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研究生:傅宇航
研究生(外文):Poh, Yu-Hang
論文名稱:氧化鋅透明電阻式記憶體應用於類神經突觸運算及光電特性
論文名稱(外文):Resistive Switching of Transparent Zinc Oxide for Brain-Inspired Computing and Optoelectronic Application
指導教授:曾俊元林詩淳林詩淳引用關係
指導教授(外文):Tseng, Tseung-YuenLin, Shih-Chun
口試委員:韋光華林群傑曾俊元
口試委員(外文):Wei, Kung-HwaLin, Chun-ChiehTseng, Tseung-Yuen
口試日期:2023-06-19
學位類別:碩士
校院名稱:國立陽明交通大學
系所名稱:電子研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2023
畢業學年度:111
語文別:英文
論文頁數:82
中文關鍵詞:非揮發電阻式記憶體馮·諾依曼瓶頸成對刺激加成脈衝時序依賴可塑性
外文關鍵詞:Non-Volatile RRAMVon-Neumann BottleneckPaired-Pulse FaciliationSpike-Timing-Dependent-PlasticityHop-Field Simulation(HNN)
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為了克服馮·諾依曼瓶頸對計算速度的限制,許多科學研究者廣泛研究了類神經網路計算。透過模仿人腦的計算架構,類神經網絡能夠執行大量的平行計算,因此可以提高計算速度及效率。而在衆多非揮發性記憶體中,電阻式記憶體(RRAM)擁有結構簡單,低功耗和CMOS兼容性高的優越特性,因此具有非常好的潛力成爲類神經突觸元件的候選人。
在本此實驗中我們先探討單層ITO/ZnO/ITO電阻式記憶體並嘗試改變製程氣體通量對電性的影響,實驗數據顯示在電性表現上還需進行進一步的改善。因此嘗試加入Al2O3使其形成雙層ITO/ZnO/Al2O3/ITO結構的元件。我們優化了Al2O3和ZnO的厚度來獲得最佳電阻轉換特性,結果表示在ITO/ZnO(35奈米)/Al2O3(3奈米)/ITO展現出最佳的DC電性表現例如1000倍的記憶窗口,可以重複操作10000次循環次數并且擁有最小的操作電壓。實現了低功耗且高穩定性的特性。我們嘗試在85攝氏度下量測元件的電阻值狀態,可以看到元件可以維持到104秒并且沒有任何崩潰現象,表現出了元件的非揮發性特性。在AC電性表現上的增强和抑制量測的非綫性度為0.73和1.3,元件可以訓練800個訓練循環周期共40萬個脈衝數。我們們嘗試引入Hop-Field simulation(HNN)架構來讓元件進行10 x 10像素的圖像辨識,在模擬結果當中發現到元件可以在進行19次迭代后成功達到100%辨識出輸入的圖形。此外元件也成功實現了脈衝時序依賴可塑性(STDP)的突觸模擬。在光學上,我們藉由405奈米波長的藍光對元件照光使其改變電阻值狀態,在量測結果中元件顯現出16個多重儲存狀態并且成功模擬了成對刺激加成(PPF)特性。最後我們藉由材料分析以及電流傳導機制來推出電阻轉換特性模型及導電絲斷裂及形成,來驗證所得的實驗數據。
In order to overcome the speed limitations imposed by the Von Neumann bottleneck, many researchers have extensively studied neural network computing. By mimicking the computational architecture of the human brain, neural networks can perform a large number of parallel computations, thereby improving computational speed and efficiency. Among the different types of non-volatile memories available, resistive random-access memory (RRAM) stands out due to its simple structure, low power consumption, and excellent compatibility with CMOS technology. These advantages position RRAM as a promising choice for neural synaptic elements.
In this experiment, we first investigated the single-layer ITO/ZnO/ITO resistive memory and attempted to change the effect of process gas flow on its electrical properties. The experimental data showed that further improvement was needed in the electrical performance. Therefore, we attempted to add Al2O3 to form a bi-layer ITO/ZnO/Al2O3/ITO structure for the device. We optimized the thickness of Al2O3 and ZnO to obtain the best resistive switching characteristics. The results showed that the ITO/ZnO(35 nm)/Al2O3(3 nm)/ITO exhibited the best DC electrical performance, such as a 1000 On/Off memory window ratio, 10,000 cycle endurance, and minimum operation voltage, achieving low power consumption and high stability. We attempted to measure the resistance state of the device at 85 degrees Celsius and found that the device could maintain up to 104s and showed non-volatile characteristics without any breakdown phenomenon. The enhancement and nonlinearity of AC electrical performance were measured to be 0.73 and 1.3, respectively, and the device could be trained for 800 training cycle periods with a total of 400,000 pulse numbers. We attempted to introduce the Hopfield simulation (HNN) framework to allow the device to perform 10x10 pixel image recognition. In the simulation results, we found that the device could successfully recognize the input image with 100% accuracy after 19 iterations. Additionally, the device successfully implemented spike-timing-dependent plasticity (STDP) synaptic simulation. In terms of optics, we used 405 nm blue light to illuminate the device and change its resistance state. In the measurement results, the device showed 16 multiple storage states and successfully simulated the paired-pulse facilitation (PPF) characteristic. Finally, we used material analysis and current conduction mechanism to deduce the resistance switching characteristic model and the formation and breakage of conductive filaments to verify the experimental data obtained.
