The resistive random access memory(RRAM)has stimulated a variety of promising applications including programmable analog circuit,massive data storage,neuromorphic computing,etc.These new emerging applications have hug...The resistive random access memory(RRAM)has stimulated a variety of promising applications including programmable analog circuit,massive data storage,neuromorphic computing,etc.These new emerging applications have huge demands on high integration density and low power consumption.The cross-point configuration or passive array,which offers the smallest footprint of cell size and feasible capability of multi-layer stacking,has received broad attention from the research community.In such array,correct operation of reading and writing on a cell relies on effective elimination of the sneaking current coming from the neighboring cells.This target requires nonlinear I-V characteristics of the memory cell,which can be realized by either adding separate selector or developing implicit build-in nonlinear cells.The performance of a passive array largely depends on the cell nonlinearity,reliability,on/off ratio,line resistance,thermal coupling,etc.This article provides a comprehensive review on the progress achieved concerning 3D RRAM integration.First,the authors start with a brief overview of the associative problems in passive array and the category of 3D architectures.Next,the state of the arts on the development of various selector devices and self-selective cells are presented.Key parameters that influence the device nonlinearity and current density are outlined according to the corresponding working principles.Then,the reliability issues in 3D array are summarized in terms of uniformity,endurance,retention,and disturbance.Subsequently,scaling issue and thermal crosstalk in 3D memory array are thoroughly discussed,and applications of 3D RRAM beyond storage,such as neuromorphic computing and CMOL circuit are discussed later.Summary and outlooks are given in the final.展开更多
The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerg...The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.展开更多
基金the National Key R&D Program of China(Grant Nos.2018YFB0407501 and 2016YFA0201800)the National Natural Science Foundation of China(Grant Nos.61804173,61922083,61804167,61904200,and 61821091)the fourth China Association for Science and Technology Youth Talent Support Project(Grant No.2019QNRC001).
文摘The resistive random access memory(RRAM)has stimulated a variety of promising applications including programmable analog circuit,massive data storage,neuromorphic computing,etc.These new emerging applications have huge demands on high integration density and low power consumption.The cross-point configuration or passive array,which offers the smallest footprint of cell size and feasible capability of multi-layer stacking,has received broad attention from the research community.In such array,correct operation of reading and writing on a cell relies on effective elimination of the sneaking current coming from the neighboring cells.This target requires nonlinear I-V characteristics of the memory cell,which can be realized by either adding separate selector or developing implicit build-in nonlinear cells.The performance of a passive array largely depends on the cell nonlinearity,reliability,on/off ratio,line resistance,thermal coupling,etc.This article provides a comprehensive review on the progress achieved concerning 3D RRAM integration.First,the authors start with a brief overview of the associative problems in passive array and the category of 3D architectures.Next,the state of the arts on the development of various selector devices and self-selective cells are presented.Key parameters that influence the device nonlinearity and current density are outlined according to the corresponding working principles.Then,the reliability issues in 3D array are summarized in terms of uniformity,endurance,retention,and disturbance.Subsequently,scaling issue and thermal crosstalk in 3D memory array are thoroughly discussed,and applications of 3D RRAM beyond storage,such as neuromorphic computing and CMOL circuit are discussed later.Summary and outlooks are given in the final.
基金This work was supported in part by the Ministry of Science and Technology of China under Grant No.2017YFA0206102in part by the National Natural Science Foundation of China under Grant No.61922083+2 种基金by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDB44000000by the European Union’s Horizon 2020 Research and Innovation Program with Grant Agreement No.824164by the German Research Foundation Projects MemCrypto under Grant No.GZ:DU 1896/2-1 and MemDPU under Grant No.GZ:DU 1896/3-1.
文摘The spiking neural network(SNN),closely inspired by the human brain,is one of the most powerful platforms to enable highly efficient,low cost,and robust neuromorphic computations in hardware using traditional or emerging electron devices within an integrated system.In the hardware implementation,the building of artificial spiking neurons is fundamental for constructing the whole system.However,with the slowing down of Moore’s Law,the traditional complementary metal-oxide-semiconductor(CMOS)technology is gradually fading and is unable to meet the growing needs of neuromorphic computing.Besides,the existing artificial neuron circuits are complex owing to the limited bio-plausibility of CMOS devices.Memristors with volatile threshold switching(TS)behaviors and rich dynamics are promising candidates to emulate the biological spiking neurons beyond the CMOS technology and build high-efficient neuromorphic systems.Herein,the state-of-the-art about the fundamental knowledge of SNNs is reviewed.Moreover,we review the implementation of TS memristor-based neurons and their systems,and point out the challenges that should be further considered from devices to circuits in the system demonstrations.We hope that this review could provide clues and be helpful for the future development of neuromorphic computing with memristors.
