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研究生: 張劭華
Chang, Shao-Hua
論文名稱: 鐵電電晶體以等脈衝編程之神經網路與極化輔助脫陷阱
Identical Pulse Programming Based Neural Network of FeFET and Polarization Assisted De-Trapping
指導教授: 李敏鴻
Lee, Min-Hung
口試委員: 張智勝 陳自強
口試日期: 2021/06/17
學位類別: 碩士
Master
系所名稱: 光電工程研究所
Graduate Institute of Electro-Optical Engineering
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 47
中文關鍵詞: 氧化鉿鋯鐵電電晶體介電層調變記憶窗機器學習
英文關鍵詞: HfZrO2, Fe-FET, Dielectric, Memory window, Machine learning
研究方法: 實驗設計法主題分析
DOI URL: http://doi.org/10.6345/NTNU202100628
論文種類: 學術論文
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  • 近幾年來半導體的領域中,鐵電材料與現今CMOS 製程相容,是非常熱門的研究主題,而鐵電氧化鉿鋯(HfZrO2, HZO)也已被廣泛的利用。本論文系統地研究了用於增強(Potentiation)/ 抑制(Depression)機器學習的等脈衝編程,以使用5nm厚的HZO鐵電電晶體實現非線性度(α_P= 1.25和α_D = -3.69)和高電導比率(> 103x)。顯示出電導比率和線性之間的權衡特性。記憶窗(memory window, MW)增強的較高剩餘極化(Pr)將導致電導比率增加,但會降低訓練曲線的線性度。對於HZO厚度從15nm到5nm的HZO,以50ns的脈衝寬度和較低的脈衝電壓執行等脈衝的優化學習條件。這些突出的優點為將來記憶體內計算(computing-in-memory, CIM)應用程序等新興應用提供了機會。
    而記憶窗作為主要決定因子,我們也嘗試透過介電層(dielectric, DE)與鐵電層相對位置調變,由量測結果發現上層的DE與下層DE相比有較大的記憶窗,且2階的資料保存能力達103秒無衰退現象,可重複操作次數超過105個cycles。在確立了其元件的特性後,我們嘗試以不同的脈衝條件,令元件達成機器學習的效果。鐵電電晶體的學習曲線具有良好的線性與對稱性。
    雙層鐵電電晶體(double-layer ferroelectric, DFE),以改善機器學習中權重更新的非線性。雙層非對稱之FeFET(HZO 5nm / Al2O3 0.5nm / HZO 10nm)具有2.3 V的記憶窗,可操作 >105的耐久性,並可保持 >104秒。FeFET新型極化輔助脫陷阱(polarization assisted de-trapping, PAD)方法,增強型學習曲線的非線性度(αp)可以降低到0.07。

    In recent years, ferroelectric materials have attracted growing interest and have been extensively investigated to leverage state-of-the-art CMOS architectures. Identical pulse stimulation for Potentiation/Depression machine learning is investigated systemically to achieve linear (αP = 1.25 and αD = -3.69) and high conductance ratio (>103x) with 5nm-thick HfZrO2 (HZO) FeFET. The trade-off characteristics between conductance ratio and linearity is exhibited. The higher remnant polarization (Pr) for memory window (MW) enhancement would lead increasing conductance ratio, but degrades linearity of training curve. The optimized stimulation condition for identical pulse is performed with pulse width 50 ns and low access voltage for HZO thicknesses from 15 nm down to 5 nm. These highlight merits provide opportunity to integrate emerging devices such as computing-in-memory (CIM) application in the future. We also modulated the ferroelectric layer position through dielectric layer (DE). The data retention >103 sec and endurance >105 cycles are obtained. The deep leaning is also performed by pulse modulation, and the excellent linearity is obtained.
    Double-layer ferroelectric (DFE) FeFET is studied for improving the non-linearity of weight update in machine learning. The proposed gate stack (HZO 5nm/ Al2O3 0.5nm/ HZO 10nm) of FeFET exhibits memory window of 2.3 V, resulting in 2 states with the endurance of 105 and the retention of 104 s. Non-linearity fitting parameter (αp) of Potentiation conductance of DFE FeFET by the novel polarization assisted de-trapping (PAD) could be reduced to 0.07.

