Nanoscale Film Thickness Gradients Printed in Open Air by Spatially Varying Chemical Vapor Deposition
Bibliographic record
Abstract
Abstract Nanoscale films are integral to all modern electronics. To optimize device performance, researchers vary the film thickness by making batches of devices, which is time‐consuming and produces experimental artifacts. Thin films with nanoscale thickness gradients that are rapidly deposited in open air for combinatorial and high‐throughput (CHT) studies are presented. Atmospheric pressure spatial atomic layer deposition reactor heads are used to produce spatially varying chemical vapor deposition rates on the order of angstroms per second. ZnO and Al 2 O 3 films are printed with nm‐scale thickness gradients in as little as 45 s and CHT analysis of a metal‐insulator‐metal diode and perovskite solar cell is performed. By testing 360 Pt/Al 2 O 3 /Al diodes with 18 different Al 2 O 3 thicknesses on one wafer, a thicker insulator layer (≈7.0 nm) is identified for optimal diode performance than reported previously. Al 2 O 3 thin film encapsulation is deposited by atmospheric pressure chemical vapor deposition (AP‐CVD) on a perovskite solar cell stack for the first time and a convolutional neural network is developed to analyze the perovskite stability. The rapid nature of AP‐CVD enables thicker films to be deposited at a higher temperature than is practical with conventional methods. The CHT analysis shows enhanced stability for 70 nm encapsulation films.
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 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) | 0.001 | 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 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".