在現今，電阻式隨機存取記憶體（RRAM）因為具備高速操作、低寫入電壓、低製程溫度和易於微縮等許多優點，無疑是替代或補充NAND flash作為下一代非揮發性記憶體的新興候選者。而在人工智能與深度學習的時代，我們也進一步探索以RRAM作為神經形態陣列中的突觸單元。我們在N+四吋矽晶圓上以二氧化鉿(HfO2)作為金屬-絕緣體-金屬結構中的介電層，而且所製備的RRAM有著從最小2.56 μm2 到最大 16,000 μm2不同面積的元件。採用HfO2作為介電層的元件不僅具有雙極性電阻轉換特性，並且在施加連續脈衝下也實現了多重阻態的調變，因此適用於類神經形態的應用。此外我們也研究如何利用電極材料的選擇來穩定電阻的轉換。

Resistive random access memory (RRAM) is a promising candidate to replace or supplement NAND flash as next-generation non-volatile memory due to its high-speed operation, low programming voltage, potentially low processing temperature, and small form factor. In the era of artificial intelligence and deep learning, this thesis explores the feasibility of using RRAM as a synaptic element in a neuromorphic array. Metal-insulator-metal RRAM devices are fabricated with an HfO2 dielectric layer and various electrodes with an active area ranging from 2.56 to 16,000 μm2 on an n+ silicon wafer. Devices with an HfO2 gate dielectric not only exhibited bipolar resistive switching characteristics but also achieved multilevel resistance states with the application of consecutive pulses, making them suitable for neuromorphic applications. In addition, it is shown that the selection of electrode material is key to stabilizing resistive switching.

Chapter 1 references
[1] Chen, B., et al. "Physical mechanisms of endurance degradation in TMO-RRAM." Electron Devices Meeting (IEDM), 2011 IEEE International. IEEE, 2011.
[2] Yu, Shimeng. Resistive Random Access Memory (rram): From Devices to Array Architectures, 2016. Internet resource.

Chapter 2 references
[1] J. F. GIBBONS and W. E. BEADLE, “SWITCHING PROPERTIES OF THIN NiO FILMS”, Solid-State Electronics Pergamon Press 1964. Vol. 7, pp. 785-797.
[2] F. ABGALL, “SWITCHING PHENOMENA IN TITANIUM OXIDE THIN FILMS”, Solid-State Electronics Pergamon Press 1968. Vol. 11, pp. 535-541.
[3] Sergiu Clima, Bogdan Govoreanu, Malgorzata Jurczak, Geoffrey Pourtois, “HfOx as RRAM material – First principles insights on the working principles”, journal Microelectronic Engineering Volume 120, 25 May 2014, Pages 13-18.
[4] Amit Prakash, Debanjan Jana and Siddheswar Maikap, “TaOx-based resistive switching memories: prospective and challenges”, Nanoscale Research Letters 2013, 8:418 (2013).
[5] S. Yu, B. Lee, and H.-S. P. Wong, "Metal oxide resistive switching memory", in Functional Metal Oxide Nanostructures. New York: Springer-Verlag, 2011.
[6] Baek, I. G., et al. "Highly scalable nonvolatile resistive memory using simple binary oxide driven by asymmetric unipolar voltage pulses." Electron Devices Meeting, 2004. IEDM Technical Digest. IEEE International. IEEE, 2004.
[7] “Resistive switching in transition metal oxides”, Correlated Electron Research Center (CERC), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8562, Japan
[8] Pan F, Gao S, Chen C, Song C, Zeng F (2014) Recent progress in resistive random access memories: materials, switching mechanisms, and performance. Mater Sci Eng R Reports 83:1–59. doi:10.1016/j.mser.2014.06.002
[9] Qi-DanLinga1Der-JangLiawbChunxiangZhucDaniel Siu-HungChancEn-TangKangaKoon-GeeNeoha, “Polymer electronic memories: Materials, devices and mechanisms”, Polymer, vol. 33, no. 10, pp.917-978, Oct. 2008
[10] Ee Wah Lim and Razali Ismail, “Conduction Mechanism of Valence Change Resistive Switching Memory: A Survey”, Electronics 2015, 4, 586-613; doi:10.3390/electronics4030586

[11] Fu-Chien Chiu, “A Review on Conduction Mechanisms in Dielectric Films”, Advances in Materials Science and Engineering
Volume 2014, Article ID 578168, 18 pages.

Chapter 3 references
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[2] Titanium nitride – Wikiwand.
[3] Sohail Anwar, et al. "Nanotechnology for Telecommunications", CRC Press Page 453, 2010.
[4] plasma-therm –PECVD http://www.plasma-therm.com/pecvd.html.
[5] warwick-TEM https://warwick.ac.uk/fac/sci/physics/current/postgraduate/regs/mpagswarwick/ex5/techniques/structural/tem/.

Chapter 4 references
[1] B. Chen,, Y. Lu , B. Gao, Y.H. Fu, F.F. Zhang, P. Huang, Y.S. Chen, L.F. Liu, X.Y. Liu, J.F. Kang, Y.Y. Wang, Z. Fang, H.Y. Yu, X. Li, X.P. Wang, N. Singh, G. Q. Lo, D. L. Kwong, “Physical Mechanisms of Endurance Degradation in TMO-RRAM,” IEDM11 Pp. 283-286, 2011
[2] B. Gao, S. Yu, N. Xu, L.F. Liu, B. Sun, X.Y. Liu, R.Q. Han, J.F. Kang, B. Yu, Y.Y. Wang, “Oxide-based RRAM switching mechanism: A new ion-transport-recombination model,” IEDM Tech. Dig, p563, 2008.
[3] Kuan-Liang Lin, Tuo-Hung Hou, Jiann Shieh, Jun-Hung Lin, Cheng-Tung Chou, and Yao-Jen Lee, “Electrode dependence of filament formation in HfO2 resistive-switching memory,” Journal of App. Phys. 109, 084104 (2011).
[4] Geoffrey W. Burr, Robert M. Shelby, Abu Sebastian, Sangbum Kim, Seyoung Kim, Severin Sidler, Kumar Virwani, Masatoshi Ishii, Pritish Narayanan, Alessandro Fumarola, Lucas L. Sanches, Irem Boybat, Manuel Le Gallo, Kibong Moon, Jiyoo Woo, Hyunsang Hwang & Yusuf Leblebici, “Neuromorphic computing using non-volatile Memory”, ADVANCES IN PHYSICS: X, 2017 VOL. 2, NO. 1, 89–124.
[5] Shimeng Yu, Bin Gao, Zheng Fang, Hongyu Yu, Jinfeng Kang, and H.-S. Philip Wong, “A Neuromorphic Visual System Using RRAM Synaptic Devices with Sub-pJ Energy and Tolerance to Variability: Experimental Characterization and Large-Scale Modeling,” IEDM12 Pp. 240-242, 2012.
[6] D. D. Lu, F.-X. Liang, Y.-C. Wang, and H.-K. Zeng, “NVMLearn: A Simulation Platform for Non-Volatile-Memory-Based Deep Learning Hardware,” 2017 International Conference on Applied System Innovation, Sapporo, Japan.