Reliable Chiplet Integration on High Density Laminate (2.XD) for AI Hardware
Bibliographic record
Abstract
In this study, High Density Laminate with a bonded organic interposer (2.XD Laminate) is described and evaluated. The 2.XD laminate has <3 μm/3 μm L/S in the region of organic interposer. The test vehicle of three chip module is used to evaluate these laminates. Three chips include a High Bandwidth Memory (HBM2) test chip, a logic chip with dual pitch gC4s and an accelerator chip. The challenges associated with mix pitch and warpage due to organic interposer are explained.These High-Density Laminates are then tested with JEDEC standard reliability tests: High Temperature Storage (HTS), Temperature Humidity Bias (THB), Deep Thermal Cycling (DTC) and Accelerated Thermal Cycling (ATC) and extended thermal cycling up to 2000 cycles in DTC and 6000 cycles in ATC. The results are presented and discussed. A physical analysis is done on the parts after extended cycling which shows no sign of degradation. From design point of view stack vias configurations are also tested in reliability. The results obtained are then explained through Finite Element Model (FEM).
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 | 0.001 |
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".