High-Throughput Computational Screening And Characterization Of Phase-Change Information Storage Semiconductors

At present,the information age has arrived,and people all over the world can quickly communicate in the form of video,pictures,voice and so on.In addition,with the deployment of mobile communications,people can easily use electronic devices such as smart phones,computers,tablets and other tools for online shopping,social media,video websites and other digital life.This has resulted in an exponential increase in the amount of electronic information generated by electronic devices.The storage,transmission,and application development of massive information pose severe challenges to current storage technologies,which require researchers to develop new types of high-density,low-power,and high-speed non-volatile memory to meet this challenge.Among many new memories,phase change memory technology is one of the most promising new memory technologies of the new generation.After decades of development,its process and technology have gradually matured and commercial products have been launched,and it also has excellent potential for applications such as breaking the von Neumann bottleneck in the post-Moore era.Although phase-change memory technology has many potentials and development,there are still many problems that have not been solved,such as the need to develop phase-change memory materials for some specific needs;the lack of perfect research on phase-change memory mechanism affects the further optimization of devices.In this paper,from first-principles calculations based on density functional theory to high-throughput screening technology,to machine learning interatomic potential technology,and from multiple technical means to study the materials,structures,mechanisms,etc.It will promote the development of new phase change memory materials and the application of phase change memory.This is the largest phase change storage high-throughput screening work in the world so far.Starting from 120,000 inorganic crystal materials and using appropriate screening criteria,we finally obtained 158 phase change storage candidate materials,and with further Phase-change memory performance analysis,we obtained 52 materials(including Ge-Sb-Te alloys)and Ge-Sb-Te alloys with similar properties.Similar to Ge-Sb-Te alloys,about 68%of them have not been reported.In addition,we also obtained materials with similar cohesive energy to GST,including Ag Bi Te₂,Tl Sb Te₂,As₂Te₃,Tl Bi Te₂,etc.,which indicates that these materials may have low power consumption performance and propose new possibilities for solving the power consumption problem of phase change memory.After that,we verified the properties of amorphous structure,signal contrast,and nucleation/crystallization ability of some unreported materials:Cd Pb₃Se₄,Tl Bi Te₂ and Mn Bi₂Te₄.Finally,various verifications show that our screening strategy is reliable,and this work will provide a new road for the further development of phase change memory materials.In addition,we apply the Gaussian approximation potential in the machine learning interatomic potential to simulate the large-scale phase change storage process.When simulating the reset process of the Ge₂Sb₂Te₅ phase change storage material,an experimentally observable mushroom-like structure can be observed.Further study of its partial melting process found that the partial melting process of the phase change memory material is limited,and the partial melting region is melted shell by shell.In addition,we also simulated and found the concept of disordered critical radius in the melting process.When the melting region is larger than the disordered critical radius,an amorphous structure can be obtained.This concept will have an important impact on the low-dimensional application of phase change storage.The above work is mainly devoted to the research in the field of phase change storage,which is helpful to meet the challenges of storage requirements in the current data era.Phase-change storage technology has also shown excellent potential in various fields such as brain-like computing and memory computing,and is one of the most promising new storage technologies at present.