Evolution of Electrical Transmission Characteristics in TSV and TGV Interconnect Structures Under Thermal Loading
Bibliographic record
Abstract
Through silicon via (TSV) and through glass via (TGV) are critical for advanced chip packaging, enabling vertical interconnections and improving bandwidth. The increase in chip density leads to a dramatic rise in heat generation, which induces the significant coefficient of thermal expansion (CTE) mismatch in TSV and TGV and further induces internal stresses, potentially degrading electrical performance and reliability. This work presented a study of TSV and TGV interconnect structures, focusing on their electrical transmission characteristics under thermal loading, evaluating the reliability differences between TGV and TSV by using electrical transmission characteristics as the metric. The findings reveal that the electrical transmission characteristics of TGV generally outperform those of TSV. Especially, when optimized by increasing signal via spacing and adding additional grounding vias, the characteristics of TGV significantly enhance high-frequency performance. Furthermore, thermal shock measurements indicate that TSV experience rapid transmission characteristics degradation at lower shock cycles, while TGV degrade more slowly even at higher cycles. Based on microstructural analysis, we observe that TSV suffers from cracked insulation layers, leading to current leakage and transmission performance loss; TGV exhibits only minor delamination issues between the RDL and PI that do not significantly impact transmission characteristics. Under high temperature, TSV initially shows improved transmission performance due to air gap formation, but later degrade sharply as copper extrudes and bridges with the silicon substrate. In contrast, under high temperature, TGV exhibit a gradual increase in impedance due to crack expansion within the copper, but this effect remains minimal. TGV not only offers superior transmission performance but also demonstrates greater reliability under thermal loading compared to TSV.
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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) | 0.000 | 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) | 0.001 | 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".