Abstract
Tantalum pentoxide (Ta2O5) films deposited on fused silica substrates are critical components of high-power laser systems. Ion-assisted electron beam evaporation (IAD-EBE) is the mainstream technique for fabricating Ta2O5 films. However, it commonly requires extensive experimental efforts for deposition quality optimization, while each coating cycle is extremely time-consuming. To solve this issue, this work establishes a dataset targeting the surface roughness (Rq) and refractive index (n) of Ta2O5 films using atomic force microscopy, as well as ellipsometer and deposition experiments. Influence of assisting ion source beam voltage (V)/current (I) and Ar (Q1)/O2 (Q2) flow rate on the n and Rq of Ta2O5 films are analyzed. Combining energy-field mechanism analysis with a Bayesian optimization approach (PI-BO), both deposition quality prediction and feature analysis of process parameters are achieved. The determination coefficient/mean absolute error for the prediction models of n and Rq reach 0.927/0.013 nm and 0.821/0.049 nm, respectively. Based on sensitivity analysis, the weight factors of V, I, Q1, and Q2 affecting n/Rq of Ta2O5 films are determined to be 0.616/0.274, 0.199/0.144, 0.113/0.582, and 0.072/0.000. V and Q2 are identified as the core factors for regulating deposition quality. The optimal ranges for V and Q2 are 600~700 V and 70~80 sccm, respectively. This study proposes a PI-BO method for predicting Rq and n of Ta2O5 films under small-data conditions, while determining the preferred parameter ranges and their sensitivity weight factors. These findings provide effective theoretical support and technical guidance for IAD-EBE strategy design and optimization of optical films in high-power laser systems.
| Original language | English |
|---|---|
| Article number | 166 |
| Journal | Micromachines |
| Volume | 17 |
| Issue number | 2 |
| DOIs | |
| State | Published - Feb 2026 |
| Externally published | Yes |
Keywords
- TaO film
- ion-assisted electron beam evaporation
- physics-informed Bayesian optimization
- refractive index
- surface roughness
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