Electronics and Packaging Intended for Emerging Harsh Environment Applications: A Review
Bibliographic record
Abstract
Several industrial applications require specific electronic systems installed in harsh environments to perform measurements, monitoring, and control tasks such as in space exploration, aerospace missions, automotive industries, down-hole oil and gas industry, and geothermal power plants. The extreme environment could be surrounding high-, low-, and wide-range temperature, intense radiation, or even a combination of above conditions. We review, in this paper, the main leading applications that demand advanced technologies to fit the unconventional requirements of extreme operating conditions, discussing their main merits and limits compared to established and emerging technologies in this field, including silicon (Si), silicon on insulator (SOI), silicon germanium (SiGe), silicon carbide (SiC) as well as III–V semiconductors particularly the gallium nitride (GaN) semiconductor. In spite of successfully exceeding extreme conditions borders by developing advanced semiconductor devices dedicated for harsh environments, especially in high-temperature applications, the packaging challenges are still limiting the reliability of the developed technologies. Those challenges are examined in this review in terms of limitations and proposed solutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".