Beyond earth: Evaluating SiC and GaN as next-generation semiconductors for Venus exploration
Bibliographic record
Abstract
Abstract This paper evaluates Silicon Carbide (SiC) and Gallium Nitride (GaN) as potential wide-bandgap semiconductor materials for Venus’ surface exploration. Both materials possess properties advantageous for withstanding Venus’ extreme conditions. Through an in-depth comparison, the study examines stability, energy efficiency, and integration levels of both materials. Although GaN showcases superior electron mobility and potential for high energy efficiency, it confronts challenges in large-scale circuit integration due to its limited wafer size and intricacies in device structure design. Conversely, SiC-based processors have demonstrated operability at high temperatures, with performance comparable to computers used in previous space missions. While neither material currently achieves the processing prowess of past space exploration computers, the study recommends SiC for Venus exploration due to its demonstrated capabilities and higher potential for integration. The paper concludes that optimized SiC processors hold promise for future Venusian surface missions.
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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 | 0.000 |
| 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 | 0.001 | 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".