Evaluating Ovonic Threshold Switching Materials with Topological Constraint Theory

Tuning Amorphous Selenium Composition with Tellurium to Improve Quantum Efficiency at Long Wavelengths and High Applied Fields

Amorphous selenium (a-Se) is a large-area compatible photoconductor that has received significant attention toward the development of UV and X-ray detectors for a wide range of applications in medical imaging, life science, high-energy physics, and nuclear radiation detection. A subset of applications require detection of photons with spectral coverage from UV to infrared wavelengths. In this work, we present a systematic study utilizing density functional theory simulations and experimental studies to investigate optical and electrical properties of a-Se alloyed with tellurium (Te). We report hole and electron mobilities and conversion efficiencies for a-Se1-xTex (x = 0, 0.03, 0.05, 0.08) devices as a function of applied field, along with band gaps and comparisons to previous studies. For the first time, these values are reported at high electric field (>10 V/μm), demonstrating recovery of quantum efficiency in Se-Te alloys. A comparison to the Onsager model for a-Se demonstrates the strong field dependence in the thermalization length and expands on the role of defect states in device performance.

Using Machine Learning Potentials to Explore Interdiffusion at Metal-Chalcogenide Interfaces

Chalcogenide alloys are key materials for selector and memory elements used in next-generation nonvolatile memory cells. However, the high electric fields and Joule heating experienced during operation can promote interdiffusion at the interfaces that degrade device performance over time. A clear atomic scale understanding of how chalcogenide alloys interact with electrodes could aid in identifying ways to improve long-term device endurance. In this work, we develop a robust set of moment tensor potentials (MTPs) to examine interactions between Ge-Se alloys and Ti electrodes. Previous works have shown evidence of strong interactions between Ti and chalcogenide alloys. This system offers an important first test in the use of ML empirical potentials to understand the role of interfaces in endurance in memory elements and broader nanoscale devices. The empirical potentials are constructed using an active learning moment tensor potential framework that leverages a broad data set of first-principles calculations for Ti, Ge, and Se compounds. Long-term simulations (>1 ns) show significant interdiffusion at the Ti|Ge-Se interface with Ti and Se both actively moving across the original interface. The strong chemical affinity of Ti and Se leads to a well-defined Ti-Se region and a severely Se-depleted central Ge-Se region with unfavorable selector characteristics. The evolution of the Ti-Se layer can be described using a self-limited growth model. By comparing effective Ti-Se diffusion constants for simulations at different temperatures, we find a low activation energy of 0.1 eV for Ti-Se layer interdiffusion.