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 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".