Measuring heat transport in electronic devices over small length scales
Bibliographic record
Abstract
High current operation is often desirable in electronic devices, as it affects a variety of essential aspects such as switching speed in transistors, sensitivity in sensors, and light output in light-emitting devices. High currents, however, can lead to premature device failure or the degradation of device performance, as the inherent increase in device operating temperature can degrade materials/junctions, increase leakage, or lower mobility. In an effort to characterize the heat transport in electronic devices near the region where the heat is generated and dissipated, we have implemented a frequency-domain thermoreflectance system that allows for the characterization of thermal transport in prototype devices, with the end goal of finding avenues to optimize the heat conduction/dissipation problem. Particularly for emerging devices based on 2D materials such as graphene, there is an opportunity to impact the device performance at the early stages of device development. We present a method that allows for the thermoreflectance signal to be detected at frequencies beyond 50 MHz, which leads to the ability to measure short-range heat transport, where most of the heat dissipation bottlenecks in small scale devices exist.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".