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 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 | 0.002 |
| 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".