An Extended <i>L</i>–2<i>L</i> De-Embedding Method for Modeling and Low Return-Loss Transition of Millimeter Wave Signal Through Silicon Interposer
Bibliographic record
Abstract
This article presents a new modeling and optimization approach for low return-loss transition of millimeter wave signals in flip-chip die-to-die interconnects using silicon interposer technology. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> –2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> de-embedding method is used and extended by carefully selecting appropriate structures and designs to model and characterize micro/millimeter-wave bumps and vias. Initially, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> –2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> method is employed to analyze microstrip line die-to-die interconnects, including trace and microbumps. Subsequently, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> –2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> de-embedding method is extended to examine the stripline signaling scheme and extract the model of vias. It is shown that the proposed approach can extract the model of stacked vias between the different layers of a silicon interposer stack-up. Also, the adopted method is utilized to analyze various parts of the transition in the case-study signaling schemes with ground shields. The validity of the proposed strategy is verified by employing the commercial full-wave simulation tool Ansys HFSS to analyze die-to-die interconnects at different stages. The effectiveness of the method in optimizing test structures is also investigated, with the optimized structures exhibiting insertion losses below 1 dB and return losses better than 25 dB from dc to 60 GHz. Using the proposed modeling method provides room for significant improvements in the prelayout design stages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".