Compositional Dependence of Physical Parameters in Sn-se-in Chalcogenide Glasses

Abstract

Recent years have witnessed a growing interest in the field of study of the properties of chalcogenide glasses. Chalcogenide glasses find numerous potential applications owing to their typical electrical and optical properties. So these glassy alloys have been investigated for the modification of their properties. These glasses possess phase reversal property between crystalline and amorphous states [1-4]. This phase reversal property makes them useful in rewritable optical recording [5, 6]. These chalcogenide glasses are semiconductors in nature and have low transmission losses in the infrared region, so these can be used in producing glass sensors, in infrared optical fibers, information transfer xerography, switching and memory devices, photolithography and fabrication of inexpensive solar cells [7-12]. The combination Sn7Se93-xInx has the glass forming ability over a wide range of compositional variation [13, 14]. The addition of impurity metal Indium to Se increases its glass forming ability which may also enhance the optical and electrical properties of the system. The addition of metallic impurity also increases the glass forming region as well as it creates disorder in the composition and configuration of the glassy system. The addition of metallic impurity Indium along with the replacement of Se increases the glass transition temperature of the system. Every impurity atom of Indium tries to adjust itself according to the nearest neighbor’s environment in Se, so it looks like causing little effect on the glassy system [15]. However recent study indicates that the addition of metallic impurities causes significant effect on the properties of chalcogenide glasses [16]. A continuous change in the physical properties has been observed with the variation in the chemical composition. The effect of compositional variation on the physical properties of the chalcogenide glasses is generally explained by using ordered bond network model also known as chemically ordered network model.