Toward accurate thermal modeling of phase change material based photonic devices

TL;DR

This paper emphasizes the importance of accurately modeling thermal properties such as enthalpy of fusion, heat capacity change, and thermal conductivity in phase change material-based photonic devices to improve their simulation and efficiency.

Contribution

It identifies overlooked thermal factors critical for precise modeling of PCM photonic devices and investigates switching energy scaling to enhance device performance understanding.

Findings

Inclusion of enthalpy of fusion improves thermal model accuracy.

Heat capacity change significantly affects phase transition behavior.

Thermal conductivity of liquid-phase PCMs influences device switching energy.

Abstract

Reconfigurable or programmable photonic devices are rapidly growing and have become an integral part of many optical systems. The ability to selectively modulate electromagnetic waves through electrical stimuli is crucial in the advancement of a variety of applications from data communication and computing devices to environmental science and space explorations. Chalcogenide-based phase change materials (PCMs) are one of the most promising material candidates for reconfigurable photonics due to their large optical contrast between their different solid-state structural phases. Although significant efforts have been devoted to accurate simulation of PCM-based devices, in this paper, we highlight three important aspects which have often evaded prior models yet having significant impacts on the thermal and phase transition behavior of these devices: the enthalpy of fusion, the heat capacity change upon glass transition, as well as the thermal conductivity of liquid-phase PCMs. We further investigated the important topic of switching energy scaling in PCM devices, which also helps explain why the three above-mentioned effects have long been overlooked in electronic PCM memories but only become important in photonics. Our findings offer insight to facilitate accurate modeling of PCM-based photonic devices and can inform the development of more efficient reconfigurable optics.