Bibliographic record
Abstract
Designers seeking electronic package miniaturization but lacking the resources to utilize custom ASIC or complex 3D integration approaches can now take advantage of chip stacking technology for integrating a range of devices into small, system-in-package (SiP) structures. A robust, innovative approach, suitable for supporting low- to medium-volume applications, has been developed which avoids the cost and/or size penalties typically encountered using traditional multi-chip packaging techniques. Using bare die and vertical interconnect/interposer structures, this stacking technology permits the design of multi-chip assemblies with either identical or dissimilar die, co-packaged with discrete and/or integrated passive devices. The approach is independent of ASIC foundry process and does not require through-silicon via (TSV) technology, and is therefore well-suited for designs incorporating multiple IC's from different semiconductor processes or manufacturing sources. Relative to system-on-chip (SoC) ASIC implementations, which carry large upfront NRE costs and long development cycles, 3D co-packaging of heterogeneous devices in customized SiP packages offers a proven, cost-effective alternative with greater design flexibility and reduced time to market. This presentation will describe this novel 3D packaging approach, and how it can be used in conjunction with discrete and integrated passive components to address package designs where size, weight, and/or performance are at a premium.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".