Luminescence Study of CdSe Quantum Dots Using Machine Learning Techniques

Abstract

The photoluminescence study of synthesized CdSe quantum dots and comparison with literature based on machine learning (ML) techniques are studied in this paper. Quantum dot is a promising photovoltaic material for thin film solar cells and also shows power superior lasing efficiency higher than existing quantum-well devices. It is possible to create a quantum dot that can generate and absorb energy across the whole solar spectrum. Quantum dots (QDs) are special in a variety of applications due to their distinct size-dependent band gap. Since their photo luminescent properties can be considerably enhanced by optimization of the techniques by which they are synthesized, and are useful in application of optoelectronic disciplines. X-ray diffraction and photoluminescence were used to determine how CdSe quantum dots formed. Later the particle size is calculated by the Debye-Scherrer equation. Because of the quantum confinement effect and size variation, the FWHM of the CdSe samples exhibit greater values in photoluminescence than an ordinary bulk semiconductor, which is also in accordance to the literature based on ML techniques.