    第1章 緒論 1 1-1 鐵電材料簡介 1 1-2 鐵電材料的應用 4 1-3 論文架構 5 第2章 單層鐵電電晶體之等脈衝激發操作 6 2-1 簡介 6 2-2 單層鐵電電晶體製程 6 2-3 量測設定及方式 7 2-3-1 量測機台簡介 7 2-3-2 模組編輯 9 2-4 實驗結果 12 2-5 結果討論與分析 17 第3章 介電層與鐵電層相對位置調變 18 3-1 簡介 18 3-2 夾介電層之鐵電電晶體製程 21 3-3 實驗結果 22 3-4 結果討論與分析 28 第4章 雙層鐵電電晶體之機器學習 29 4-1 簡介 29 4-2 雙層鐵電電晶體製程 29 4-3 雙層鐵電電晶體之機器學習 30 4-3-1 極化輔助脫陷阱應用 35 4-4 結果討論及分析 38 第5章 總結與未來發展 39 5-1 總結 39 5-2 未來發展 40 參考文獻 41

    [1] T. Boescke, J. Heitmann, U. Schroder, “Integrated circuit with dielectric layer, ” US 7,709,359 B2, 2010 (Filing date 2007-09-05).
    [2] T. S. Böscke, St. Teichert, D. Bräuhaus, J. Müller, U. Schröder, U. Böttger and T. Mikolajick, “Phase Transitions in Ferroelectric Silicon Doped Hafnium Oxide, ’’ Appl. Phys. Lett., vol. 99, 2011, Art. no. 112904.
    [3] J. Müller, T. S. Böscke, D. Bräuhaus, U. Schröder, U. Böttger, J. Sundqvist, P. Kücher, T. Mikolajick, and L. Frey, “Ferroelectric Zr0.5Hf0.5O2 Thin Films for Nonvolatile Memory Applications, ’’ Appl. Phys. Lett., vol. 99, 2011, Art. no. 112901.
    [4] J. Müller, U. Schröder, T. S. Böscke, I. Müller, U. Böttger, L. Wilde, J. Sundqvist, M. Lemberger, P. Kücher, T. Mikolajick, and L. Frey, “Ferroelectricity in Yttrium-doped Hafnium Oxide, ’’ Appl. Phys. Lett., vol. 110, 2011, Art. no. 114113.
    [5] S. Müller, J. Müller, A. Singh, S. Riede, J. Sundqvist, U. Schroeder and T. Mikolajick, “Incipient Ferroelectricity in Al-Doped HfO2 Thin Films, ’’ Advanced Functional Materials, vol. 22, no. 11, pp. 2412-2417, 2012.
    [6] T. Schenk, S. Mueller, U. Schroeder, R. Materlik, A. Kersch, M. Popovici, C. Adelmann, S. V. Elshocht and T. Mikolajick, “Strontium Doped Hafnium Oxide Thin Films: Wide Process Window for Ferroelectric Memories. ” European Solid-State Device Research Conference, Set. 2013, pp. 260-263.
    [7] A. G. Chernikova, D. S. Kuzmichev, D. V. Negrov, M. G. Kozodaev, S. N. Polyakov, and A. M. Markeev., “Ferroelectric Properties of Full Plasma-Enhanced ALD TiN/La:HfO2/TiN Stacks, ” Appl. Phys. Lett., vol. 108, 2016, Art. no. 242905.
    [8] M. Hoffmann, Franz P. G. Fengler, M. Herzig, T. Mittmann, B. Max, U. Schroeder, R. Negrea, P. Lucian, S. Slesazeck and T. Mikolajick, “Unveiling the Double-Well Energy Landscape in a Ferroelectric Layer, ” Nano Letters, vol. 565, pp. 463-467, 2019.
