Journal Article

Deep machine learning unravels the structural origin of mid‐gap states in chalcogenide glass for high‐density memory integration

Miao, Xiangshui, Xu, Ming, Xu, Meng

2022

Overview

The recent development of three‐dimensional semiconductor integration technology demands a key component—the ovonic threshold switching (OTS) selector to suppress the current leakage in the high‐density memory chips. Yet, the unsatisfactory performance of existing OTS materials becomes the bottleneck of the industrial advancement. The sluggish development of OTS materials, which are usually made from chalcogenide glass, should be largely attributed to the insufficient understanding of the electronic structure in these materials, despite of intensive research in the past decade. Due to the heavy first‐principles computation on disordered systems, a universal theory to explain the origin of mid‐gap states (MGS), which are the key feature leading to the OTS behavior, is still lacking. To avoid the formidable computational tasks, we adopt machine learning method to understand and predict MGS in typical OTS materials. We build hundreds of chalcogenide glass models and collect major structural features from both short‐range order (SRO) and medium‐range order (MRO) of the amorphous cells. After training the artificial neural network using these features, the accuracy has reached ~95% when it recognizes MGS in new glass. By analyzing the synaptic weights of the input structural features, we discover that the bonding and coordination environments from SRO and particularly MRO are closely related to MGS. The trained model could be used in many other OTS chalcogenides after minor modification. The intelligent machine learning allows us to understand the OTS mechanism from vast amount of structural data without heavy computational tasks, providing a new strategy to design functional amorphous materials from first principles. The 3D semiconductor fabrication technology requires an “ovonic threshold switching (OTS)” selector device to control the open and shut of each memory unit. The physics of these materials, however, has not been well understood due to complex structure of chalcogenide glass. The authors focus on the defect states which are responsible for OTS behaviors via machine learning of the large amount of structure data. The physical origin of OTS is revealed and the properties of these materials can be predicted, paving the way for the materials design toward high‐density memory integration.

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Amorphous materials / Artificial neural networks / Bias / chalcogenide glass / Chalcogenides / Chips (memory devices) / Current leakage / Density / Electric fields / Electronic structure / First principles / Information storage / Machine learning / mid‐gap states / ovonic threshold switching / phase‐change memory / Random access memory / selector