Borophene-Ge2Sb2Te5 (GST)-Based Refractive Index Sensor: Numerical Study and Behaviour Prediction Using Machine Learning

AbstractWe proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3 –1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refrac tive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the inciden t wave’s wide incident angle, and the GST material’s phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnet ic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated dat a and make predictions regarding the behaviour of the structure. We have found the R2 = 0.97 for the proposed artificial neural net...
Source: Plasmonics - Category: Biomedical Science Source Type: research