Ming Xu

Amorphous System and Theory

Huazhong University of Science and Technology

School of Integrated Circuits, Huazhong University of Science and Technology 430074, Wuhan, China

Email: mxu@hust.edu.cn

Biography

  Dr. Ming Xu is a full professor in Huazhong University of Science and Technology (HUST) and the department chair of microelectronics. He received his bachelor/master degree in Fudan University in China and got PhD degree in the Johns Hopkins University in USA. Sponsored by the Humboldt foundation, he worked as a postdoc scholar in RWTH Aachen University in Germany, and then joined HUST as a faculty in 2016. Dr. Xu has focused on chalcogenide memory materials, devices and integrations for almost 20 years. He is particularly interested in the exploration of the physics and mechanisms in these materials. He has published over 80 journal papers and owns 20 patents in this area.

Abstract for Presentation

Defect states in chalcogenide glass recognized by machine learning for 3D memory integration

   The recent development of 3D 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. 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. [1-2]

Figure 1. The defect states recognized using machine learning method in chalcogenide glass GeSe.

References 
[1] J. Wang et.al., Nat.Comm., 12, (2021) 58.
[2] A. J. Kronemeijer et.al., Phys. Rev. Lett., 105, 156604 (2010)