Formation and Diffusion of Charged and Neural Defect States in Crystalline GeSe for Synaptic Electronics

Information and communication technologies have been historically powered by silicon. The current major worldwide drive for big data, machine learning and computing threatens to overwhelm Si-based resources. The search for alternative materials and technologies is thus crucial and represents a unique opportunity to explore and link materials’ properties and performances in unexplored architectures. In this upcoming process, new disrupting solutions referred to as in-memory computing, based on synaptic electronics, are emerging. Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and selectors. However, the interplay between the properties of materials and the devices’ performances has not been completely understood. Here by means of state-of-the-art high-throughput simulations workflows for first principles condensed matter simulations, as implemented in the AiiDA infrastructure for the Quantum ESPRESSO, we study charged and neural defect states in GeSe chalcogenide with particular attention to their formation energies and diffusion barriers. Our results deepen current understanding of the interplay between structural, doping, and electrical properties of complex GeSe compounds in their application as switching materials.