Electromigration of Power Devices Part 2: Power Device in Applications in Controlled Environment
Bibliographic record
Abstract
In the power semiconductor industry, there is a continuous development toward a higher current capability of devices while device’s dimensions shrink. Although higher current is preferred, the reliability risk of the electromigration (EM) remains challenging. In the present study, a special EM test vehicle is used to investigate the pure EM behavior of power device, following our previous work. The selection of the test condition is discussed in detail, which is defined based on the temperature and current profiles of the device in the actual application, focusing on the applications of the power devices in a controlled environment, i.e., data centers or telecommunication servers. The current and temperature profiles of the device are discussed based on the device’s maximum power dissipation and its respective thermal design. The results show that the different combinations of currents and temperatures can lead to different ratios of acceleration factor between the temperature and the current. Consequently, an inappropriate selection of the testing condition can result in the over-domination of temperature over the current, leading to wrong conclusions. The effects of the PCB surface finish (both Ni and Sn plating) as well as different electron flow directions on the EM were studied. It is concluded that the maximum current that can be applied to a power device in the application within a controlled environment is rather limited by the cooling capability of the system than the EM induced device failure.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".