Abstract
The proposed multilayer superlattice-based amorphous Se structure by Esaki exhibits fascinating features, including quantum size effects, negative differential resistance, and sequential resonant tunneling. This study utilizes data from the transport characteristics of the structure measured using variations of current from the applied voltage (IV) when placed under dark conditions and when illuminated with a superlattice structure deposited on the backside of n-type Si. Computational values and functional fitting curves are plotted using machine learning algorithms, such as Neural Net Fitting and Regression Learner, to optimize the multinano interfaces. Unlike other methodologies used in analyzing nonsuperlattice Se and As 2 Se 3 structure, the methods employed in this study in modeling the Se and As 2 Se 3 superlattice will not only provide significant advancement by observing vital predictions on the responsibility and sensitivity of the photodetectors but also refurbish nano-electronics device fabrication with efficient tools for researchers in the domains of data analysis for semiconductor material and characterizations, as well as paving the way for future research.