far Back End of Line Aluminum Stress Reduction Methods for Two-Dimensional/2.5D Fine Pitch Assemblies
Bibliographic record
Abstract
Fine pitch interconnects when used with two-dimensional (2D)/2.5D packaging technology offer enormous potential toward decreasing signal latency and by making it possible to package increased electrical functionality within a given area. However, fine pitch interconnects present their own set of challenges not seen in packages with coarse pitch interconnects. Increased level of stresses within the far back end of line (FBEOL) layers of the chip is the primary concern. Seven different types of 2D and 2.5D test vehicles with fine pitch and coarse pitch interconnects were built and tested for mechanical integrity by subjecting them to accelerated thermal cycling between −55 °C and 125 °C. Finite element based mechanical modeling was done to determine the stress level within the FBEOL layers of these test vehicles. For all the tested assemblies, experimental data and modeling results showed a strong correlation between reduced pitch and increased level of stresses and increased incidence of failures within the FBEOL region. These failures were observed exclusively at the passivation layer and aluminum pad interface. Experimental data in conjunction with mechanical modeling were used to determine a safe level of stress at the aluminum to passivation layer interface. Global and local design changes were explored to determine their effect on the stresses at this interface. Several guidelines have been provided to reduce these stresses for a 2D/2.5D package assembly with fine pitch interconnects. Finally, a reliable low stress configuration, which takes into account all the design changes, has been proposed, which is expected to be robust with very low risk of failure within the FBEOL region.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".