Power Semiconductors for An Energy-Wise Society
Bibliographic record
Abstract
This IEC White Paper establishes the critical role that power semiconductors play in transitioning to an energy wise society. It takes an in-depth look at expected trends and opportunities, as well as the challenges surrounding the power semiconductors industry. Among the significant challenges mentioned is the need for change in industry practices when transitioning from linear to circular economies and the shortage of skilled personnel required for power semiconductor development. The white paper also stresses the need for strategic actions at the policy-making level to address these concerns and calls for stronger government commitment, policies and funding to advance power semiconductor technologies and integration. It further highlights the pivotal role of standards in removing technical risks, increasing product quality and enabling faster market acceptance. Besides noting benefits of existing standards in accelerating market growth, the paper also identifies the current standardization gaps. <br/>The white paper emphasizes the importance of ensuring a robust supply chain for power semiconductors to prevent supply-chain disruptions like those seen during the COVID-19 pandemic, which can have widespread economic impacts.<br/>The white paper highlights the importance of inspiring young professionals to take an interest in power semiconductors and power electronics, highlighting the potential to make a positive impact on the world through these technologies.<br/>The white paper concludes with recommendations for policymakers, regulators, industry and other IEC stakeholders for collaborative structures and accelerating the development and adoption of standards.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.092 | 0.063 |
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".