    [9] T. S. Bösckea, J. Müllerb, D. Bräuhausc, U. Schröderd, and U. Böttgerc, “Ferroelectricity in Hafnium Oxide: CMOS Compatible Ferroelectric Field Effect Transistors, ” in IEDM Tech. Dig., Dec. 2011, pp. 547-550.
    [10] K. Ni, M. Jerry, J. A. Smith, and S. Datta, “A Circuit Compatible Accurate Compact Model for Ferroelectric-FETs, ” in VLSI Technology Symp., 2018, pp. 131-132.
    [11] S. Müller, H. Mulaosmanovic, S. Slesazeck, J. Müller, and T. Mikolajick, “CMOS Compatible Ferroelectric Devices for Beyond 1X nm Technology Nodes, ” in Solid State Device and Materials(SSDM), 2017, pp. 539-540.
    [12] T. S. Bösckea, J. Müllerb, D. Bräuhausc, U. Schröderd, and U. Böttgerc, “Ferroelectricity in Hafnium Oxide: CMOS Compatible Ferroelectric Field Effect Transistors, ” in IEDM, 2011, pp. 547-550.
    [13] P. Polakowski, S. Riedel, W. Weinreich, M. Rudolf, J. Sundqvist, K. Seidel, and J. Müller, “Ferroelectric Deep Trench Capacitors based on Al:HfO2 for 3D Nonvolatile Memory Applications, ” in International Memory Workshop (IMW), 2014, pp. 1-4.
    [14] C. H. Cheng and A. Chin, “Low-Leakage-Current DRAM-Like Memory Using a One-Transistor Ferroelectric MOSFET With a Hf-Based Gate Dielectric, ” Electron Device Letter, vol. 35, pp. 138-140, 2014.
    [15] C. H. Cheng and A. Chin, “Low-Voltage Steep Turn-on PMOSFET Using Ferroelectric High-k Gate Dielectric, ” IEEE Electron Device Letter, vol. 35, pp. 274-276, 2014.
    [16] M. H. Park, H. J. Kim, Y. J. Kim, T. Moon,K. D. Kim, and C. S. Hwangn, “Toward a Multifunctional Monolithic Device Based on Pyroelectricity and the Electrocaloric Effect of Thin Antiferroelectric HfxZr1-xO2 Films, ” Nano Energy, vol. 12, pp. 131-140, 2015.
    [17] Y. C. Chiu, C. H. Cheng, C. Y. Chang, M. H. Lee, H. H. Hsuand, and S. S. Yen, “Low Power 1T DRAM/NVM Versatile Memory Featuring Steep Sub-60-mV/decade Operation, Fast 20-ns Speed, and Robust 85oC-Extrapolated 1016 Endurance, ” in VLSI Technology Symp., 2015, pp. 184-185.
    [18] S. Fujii, Y. Kamimuta, T. Ino, Y. Nakasaki, R. Takaishi, and M. Saitoh, “First Demonstration and Performance Improvement of Ferroelectric HfO2-Based Resistive Switch With Low Operation Current and Intrinsic Diode Property, ” in VLSI Technology Symp., 2016, pp. 978-979.
    [19] H. Mulaosmanovic, J. Ocker, S. Müller, M. Noack, J. Müller, P. Polakowski, T. Mikolajick, and S. Slesazeck, “Novel Ferroelectric FET based Synapse for Neuromorphic Systems, ” in VLSI Technology Symp., 2017, pp. 176-177.
    [20] R. Eskandari, X. Zhang, and L. M. Malkinski, “Polarization-Dependent Photovoltaic Effect in Ferroelectric-Semiconductor System, ” Applied Physics Letters, vol. 110, pp. 121105, 2017.
    [21] M. Dragoman, M. Aldrigo, M. Modreanu, and D. Dragoman, “Extraordinary Tunability of High-Frequency Devices Using Hf0.3Zr0.7O2 Ferroelectric at Very Low Applied Voltages, ” Applied Physics Letters, vol. 110, pp. 103104, 2017.
    [22] J. V. Houdt, “Memory Technology for the Terabit Era: from 2D to 3D, ” in VLSI Technology Symp., 2017, pp. 978-979.
