Ba<inf>1−x</inf>Sr<inf>x</inf>TiO<inf>3</inf> (x =0.4) nanoparticles dispersion for 3D integration of decoupling capacitors on glass interposer
Bibliographic record
Abstract
With the aim of scaling up and increasing the frequency of data processing within 3D integration, the placement of decoupling capacitors close to the chip is required in order to increase electrical performances of the module. Thin films deposition of materials with high permittivity (high k materials) (8 μF/cm2). Ba0.6Sr0.4TiO3nanoparticles synthesized using supercritical fluid technique and presenting attractive properties, e.g. size ~15±2 nm, εr= 260 at 1 kHz for T = 300K, have been chosen to overcome technological limits for the realization of such high capacitance density thin films. In this paper, different approaches are proposed to disperse the nanoparticles, since their spontaneous agglomeration is critical. In order to reduce the size of these agglomerates (~500 nm), nanoparticles are dispersed in methanol or water. In both cases, the effects of the solution concentration and the ultrasonic homogenizer power on the dispersion of nanoparticles are examined. Ultrasonication is used to break the agglomerates. Dynamic light scattering is used to monitor the changes of agglomerates size. The process to take samples from solution was optimized. In the case of dispersion in methanol, the ultrasonication (power =100W - pulse 1s/3s) of concentrated solution (C ≥ 0.1 g/L) during 10 min, leads to agglomerates corresponding to 3-5 particles (60±10nm). In the second case, the measurement of Zeta potential gives access to the optimal pH conditions (pH9.7) for which the dispersion of Ba0.6Sr0.4TiO3nanoparticles is stabilized.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.034 |
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".