  Photonic Crystal Fiber design based on surface plasmon resonance phenomenon
(PCF SPR) is optimized before it is fabricated for a particular application. An
artificial intelligence algorithm is evaluated here to increase the ease of the
simulation process for common users. COMSOL MultiPhysics is used. The algorithm
suggests best among eight standard machine learning and one deep learning model
to automatically select the desired mode, chosen visually by the experts
otherwise. Total seven performance indices: namely Precision, Recall, Accuracy,
F1-Score, Specificity, Matthew correlation coefficient, are utilized to make
the optimal decision. Robustness towards variations in sensor geometry design
is also considered as an optimal parameter. Several PCF-SPR based Photonic
sensor designs are tested, and a large range optimal (based on phase matching)
design is proposed. For this design algorithm has selected Support Vector
Machine (SVM) as the best option with an accuracy of 96%, F1-Score is 95.83%,
and MCC of 92.30%. The average sensitivity of the proposed sensor design with
respect to change in refractive index (1.37-1.41) is 5500 nm/RIU. Resolution is
2.0498x10^(-5) RIU^(-1). The algorithm can be integrated into commercial
software as an add-on or as a module in academic codes. The proposed novel step
has saved approximately 75 minutes in the overall design process. The present
work is equally applicable for mode selection of sensor other than PCF-SPR
sensing geometries.
