臺灣博碩士論文加值系統

電阻式記憶體具有高密度、低功耗、操作速度快等優勢，更重要的是我們可以利用其可調變的阻值特性來仿效人類大腦在接收外部訊號後神經間突觸權重的變化，以實現能加速人工智慧運算的硬體類神經網路，因此在作為儲存記憶體與電子突觸元件上，電阻式記憶體都扮演著非常重要的角色。
在本文中我們探討氧化鉭基電阻式記憶體之轉換特性與類神經應用。第一部分，我們以鉭/氮化鉭作為氧化鉭基電阻式記憶體之上電極，製作出具有類比權重轉換特性的電阻式記憶體;第二部分，我們研究該元件之類神經特性，包含多重阻態轉換(Multi-level Cell)、突觸權重的增強(Potentiation)與抑制(Depression)行為，並嘗試改變鉭電極之厚度以優化元件特性;第三部分，由於氧化鋁有較低的吉布斯自由能（Gibbs Free Energy）與低離子移動速率之材料特性，我們透過添加一層氧化鋁薄膜讓元件在維持權重更新的線性度時還可大幅提升操作的穩定性，直流電壓操作下阻態轉換可達到104次，在室溫下維持其阻態而不變化長達104秒，元件的增強及抑制行為非線性度分別為2.41與2.77，至少可達到500個類比突觸權重值，且循環的增強及抑制行為可穩定操作高達500次，此改善使我們的元件更適合應用在類神經網路 ; 最後，我們討論該類比突觸元件在添加氧化鋁薄膜前後的電阻轉換機制，並分別建構出對應的導電絲示意圖。

Resistive random access memory (RRAM) is one of the most promising nonvolatile memory due to its low power consumption, high operation speed, and compatible with the conventional CMOS process. Most importantly, the synaptic plasticity of RRAM was explored for potential use as an electronic synapse for neuromorphic computing application.
In this thesis, we demonstrated a novel approach to optimize conductance tuning linearity and switching stability in TaOx based RRAM device. Firstly, we got the analog resistive switching behavior instead of binary switching by using Ta/TaN top electrode. Secondly, we investigated the synaptic properties including multilevel capability and nonlinearity of potentiation and depression behavior on TaN/Ta/TaOx/Pt stack, and engineered device with optimized Ta thickness. Thirdly, by inserting a 1 nm Al2O3 due to its lower Gibbs free energy and lower ion migration speed, the switching stability can be significantly improved while maintaining the high conductance tuning linearity. The DC endurance can up to 104 cycles. The nonlinearity of potentiation and depression are 2.41 and 2.77 with 500 conductance states and show 500 times repeated potentiation and depression cycles with a total of 500000 training pulses. It suggests the analog RRAM can potentially be used as an electronic synapse device for the emerging neuromorphic computation system. Finally, we proposed the switching mechanism and the schematic of filament growth and dissolution of this analog RRAM.

摘要 i
Abstract ii
Acknowledgement iii
Contents iv
Table captions vii
Figure captions viii
Chapter 1 Introduction x
1.1 Introduction to Randam Access Memory 1
1.2 Volatile Memory 1
1.2.1 DRAM 1
1.2.2 SRAM 2
1.3 Nonvolatile Memory 2
1.3.1 Flash Memory 3
1.3.2 MRAM 3
1.3.3 PCRAM 4
1.3.4 FeRAM 5
1.3.5 RRAM 5
1.4 RRAM-based Synapse Devices for neuromorphic Applications 6
1.4.1 Introduction to hardware neural networks 6
1.4.2 Requirements and limitations of the RRAM synapse device 8
1.4.3 Nonlinear Weight Update Model 8

Chapter 2 Mechanism and Opeation of RRAM for Neuromorphic Computing 16
2.1 Structure and Materials of RRAM 16
2.2 Electric Characteristics in RRAM 17
2.2.1 Forming Process 17
2.2.2 Set and Reset Process 18
2.2.3 Classification of Operation Mode 18
2.2.4 Potentiation and Depression 19
2.3 Resistive Switching Mechanism 20
2.3.1 Conductive Filament Model 20
2.3.2 Oxygen Vacancies Migration 20
2.3.3 Cation Migration 21
2.2.4 Joule Heating 22
Chapter 3 Experiment Details 26
3.1 Experiment Process Flow 26
3.2 Sample Fabrication 26
3.2.1Substrate Preparation 26
3.2.2 Fabrication of TiN/Ti/TaOx/Pt device 26
3.2.3 Fabrication of TaN/Ta/TaOx/Pt device 27
3.2.4 Fabrication of TaN/Ta/TaOx/Al2O3/Pt device 28
3.3 Electrical Measurement 28
3.3.1 Current-Voltage(I-V) Measurement 29
3.3.2 Endurance Test 29
3.3.3 Electrical-Pulse-Induced Resistance Switching Measurement 29
3.3.4 Data Retention Time Measurement 30

Chapter 4 Results and Discussion 33
4.1 Motivation 33
4.2 Electrical performances of TaOx-based RRAM Device with Different Top Electrode 34
4.3 Electrical performances of TaN/Ta/TaOx/Pt structure 35
4.4Electrical performances of TaN/Ta/TaOx/Al2O3/Pt structure with thin Al2O3 Film 36
4.5 Resistive Switching Mechanism of TaOx/Al2O3 bilayer RRAM Device 39
Chapter 5 Conclusions 53
References 54

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