    [23] S. W. Smith, A. R. Kitahara, M. A. Rodriguez, M. D. Henry, and M. T. Brumbach, and J. F. Ihlefeld, “Pyroelectric Response in Crystalline Hafnium Zirconium Oxide (Hf1-xZrxO2) Thin Films, ” Applied Physics Letters, vol. 110, pp. 072901, 2017.
    [24] F. Huang, Y. Wang, X. Liang, J. Qin, Y. Zhang, X. Yuan, Z. Wang, B. Peng,L. Deng, and Q. Liu, “HfO2-Based Highly Stable Radiation-Immune Ferroelectric Memory, ” IEEE Electron Device Letter, vol. 38, pp. 330-333, 2017.
    [25] A. Chen, “Nanoelectronic Device Research for beyond - CMOS Technologies, ” in “Emerging Technologies for the post 14nm Node Area, ” in IEDM short course, Dec. 8, 2012.
    [26] K. Ni, M. Jerry, J. A. Smith, and S. Datta, “A Circuit Compatible Accurate Compact Model for Ferroelectric-FETs, ” in VLSI Technology Symp., 2018, pp. 131-132.
    [27] M. K. Kim, I.J. Kim, S. J. Lee, “CMOS-compatible ferroelectric NAND flash memory for high-density, low-power, and high-speed three-dimensional memory, ” Sci. Adv., 7, 3, eabe1341, 2021.
    [28] T. Gokmen and Y. Vlasov, “Acceleration of deep neural network training with resistive cross-point devices: Design considerations, ” Front. Neurosci., 10, 1–13, 2016.
    [29] S. Yu, P.- Y. Chen, Y. Cao, L. Xia, Y. Wang, H. Wu, “Scaling-up Resistive Synaptic Arrays for Neuro-Inspired Architecture: Challenges and Prospect, ” in IEDM Tech. Dig., 2016.
    [30] W. Chung, M. Si, and P. D. Ye, “First Demonstration of Ge Ferroelectric Nanowire FET as Synaptic Device for Online Learning in Neural Network with High Number of Conductance State and Gmax/Gmin, ” in IEDM Tech. Dig., Dec. 2018, pp. 344–347.
    [31] M. Seo, M.-H. Kang, S.-B Jeon and H. Bae, J. Hur, B.- C. Jang, S. Yun, S. Cho, W.-K. Kim, M.-S. Kim, K.-M. Hwang, S. Hong, S.-Y. Choi, and Y.-K. Choi “First Demonstration of a Logic-Process Compatible Junctionless Ferroelectric FinFET Synapse for Neuromorphic Applications, ” IEEE Electron Device Letters, vol. 39, no. 9, pp. 1445–1448, Sep. 2018.
    [32] M. Jerry, P.-Y. Chen, J. Zhang, P. Sharma, K. Ni, S. Yu, and S. Datta, “Ferroelectric FET Analog Synapse for Acceleration of Deep Neural Network Training, ” in IEDM Tech. Dig., Jan. 2017, pp. 139-142.
    [33] S. Oh, T. Kim, M. Kwak, J. Song, J. Woo, S. Jeon, I. Yoo and H. Hwang, “HfZrOx-based Ferroelectric Synapse Device with 32 levels of Conductance States for Neuromorphic Applications, ” IEEE Electron Devices Letters, 99, 732–735, 2017.
    [34] K. Ni, J. A. Smith, B. Grisafe, T. Rakshit. B. Obradovic, J. A. Kittl, M. Rodder and S. Datta, “SoC logic compatible multi-bit FeMFET weight cell for neuromorphic applications,” in IEDM Tech. Dig., Dec. 2018, pp. 296–299.
