(Invited) In-Situ and Combinatorial Techniques for Spatial ALD
Bibliographic record
Abstract
Atmospheric-pressure spatial atomic layer deposition (AP-SALD) and chemical vapor deposition (AP-CVD) have been developed in recent years as scalable techniques for the rapid deposition of oxide thin films on different substrates for a variety of applications. The atmospheric nature of these techniques facilitates the integration of characterization tools and the modification of the experimental setup to produce novel materials. Here we report in-situ electrical and optical characterization methods that have been developed for our AP-SALD/CVD system, as well as new techniques to deposit oxide films with nanoscale thickness gradients. The in-situ electrical measurements are enabled by a custom-designed, flexible printed circuit board substrate and the optical measurements are performed via reflectometry techniques. The thickness, resistance, and optical constants of prototypical AP-SALD films (ZnO and Al2O3) were monitored during depositions, providing insight into film nucleation and growth. The nanoscale thickness gradient films facilitate combinatorial high-throughput screening of devices, where a multitude of devices with varying film thicknesses can be fabricated on a single substrate. This combinatorial approach was applied to study the role of Al2O3 film thickness as an insulating layer in quantum-tunneling metal-insulator-metal diodes and as an encapsulation layer in metal halide perovskite solar cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.035 |
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".