Öner B. B., Alp İ.
MODELLING, cilt.7, sa.5, ss.1-15, 2026 (ESCI)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
7
Sayı:
5
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Basım Tarihi:
2026
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Doi Numarası:
10.3390/modelling7050215
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Dergi Adı:
MODELLING
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Derginin Tarandığı İndeksler:
Emerging Sources Citation Index (ESCI)
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Sayfa Sayıları:
ss.1-15
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Gazi Üniversitesi Adresli:
Evet
Özet
Despite recent advances, achieving higher quality factors while maintaining smaller mode
volumes remains a major challenge in current cavity designs. Since strong spatial confinement with minimal electromagnetic leakage depends on a precisely optimized hole-radius
distribution, an excessively high computational burden is often required. This paper proposes a hybrid strategy that combines a machine learning model with a genetic algorithm.
A quality factor exceeding 10^8 is achieved, and the corresponding 15-hole pair Fabry–Perot
cavity exhibits a mode volume of 0.52 (λ/n)3
. The approach thus yields a high Q-factor
within a cavity of such ultra-compact dimensions while significantly reducing calculation
cost. A comprehensive comparison is also given, demonstrating these certain advantages
over the other three methods—analytical tapering profiles, a pure genetic algorithm, and
machine-learning-based estimation—applied separately in this study. The methodology
considered may provide an adaptable framework for various related complex photonic
optimization problems.