隨著資訊時代的來臨，我們需要透過記憶體來儲存大量的資料，因此非揮發性記憶體在我們日常生活中扮演著不可或缺的角色，舉凡手機、相機、電腦以及固態硬碟裡，都需要具備非揮發性記憶體來幫助我們保存資料，而目前市場上所廣泛流通的非揮發性記憶體主要以電荷儲存式的快閃記憶體為主，但隨著產業持續微縮的發展狀況下，這種快閃式記憶體面臨了物理極限的問題，因此研究新式的非揮發性記憶體變得相當重要，而新式的非揮發性記憶體包含磁阻式記憶體(Magnetoresistive Random Access Memory, MRAM)、相變化(Phase Change Random Access Memory, PCRAM)以及此次研究重點電阻式記憶體(Resistive Random Access Memory, RRAM)和鐵電記憶體(Ferroelectric Random Access Memory, FeRAM)這四類，來達到高效能、低功率消耗、低操作電壓以及面積小的優勢。
電阻式記憶體除了可以作為傳統的記憶體來使用外，亦可以結合人工智慧運算來使的該結構應用在硬體類神經網絡上，透過模擬人類大腦中的網絡結構，來建立下一世代電腦處理系統的模式，但，單純的電晶體結構無法達到仿生系統(neuromorphic system)的效果，因此有研究團隊嘗試藉由嵌入電阻式記憶體來做為突觸元件，來克服龐大運算以及高密度神經網絡的問題，使元件能具有像神經元一樣可調整突觸權重值(亦稱為突觸可塑性, synaptic plasticity)的能力，顯現海伯學習法(Hebb's learning)中重要的短期記憶(STM)、長期記憶(LTM)仿生特性。
在本篇論文中，我們透過將鎳金屬奈米點鑲嵌於二氧化鉿介電層裡來達到高效能的RRAM結構，透過這個方法來提升鎳金屬奈米點附近的有效電場，來使的絕緣層中的阻絲(Conductance Filaments, CF)更容易地被形成以及打斷，同時我們也透過Centaurus 3D TCAD模擬軟體觀察到電場增強的現象。本篇論文成功的使鎳金屬奈米點具有極低的操作電壓(±2V)、高切換速度(60ns)、高達350倍的高低阻抗比(High Resistance State /Low Resistance State>350, HRS/LRS>350)、高可靠度、良好的均勻性以及6個Bits (64 states)的記憶能力，同時，我們也有對該元件的絕緣層電子導通機制進行探討，並以未摻雜的純二氧化鉿絕緣層結構作為對照組，來比較改良後的元件特性優劣。同時我們也對具有量產潛力的FeFET結構進行初步的探討，並結合現行的鰭式電晶體結構(FinFET)，在該結構中，其記憶體儲存空間(Memory Window, MW)超過1V，而寫入抹除的速度則高達100ns。
此研究中的鑲嵌鎳金屬奈米點結構具有低電壓、高速運算、高可靠度以及高記憶能力的效果，相當適合應用在類神經網絡系統上，但在製程所需的退火溫度上，仍有可改善的空間，可以透過嘗試不一樣的金屬奈米點材料，來進一步的優化該結構，使該結構在未來的類神經應用上，扮演重要的角色，而我們所研究的FeFET具備的高速運算能力，也是一個值得深入探討的研究方向。

With the advantage of the information age, we need to store a large amount of data through memory devices. Therefore, non-volatile memory (NVM) plays an indispensable role in our daily lives. For example, mobile phones, cameras, computers, and solid-state devices. All need to have non-volatile memory to help us with saving data. The main type of the NVM currently widely be circulated in the market and surround our life is the charge storage flash memory. But, as the industry rapidly develops, this kind of flash memory faces a big problem of physical limitations. Thus, it is very important to study a new type of NVM. The new type of NVM includes magneto resistive random access memory (MRAM), phase-change random access memory (PCRAM), resistive random access memory (RRAM), and ferroelectric random access memory (FeRAM), which are the main focus of this research, is to achieve the advantages of high efficiency, low power consumption, low operating voltage and reduce the area.
RRAM can not only be used as a traditional memory but also can be combined with artificial intelligence to the application on the hardware-based neural network system. By imitating the network structure of the human brain, the computing model of the next-generation computers can be built. However, only the simple CMOS-based transistor structures cannot achieve the effect of the neuromorphic system. Therefore, some researchers tried to overcome the problems of large calculations and high-density neural networks by embedding RRAM as a synaptic component. Enables the component to have the ability to adjust the synaptic weight value to shows the important bionic characteristics of short-term memory (STM) and long-term memory (LTM) in Hebb's learning.
In this work, first, we present a novel nickel (Ni) nanocrystals (NCs) device by inserting NCs in the HfO2 layer to achieve a high efficiency resistive random access memory (RRAM). This result arises from the fact that the enhanced electric field intensity near Ni NCs, evidenced by the TCAD simulation, can effectively facilitate the formation and rupture of the conductive filaments (CF). The device operates at a low bias (±2V), fast switching (60ns), the high resistance state (HRS)/low resistance state (LRS) ratio can up to 350, high reliability, good uniformity, and multi-bits function (6-bits). The current fitting method of the Ni NCs device was also used to observe the carrier transport mechanisms. At the same time, the control sample of the pure HfO2 device (HfO2 device) is also made to compare the pros and cons. At the same time, we have also a preliminary discussion of the ferroelectric FinFET memory structure, which bears high production potential. Its memory window (MW) has >1V, and the speed of the program and erase is as high as 100ns.
In this research, the Ni NCs device has the advantage of low operation voltage, high switching speed, high reliability, and multi-bits function. Thus, it is very suitable for the application of the neuron network systems. However, the thermal budget during the process still can be further improved by using different materials to form the metal NCs for future application on neuron systems. The high-speed computing capability of ferroelectric FinFET memory we studied is also a research direction worthy of in-depth discussion.

Chapter 1
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Chapter 4
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Chapter 5
[5-1] Mulaosmanovic, H., Dünkel, S., Müller, J., Trentzsch, M., Beyer, S., Breyer, E. T., Mikolajick, T. and Slesazeck, S., "Impact of Read Operation on the Performance of HfO2-Based Ferroelectric FETs", IEEE Electron Device Letters. Vol. 41, (2020) pp. 1420-1423.
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