Tailoring the refractive index of ITO thin films by genetic algorithm optimization of the reactive DC-sputtering parameters
Bibliographic record
Abstract
The variation of oxygen concentration in the Indium Tin Oxide (ITO) structure highly impacts its electrical and optical characteristics. In this work, we investigated the effect of oxygen partial flow (O<sub>2</sub>/O<sub>2</sub>+Ar) and deposition pressure (<i>p</i>) on the refractive index (<i>n</i>) of reactive sputtered ITO thin films. A statistical study with a Genetic Algorithm (GA) optimization was implemented to find optimal deposition conditions for obtaining particular refractive indices. Several samples of ITO thin films with refractive indices ranging from 1.69 - 2.1 were deposited by DC sputtering technique at various oxygen concentrations and deposition pressures, in order to develop the statistical database. A linear polynomial surface was locally fitted to the data of O<sub>2</sub>/O<sub>2</sub>+Ar, <i>p</i>, and <i>n</i> of deposited films. This surface was then used as the fitness function of the GA. By defining the desired n as the objective value of the GA, the optimized deposition conditions can be found. Two cases were experimentally demonstrated, with the GA determining the needed process parameters to deposit ITO with <i>n</i>=2.2 and <i>n</i>=1.6. Measured results were very close to desired values, with <i>n</i>=2.25 and <i>n</i>=1.62, demonstrating the effectiveness of this method for predicting needed reactive sputtering conditions to enable arbitrary refractive indices.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".