Micromachined Chip Scale Thermal Sensor for Thermal Imaging
Bibliographic record
Abstract
The lateral resolution of scanning thermal microscopy (SThM) has hitherto never approached that of mainstream atomic force microscopy, mainly due to poor performance of the thermal sensor. Herein, we report a nanomechanical system-based thermal sensor (thermocouple) that enables high lateral resolution that is often required in nanoscale thermal characterization in a wide range of applications. This thermocouple-based probe technology delivers excellent lateral resolution (∼20 nm), extended high-temperature measurements >700 °C without cantilever bending, and thermal sensitivity (∼0.04 °C). The origin of significantly improved figures-of-merit lies in the probe design that consists of a hollow silicon tip integrated with a vertically oriented thermocouple sensor at the apex (low thermal mass) which interacts with the sample through a metallic nanowire (50 nm diameter), thereby achieving high lateral resolution. The efficacy of this approach to SThM is demonstrated by imaging embedded metallic nanostructures in silica core-shell, metal nanostructures coated with polymer films, and metal-polymer interconnect structures. The nanoscale pitch and extremely small thermal mass of the probe promise significant improvements over existing methods and wide range of applications in several fields including semiconductor industry, biomedical imaging, and data storage.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".