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Investigation of Resistive Switching Device for Memory and Neuromorphic applications

저항변화 소자의 메모리 및 뉴론몰픽 응용에 대한 연구

이대석 (Daeseok Lee)

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초록 moremore
Typical charge-storage based memories such as NAND or NOR Flash memory have faced limitations in a device scaling respect. To overcome the limitations, several future non-volatile memories: phase-change memory (PRAM), magnetic memory (MRAM), and resistive memory (ReRAM) have been proposed as al...
Typical charge-storage based memories such as NAND or NOR Flash memory have faced limitations in a device scaling respect. To overcome the limitations, several future non-volatile memories: phase-change memory (PRAM), magnetic memory (MRAM), and resistive memory (ReRAM) have been proposed as alternative techniques. Among those candidates, especially, the ReRAM has been extensively investigated on the basis of its various advantages (simple metal-insulator-metal structure, low power consumption, fast operation, and CMOS compatibility). However, one main obstacle is the variability of switching parameters, which can be a critical drawback for the future non-volatile memory applications. The randomly generated conducting filament, during switching operations, is considered as a dominant origin of the switching variability. Thus, in this thesis, as the first section, the author investigated several approaches such as a defect engineering, device optimization, and structural engineering to maximize the advantages and minimize the disadvantages (mainly switching variability). In addition, for a feasibility of high density integration of the ReRAM, novel two terminal switch device was developed to prevent the leakage-current which can lead to a misoperation in cross-point array structure. Furthermore, in the second section, new application of the ReRAM: neuromorphic application that is a novel computing process for complex and huge amount of input was researched. To realize the neuromorphic application, the ReRAM needs to be modified for achieving a brain-inspired synaptic characteristics such as non-volatile behaviour, analogue, and symmetric conductance change. To achieve the synaptic characteristics from the ReRAM, the author optimized Pr0.7Ca0.3MnO3 (PCMO) based ReRAM, then the author demonstrated the feasibility of neuromorphic application by an identification of rat’s fear-state neural signal. To identify the rat’s neural signals, the neuromorphic system composed of a multi-layer artificial neural network and the optimized PCMO based ReRAM was simulated on the basis of normal- and fear-state neural signals. Consequently, the rat’s neural signals were clearly identified by proposed compensational circuit which can improve the synaptic characteristics of the PCMO based ReRAM. All of the results, in this thesis, obviously demonstrated the feasibilities of ReRAM not only in the memory application but also in the neuromorphic application.
목차 moremore
Abstract 1
Contents 3
List of Figures and Tables 6
...
Abstract 1
Contents 3
List of Figures and Tables 6
I. Introduction 11
1.1 Resistive switching in device scaling respect 11
II. Resistive switching device for memory application 13
2.1 Switching reliability 13
2.1.1 Switching variability in scaled device 13
2.1.1.1 Introduction 13
2.1.1.2 Experiments 14
2.1.1.3 Results and discussion 15
2.1.1.4 Conclusion 17
2.1.2 Defect engineering 20
2.1.2.1 Introduction 20
2.1.2.2 Experiments 21
2.1.2.3 Results and discussion 22
2.1.2.4 Conclusion 24
2.1.3 Lightning rod effect 27
2.1.3.1 Introduction 27
2.1.3.2 Experiments 28
2.1.3.3 Results and discussion 28
2.1.3.4 Conclusion 30
2.1.4 Dependence of reactive top electrode 33
2.1.4.1 Introduction 34
2.1.4.2 Experiments 35
2.1.4.3 Results and discussion 35
2.1.4.4 Conclusion 36
2.2 Two terminal selection device 41
2.2.1 Introduction 41
2.2.2 Complementary resistive switching 44
2.2.2.1 Introduction 44
2.2.2.2 Experiments 45
2.2.2.3 Results and discussion 45
2.2.2.4 Conclusion 49
2.2.3 Metal-Insulator Transition based selector 52
2.2.3.1 Introduction 52
2.2.3.2 Results 52
2.2.3.3 TixOy-based MIT switching device 53
2.2.3.4 Structural engineering: localization of transition region and oxygen ratio control 54
2.2.3.5 Realization of 3-D high-density switching device and 1S1R characteristics 57
2.2.3.6 Measurement 59
2.2.3.7 Preparation of memory and switching devices 59
III. Resistive switching device for neuromorphic application 75
3.1 Pr0.7Ca0.3MnO3 based synapse device 75
3.1.1 Introduction 75
3.1.2 Experiments 77
3.1.3 Results and discussion 78
3.1.4 Conclusion 79
3.2 Real time fear-state memory identification 80
3.2.1 Introduction 80
3.2.2 Identification of Local Field Potential (LFP) 83
3.2.3 LFP recording based on the contextual fear conditioning 84
3.2.4 Hardware implementation of ANN 85
3.2.5 Fear-memory identification among different rats 87
3.2.6 Animal experiments: subjects and surgery 88
3.2.7 Neural network 89
3.2.8 Preparation of synapse devices 89
IV. Conclusion 104
V. 요 약 문 107
VI. References 109
VII. Acknowledgements 129
VIII. Curriculum Vitae 130