    [35] K.-Y. Hsiang, C.-Y. Liao, K.-T. Chen, Y.-Y. Lin, C.-Y. Chueh, C. Chang, Y.-J. Tseng, Y.-J. Yang, S. T. Chang, M.-H. Liao, T.-H. Hou, C.-H. Wu, C.-C. Ho, J.-P. Chiu, C.-S. Chang, and M. H. Lee, “Ferroelectric HfZrO2 with Electrode Engineering and Stimulation Schemes as Symmetric Analog Synaptic Weight Element for Deep Neural Network Training, ” IEEE Trans. on Electron Device, vol. 67, no. 10, pp. 4201-4207, 2020.
    [36] T. Ali, K. Mertens, R. Olivo, M. Rudolph, S. Oehler, K. Kühnel, D. Lehninger, F. Müller, M. Lederer, R. Hoffmann, P. Schramm, K. Biedermann, Alireza M. Kia, J. Metzger, R. Binder, M. Czernohorsky, T. Kämpfe, J. Müller, K. Seidel, J. Van Houdt, and L. M. Eng, “A Novel Hybrid High-Speed and Low Power Antiferroelectric HSO Boosted Charge Trap Memory for High-Density Storage, ” in IEDM Tech. Dig., Dec. 2020, pp. 383–386.
    [37] S. L. Miller and P. J. McWhorter, “Physics of the Ferroelectric Nonvolatile Memory Field Effect Transistor, ” Journal of Applied Physics, 72, 5999, 1992.
    [38] B1500A Semiconductor Device Analyzer user’s manual, pp. 1,4-2,32.
    [39] C.-Y. Liao, K.-Y. Hsiang, F.-C. Hsieh, S.-H. Chiang, S.-H. Chang, J.-H. Liu, C.-Y. Lin, T.-C. Chen, C.-S. Chang, and M. H. Lee, “Multibit Ferroelectric FET Based on Nonidentical Double HfZrO2 for High-Density Nonvolatile Memory, ” IEEE Electron Device Letters, Vol. 42, No. 4, pp. 617-620, April, 2021.
    [40] S. L. Miller and P. J. McWhorter, “Physics of the Ferroelectric Nonvolatile Memory Field Effect Transistor, ” Journal of Applied Physics, 72, 5999, 1992.
    [41] H. J. Kim, M. H. Park, Y. J. Kim, Y. H. Lee, W. Jeon, T. Gwon, T. Moon, K. D. Kim, and C. S. Hwang, “Grain size engineering for ferroelectric Hf0.5Zr0.5O2 films by an insertion of Al2O3 interlayer, ” Appl. Phys. Lett., 105, 192903, Nov. 2014.
    [42] M. H. Park, H. J. Kim, Y. J. Kim, T. Moon, and C. S. Hwang, “The effects of crystallographic orientation and strain of thin Hf0.5Zr0.5O2 film on its ferroelectricity, ” Appl. Phys. Lett., 104, 072901, Feb. 2014.
    [43] M.-K. Kim, I.-J. Kim, J.-S. Lee, “CMOS-compatible ferroelectric NAND flash memory for high-density, low-power, and high-speed three-dimensional memory, ” Sci. Adv. 2021; 7 : eabe1341.
    [44] Y. Qi and A. M. Rappe, “Designing Ferroelectric Field-Effect Transistors Based on the Polarization-Rotation Effect for Low Operating Voltage and Fast Switching, ” Physical Review Applied, 4, 044014, 2015.
    [45] T. Ali, P. Polakowski, K. Kühnel, M. Czernohorsky, T. Kämpfe, M. Rudolph, B. Pätzold, D. Lehninger, F. Müller, R. Olivo, M. Lederer, R. Hoffmann, P. Steinke, K. Zimmermann, U. Mühle, K. Seidel, and J. Müller, “A Multilevel FeFET Memory Device based on Laminated HSO and HZO Ferroelectric Layers for High-Density Storage, ” in IEDM Tech. Dig., Dec. 7-11, 2019.
    [46] S. Oh, T. Kim, M. Kwak, J. Song, J. Woo, S. Jeon, I. K. Yoo, and H. Hwang, “HfZrOx-Based Ferroelectric Synapse Device With 32 Levels of Conductance States for Neuromorphic Applications, ” IEEE Electron Device Letter, vol. 38, no. 6, 2017.

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