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Updated: Jun 12, 2026
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The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry
Published on: August 12, 2013
Computational manufacturing of optical interference coatings: method, simulation results, and comparison with
Karen Friedrich1, Steffen Wilbrandt, Olaf Stenzel
1Fraunhofer Institut für Angewandte Optik und Feinmechanik (IOF), Albert-Einstein-Strasse 7, D-07745 Jena, Germany.
Applied Optics
|June 3, 2010
Summary
Virtual deposition runs estimate production yield for plasma ion-assisted deposition of optical coatings. Simulations quantify deposition errors, improving process control and yield prediction for advanced optical filters.
Area of Science:
- Materials Science
- Optical Engineering
- Thin Film Technology
Background:
- Plasma ion-assisted deposition (PIAD) is crucial for advanced optical coatings.
- Accurate prediction of production yield requires understanding and quantifying deposition errors.
- Optical monitoring and quartz crystal monitoring are key techniques for layer termination.
Purpose of the Study:
- To estimate the production yield of oxide optical interference coatings using PIAD.
- To investigate the reproducibility of coating properties and quantify deposition errors.
- To verify simulation results with experimental data.
Main Methods:
- Virtual deposition runs were performed to simulate the PIAD process.
- Single-layer coatings were deposited to quantify variations in refractive index, extinction coefficient, and film thickness.
- The oscillator model was used to simulate optical properties, with parameters varied using a normal distribution.
- Film thickness variations were simulated based on monitoring strategies (broadband optical monitoring or quartz crystal monitoring).
Main Results:
- Simulations successfully estimated production yield for PIAD oxide optical interference coatings.
- Quantified variations in refractive index, extinction coefficient, and film thickness provide insights into deposition errors.
- Verified simulation predictions against experimental data from multiple deposition runs.
Conclusions:
- Virtual deposition runs are effective for predicting the production yield of optical coatings.
- Understanding and simulating deposition errors are critical for optimizing PIAD processes.
- The study provides a framework for improving the reliability and efficiency of optical coating production.
