Engineering Film Microstructure with Glancing Angle Deposition
Bibliographic record
Abstract
The capability of GLAD to fabricate precise nanocolumnar structures is determined by substrate motion. In this chapter, the design of substrate motion algorithms to control ballistic shadowing and engineer film microstructure is described in detail. Thin-film growth is outlined, focusing on the aspects that are most important for GLAD structures. The origin of columnar structures in thin films is described, starting with nucleation through to thin films described by the structure-zone model. Top-down control of GLAD films is achieved using substrate motion to introduce ballistic shadowing to thin-film growth conditions, resulting in columnar structures. The dependence of column tilt angle and porosity on deposition angle is described in detail. For a given column tilt angle, continuous and discrete azimuthal substrate rotation during deposition can produce slanted posts, helical columns, vertical columns and combinations thereof. Through advanced substrate rotation algorithms such as spin–pause and phisweep, the normally interdependent parameters of column tilt and porosity can be partially decoupled to enable access to a wider range of column architectures. Phisweep can also be used to suppress intercolumn evolutionary competition, which contributes to column broadening and fanning. While most GLAD films are grown at low temperature, additional microstructures can be accessed at higher temperatures.
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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) | 0.005 | 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".