Today’s explosion of data urgently requires memory technologies capable of storing large volumes of data in shorter time frames,a feat unattain-able with Flash or DRAM.Intel Optane,commonly referred to as three-dimen...Today’s explosion of data urgently requires memory technologies capable of storing large volumes of data in shorter time frames,a feat unattain-able with Flash or DRAM.Intel Optane,commonly referred to as three-dimensional phase change memory,stands out as one of the most promising candidates.The Optane with cross-point architecture is constructed through layering a storage element and a selector known as the ovonic threshold switch(OTS).The OTS device,which employs chalcogenide film,has thereby gathered increased attention in recent years.In this paper,we begin by providing a brief introduction to the discovery process of the OTS phenomenon.Subsequently,we summarize the key elec-trical parameters of OTS devices and delve into recent explorations of OTS materials,which are categorized as Se-based,Te-based,and S-based material systems.Furthermore,we discuss various models for the OTS switching mechanism,including field-induced nucleation model,as well as several carrier injection models.Additionally,we review the progress and innovations in OTS mechanism research.Finally,we highlight the successful application of OTS devices in three-dimensional high-density memory and offer insights into their promising performance and extensive prospects in emerging applications,such as self-selecting memory and neuromorphic computing.展开更多
Selector devices are indispensable components of large-scale memristor array systems.The thereinto,ovonic threshold switching(OTS)selector is one of the most suitable candidates for selector devices,owing to its high ...Selector devices are indispensable components of large-scale memristor array systems.The thereinto,ovonic threshold switching(OTS)selector is one of the most suitable candidates for selector devices,owing to its high selectivity and scalability.However,OTS selectors suffer from poor endurance and stability which are persistent tricky problems for applica-tion.Here,we report on a multilayer OTS selector based on simple GeSe and doped-GeSe.The experimental results show im-proving selector performed extraordinary endurance up to 1010 and the fluctuation of threshold voltage is 2.5%.The reason for the improvement may lie in more interface states which strengthen the interaction among individual layers.These develop-ments pave the way towards tuning a new class of OTS materials engineering,ensuring improvement of electrical perform-ance.展开更多
Chalcogenide glass has a unique volatile transition between high‐and low‐resistance states under an electric field,a phenomenon termed ovonic threshold switching(OTS).This characteristic is extensively uti-lized in ...Chalcogenide glass has a unique volatile transition between high‐and low‐resistance states under an electric field,a phenomenon termed ovonic threshold switching(OTS).This characteristic is extensively uti-lized in various electronic memory and computational devices,particu-larly as selectors for cross‐point memory architectures.Despite its advantages,the material is susceptible to glass relaxation,which can result in substantial drifts in threshold voltage and a decline in off‐current performance over successive operational cycles or long storage time.In this study,we introduce an OTS device made from stoichio-metric Sb_(2)Se_(3) glass,which retains an octahedral local structure within its amorphous matrix.This innovative material exhibits outstanding OTS capabilities,maintaining minimal degradation despite undergoing over 10^(7) operating cycles.Via comprehensive first‐principles calculations,our findings indicate that the mid‐gap states in amorphous Sb_(2)Se_(3) predom-inantly stem from the atomic chains characterized by heteropolar Sb‐Se bonds.These bonds exhibit remarkable stability,showing minimal alteration over time,thereby contributing to the overall durability and consistent performance of the material.Our findings not only shed light on the complex physical origins that govern the OTS behavior but also lay the groundwork for creating or optimizing innovative electrical switching materials.展开更多
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 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.展开更多
基金M.Zhu acknowledges support by the National Outstanding Youth Program(62322411)the Hundred Talents Program(Chinese Academy of Sciences)+1 种基金the Shanghai Rising-Star Program(21QA1410800)The financial support was provided by the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB44010200).
文摘Today’s explosion of data urgently requires memory technologies capable of storing large volumes of data in shorter time frames,a feat unattain-able with Flash or DRAM.Intel Optane,commonly referred to as three-dimensional phase change memory,stands out as one of the most promising candidates.The Optane with cross-point architecture is constructed through layering a storage element and a selector known as the ovonic threshold switch(OTS).The OTS device,which employs chalcogenide film,has thereby gathered increased attention in recent years.In this paper,we begin by providing a brief introduction to the discovery process of the OTS phenomenon.Subsequently,we summarize the key elec-trical parameters of OTS devices and delve into recent explorations of OTS materials,which are categorized as Se-based,Te-based,and S-based material systems.Furthermore,we discuss various models for the OTS switching mechanism,including field-induced nucleation model,as well as several carrier injection models.Additionally,we review the progress and innovations in OTS mechanism research.Finally,we highlight the successful application of OTS devices in three-dimensional high-density memory and offer insights into their promising performance and extensive prospects in emerging applications,such as self-selecting memory and neuromorphic computing.
基金supported by National Natural Science Foundation of China(Grant Nos.61974164,62074166,61804181,62004219,and 6200422).
文摘Selector devices are indispensable components of large-scale memristor array systems.The thereinto,ovonic threshold switching(OTS)selector is one of the most suitable candidates for selector devices,owing to its high selectivity and scalability.However,OTS selectors suffer from poor endurance and stability which are persistent tricky problems for applica-tion.Here,we report on a multilayer OTS selector based on simple GeSe and doped-GeSe.The experimental results show im-proving selector performed extraordinary endurance up to 1010 and the fluctuation of threshold voltage is 2.5%.The reason for the improvement may lie in more interface states which strengthen the interaction among individual layers.These develop-ments pave the way towards tuning a new class of OTS materials engineering,ensuring improvement of electrical perform-ance.
基金National Natural Science Foundation of China,Grant/Award Number:62174060National Science and Technology Major Project of China,Grant/Award Number:2022ZD0117600Hubei Key Laboratory of Advanced Memory,and the Fundamental Research Funds for the Central Universities,Grant/Award Number:2021GCRC051。
文摘Chalcogenide glass has a unique volatile transition between high‐and low‐resistance states under an electric field,a phenomenon termed ovonic threshold switching(OTS).This characteristic is extensively uti-lized in various electronic memory and computational devices,particu-larly as selectors for cross‐point memory architectures.Despite its advantages,the material is susceptible to glass relaxation,which can result in substantial drifts in threshold voltage and a decline in off‐current performance over successive operational cycles or long storage time.In this study,we introduce an OTS device made from stoichio-metric Sb_(2)Se_(3) glass,which retains an octahedral local structure within its amorphous matrix.This innovative material exhibits outstanding OTS capabilities,maintaining minimal degradation despite undergoing over 10^(7) operating cycles.Via comprehensive first‐principles calculations,our findings indicate that the mid‐gap states in amorphous Sb_(2)Se_(3) predom-inantly stem from the atomic chains characterized by heteropolar Sb‐Se bonds.These bonds exhibit remarkable stability,showing minimal alteration over time,thereby contributing to the overall durability and consistent performance of the material.Our findings not only shed light on the complex physical origins that govern the OTS behavior but also lay the groundwork for creating or optimizing innovative electrical switching materials.
基金National Key R&D Plan of China(Grant No.2019YFB2205100,2017YFB0701700)National Science and Technology Major Project of China(Grant No.2017ZX02301007-002)+2 种基金National Natural Science Foundation of China(Grant No.62174060)Fundamental Research Funds for the Central Universities,HUST(No.2021GCRC051)Hubei Key Laboratory of Advanced Memories.
文摘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.
