Abstract
A method for thickness monitoring and turning-point prediction during deposition of narrow band pass optical filters (NBPF) for dense-wavelength-division-multiplexing (DWDM) applications is proposed. The method is based on a recurrent approach, with relative transmittance .tting, and includes partial coherence and monochromator bandpass e.ects. We show that the partial coherence e.ects in thin .lm structures are signi.cant and can not be neglected. The proposed method is applicable for precise thickness monitoring and deposition control of any complex multilayer coating.
Highlights
Thin-film-filter technology provides high performance and low cost dense-wavelengthdivision-multiplexing (DWDM) devices
Design of NBPFs consisting of more than 100 dielectric films is based on several Fabry-Perot cavities [5,6,7,8]
Sensitivity to thickness errors and thickness error compensation have been discussed by Bousquet et al [9], Willey [10], and Sullivan and Dobrowolski [11, 12]
Summary
Thin-film-filter technology provides high performance and low cost dense-wavelengthdivision-multiplexing (DWDM) devices. Correct predictions of turning points during deposition of narrow band pass filters are crucial for overall filter performance. There are two different approaches for monitoring of turning-points using optical transmittance data. The second approach is based on a fitting of all transmittance data during the film deposition to a model based on light interference in the filter structure. We propose a method to model the most important optical phenomena affecting fitted transmittance signal and simultaneously keep computation time sufficiently short. The proposed approach uses: (i) recurrence giving fast computation, (ii) fitting of relative quantity which eliminates inaccuracy of the absolute transmittance measurement, (iii) numerical correction of the effects of partial coherence originating from monochromator bandwidth, (iv) correction of divergence of the testing incident beam, and (v) fitting of the measured data to the model using robust non-
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