Nanoscale Heat Transport and Magnetism in Magnetic Data Storage Applications
Bibliographic record
Abstract
The hard disk drive has been a key technology in computing for over 60 years, and in spite of the advent of solid state drives which now dominate consumer products, advances in nanoscience are making their way into the hard disk drive to continue to support the demands of cloud computing. In this talk I will outline heat-assisted magnetic recording (HAMR), a technology coming into production soon that allows data storage densities to go beyond what was thought to be the unbreakable superparamagnetic limit of 1 Tera-bit per square inch. Specifically, I will focus on some of the nanoscale aspects of heat transport and magnetism that have posed challenges in the development of HAMR. Regarding heat transport, I will discuss heat dissipation issues in nanostructures such as the near-field plasmonic antenna used to record the data, and show that changes in phonon dispersion in layers only a couple nm in thickness can have a surprising impact on interfacial heat flow. In terms of magnetism, I will present the characterization of the distribution in Curie temperatures in the nanoscale ferromagnetic grains used to store the data. This optical pump-probe technique bypasses the difficulty of attempting to measure the vanishing magnetization at the ferromagnetic-paramagnetic transition, and opens up new questions in the physics of magnetization dynamics at high temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".