摘要 i
Abstract iii
Acknowledgement v
Table Captions ix
Figure Captions x
Chapter 1 Introduction 1
1.1 Introduction of Neuromorphic Computing 1
1.2 Overview of Non-Volatile Emerging Memory 2
1.2.1 PCRAM 3
1.2.2 MRAM 3
1.2.3 FeRAM 4
1.2.4 RRAM 4
1.3 Different Types of RRAM 5
1.3.1 CBRAM 5
1.3.2 Oxide-RAM 6
1.4 Motivation 6
Chapter 2 Overviews of Electrical and Optical Characteristics for Synaptic RRAM 14
2.1 Synaptic RRAM 14
2.2 DC Electrical Characteristics in RRAM 15
2.2.1 Forming Voltage 15
2.2.2 Set and Reset Operation 15
2.2.3 Endurance 16
2.2.4 Resistive Memory Window 16
2.2.5 Retention 16
2.3 AC Electrical Characteristics in Synaptic RRAM 17
2.3.1 Potentiation and Depression 17
2.3.2 Nonlinearity 18
2.3.3 Spike-Timing-Dependent-Plasticity 18
2.4 Optoelectronic Characteristics in Synaptic RRAM 18
2.4.1 Pair-Pulsed Facilitation (PPF) 19
2.5 Conduction Mechanism of RRAM 19
Chapter 3 Experimental Details 25
3.1 Experiment Process Flow 25
3.2 Sample fabrication 25
3.2.1 Substrate Preparation 25
3.2.2 Fabrication Process Flow of ITO/ZnO/ITO Device 25
3.2.3 Fabrication Process Flow of ITO/ZnO/Al2O3/ITO Bilayer Device 26
3.3 Measurement Method and System 26
3.4 DC electrical measurement System and Setup 27
3.4.1 Current-Voltage Test 27
3.4.2 Endurance Test 27
3.4.3 Retention Test 28
3.5 AC Electrical Measurement System and Setup 28
3.5.1 Potentiation and Depression Test 28
3.5.2 Synaptic Weight Modulation Nonlinearity Test 29
3.5.3 Hopfield Neural Network Simulation 30
3.5.4 Spike-Timing-Dependent-Plasticity Test 30
3.6 Optical Measurement System and Setup 31
3.6.1 Transparency Test 31
3.6.2 Light Illumination Induced Current Change 32
3.6.3 Paired-pulse Facilitation (PPF) Test 32
3.6.4 Forgetting Behavior 33
Chapter 4 Results and Discussion 36
4.1 DC Electrical Performances of ITO/ZnO/ITO with Different Sputtering Gas Flow 36
4.2 DC Electrical Performances of ITO/ZnO/Al2O3/ITO with Different Al2O3 layer Thicknesses 37
4.3 DC Electrical Performances of ITO/ZnO/Al2O3/ITO with Different ZnO layer Thicknesses 38
4.4 AC Electrical Performances of ITO/ZnO/Al2O3/ITO with Different ZnO layer Thicknesses 39
4.5 Further Electrical Analysis of Optimal Thickness ITO/ZnO/Al2O3/ITO RRAM Device 40
4.5.1 Retention 40
4.5.2 Multilevel Cell Storage Capacity 40
4.5.3 Current Conduction Mechanism Analysis 41
4.6 Synaptic Application Performances of Optimal Thickness ITO/ZnO/Al2O3/ITO RRAM Device 42
4.6.1 STDP Characteristics 42
4.6.2 Image Recognition by Hop-field Simulation 43
4.7 Optical Performances of Optimal Thickness ITO/ZnO/ Al2O3/ITO Optoelectronic RRAM Device 44
4.7.1 Transparency Result 45
4.7.2 Light Induced Current Change 45
4.7.3 Optical Paired-Pulse Facilitation (PPF) 46
4.7.4 Forgetting Behavior 46
4.8 Material Analysis of Optimal Thickness ITO/ZnO/ Al2O3/ITO RRAM Device 47
4.9 Resistive Switching Model 47
Chapter 5 Conclusions & Future Works 73
References 75
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