Effect of Vacuum Heat Treatment on the Microstructure of a Laser Powder-Bed Fusion-Fabricated NiTa Alloy
Bibliographic record
Abstract
The semiconductor industry uses a physical vapor-deposition process, with a nickel-tantalum (NiTa) alloy-sputtering target, to apply an amorphous NiTa thin film layer between the magnetic soft underlayer and substrate of a heat-assisted magnetic-recording hard disk drive. Currently, the alloy-sputtering target is produced through a hot-pressing (HP) process followed by a hot isostatic pressing (HIP). In this study, we demonstrate a better process for producing the sputtering targets, using laser powder-bed fusion (L-PBF) followed by vacuum heat treatment (VHT), to produce alloy targets with superior microstructural characteristics that will produce better-quality thin films. We compare as-fabricated (just L-PBF) specimens with specimens produced by L-PBF and then annealed at different conditions. Where the as-fabricated specimens are characterized by columnar dendrites, annealing at 1275 °C for 4 h produces a uniform equiaxed grain microstructure and a uniformly dispersed fcc Ta precipitate. In addition, the average microhardness value is reduced from 725 ± 40 to 594 ± 26 HV0.2 and the maximum compressive residual stress is reduced from 180 ± 50 MPa to 20 ± 10 MPa as the result of dislocation elimination during the recovery and recrystallization process. Finally, due to microstructure recrystallization, the VHT-treated L-PBF NiTa specimens exhibit a smaller grain size (2.1 ± 0.2 µm) than the traditional HIP-treated HP specimens (6.0 ± 0.6 µm).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".