Substrate integrated circuits (SICs) for GHz and THz electronics and photonics: Current status and future outlook
Bibliographic record
Abstract
This paper attempts to provide a panoramic picture of the research and development of substrate integrated circuits (SICs), presumably the next generation of high-frequency integrated circuits for GHz and THz electronics and photonics. This work begins with a summarized overview of current status of SICs-related research over microwave and millimeter-wave frequency ranges, and an outlook into their future development is then discussed with respect to the last unexplored (or rather limited) explored frontier of THz electromagnetic spectrum. With special interest in low-cost and matured CMOS and Si-related technologies, we would be able to examine the possibility of using SICs technologies within such matured processing platforms. This development may be enabled by the rapid deployment of through-silicon via (TSV) processes and related 3-D stack Silicon techniques as well as material research progress such as nanostructured and subwavelength plasmonics. In this way, SICs may allow us to anticipate and extrapolate their applications trends towards the THz frequency range where no tangible integrated circuits technology is available to date. Challenging issues and future directions are considered, pointing to a potentially cost-effective and performance-promising ICs solution for mass commercial applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".