Design of a multilayer photonic crystal biosensor for ocular studies enhanced with machine learning towards the evaluation of proliferative vitreoretinopathy.

Ocular studies utilizing a one-dimensional (1D) ternary photonic crystal biosensor are proposed. Incorporation of materials such as gallium nitride, aluminum nitride, and silicon in the design enhances the sensor metrics, facilitating diagnosis of diabetes and eye cancer. Multiple wavelengths are chosen in this investigation in the range of 580-1700 nm. The design analysis of this sensor is done on the basis of the transfer matrix method. Transmission characteristics exhibited by the sensor provide distinct analyte identification. The quality factor, sensitivity, and figure of merit exhibited by the sensor for the diabetes study utilizing eye tear fluids are 40,042, 783 nm RIU-1, and 18,433 RIU-1, respectively. The proposed photonic crystal sensor is also investigated for the study of proliferative vitreoretinopathy, and a high quality factor is exhibited by the sensor. Plane wave expansion and finite difference time domain techniques validate the band gap and field distribution analysis. Machine learning models such as random forest and extreme gradient boosting enhance the work in the prediction of transmission amplitude, considering errors during the fabrication of the photonic crystal structure.