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姓名	白家碩(Chia-Shuo Pai)  查詢紙本館藏	畢業系所	電機工程學系
論文名稱	可實現 2.3V大記憶視窗（每單元3位元）、可立即讀取並具有????^??次寫入之鐵電電晶體及其在機器學習之高精確度研究 (Ferroelectric Transistor with a 2.3V Large Memory Window (3 bits per cell), Instant Readout, and 10? Write Cycles, and Its High Accuracy in Machine Learning Research)
檔案	[Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 ( 永不開放)
摘要(中)	隨著物聯網、人工智慧、自動駕駛等領域的迅速發展，對於存儲技術的需求 也將不斷增加。在這些新興記憶體技術中，非揮發性記憶體（Non-V olatile Memory，NVM）扮演著越來越重要的角色。NVM 的主要特色是在斷電後能夠長時間保持原本的儲存狀態，同時需具備良好的可擴張性、高速、低功耗、較長的壽命以及 耐久性。這使得NVM 成為未來數據存儲和處理中不可或缺的一部分。 在本次實驗中，我們專注於非揮發性記憶體中常見的鐵電記憶體，特別是鐵電電容記憶體（FeCAP）和鐵電場效應電晶體（FeFET）這兩種元件。我們從製程技術出發，詳細記錄了製程過程，並從材料分析和電性量測兩個角度對其進行了評估和比較。最終，我們提出了一種利用同型相界面（Morphotropic Phase Boundary，MPB）增強元件的方法，並有效地減少了缺陷，達到兩倍的記憶視窗。這一方法提升了元件的操作速度和可靠性。 在這項實驗中，我們探討了以鋯鈰氧化物（HZO）與 ZrO2/HZO 堆疊為基礎 的 MPB FeFET 的開關電壓、保久性和耐久性的提升。透過 X 光光電子能譜儀 （X-ray photoelectron spectroscopy，XPS），我們展示在 MPB FeFET，能有效降低改善了由氧空缺的濃度，從原本的 40.5%降至 20.3%。降低了去極化電場，並將崩潰電壓的大小從未 4.2V 提升到 6V，實現低漏電流。此外，異質接面 MPB 的FeFET 展現了穩定的讀寫後保久性和改善的耐久性，即使在達到 109 次讀寫後也沒有發生故障。最後，我們利用固定脈衝±2V～±4V ，寬度為 100us 、步進為 0.05V的脈衝方波進行寫入讀取操作來量測電導度的結果，將其放入 NeuroSim 進行機器學習並發現達到極高的準確率=92%，優於其他樣品結構，顯示本次實驗結構樣品有助於提高器件的整體效能、可靠和耐用性，使這項結構在 AI 具有更大的應用潛力。
摘要(英)	With the rapid development of fields such as the Internet of Things (IoT), artificial intelligence (AI), and autonomous driving, the demand for memory technologies is steadily increasing. Among emerging memory technologies, non-volatile memory (NVM) is playing an increasingly important role. The main feature of NVM is its ability to retain stored data even when power is off, along with characteristics such as scalability, high speed, low power consumption, long lifespan, and durability. This makes NVM an indispensable part of future data storage and processing. In this work, we focus on ferroelectric memories within NVM, specifically ferroelectric capacitors (FeCAP) and ferroelectric field-effect transistors (FeFET). Starting from process technology, we documented the fabrication process in detail and evaluated and compared these devices from the perspectives of material analysis and electrical measurement. Ultimately, we proposed a method to enhance device performance by utilizing a morphotropic phase boundary (MPB), effectively reducing defects and achieving a twofold increase in memory window. This approach improved device operating speed and reliability. we explored improvements in the switching voltage, retention, and endurance of MPB FeFETs based on hafnium zirconium oxide (HZO) and ZrO?/HZO stacking. Using X-ray photoelectron spectroscopy (XPS), we demonstrated that MPB FeFETs effectively reduced oxygen vacancy concentration from 40.5% to 20.3%, lowered depolarization fields, and increased breakdown voltage from 4.2V to 6V, achieving low leakage current. Additionally, the heterojunction MPB FeFET exhibited stable post-read/write retention and enhanced endurance, with no failures observed even after 10? read/write cycles. Finally, using fixed pulses of ±2V to ±4V, with a width of 100 μs and a step size of 0.05V, we measured conductance in write-read operations. These results were fed into NeuroSim for machine learning, where an accuracy rate of 92% was achieved, surpassing other sample structures. This demonstrates that the experimental structure in this study contributes to improved overall performance, reliability, and durability of the device, highlighting its potential for broader applications in AI.
關鍵字(中)	★ 鐵電電晶體 ★ 人工智慧 ★ 記憶視窗 ★ 同型相界面 ★ 超晶格HZO	關鍵字(英)	★ Ferroelectric Transistor ★ AI ★ Memory Window ★ Morphotropic Phase-Boundary (MPB) ★ SL-HZO
論文目次	摘要 I ABSTRACT II 致謝 IV 圖目錄 VIII 表目錄 X 1 第一章 序論與文獻回顧 1 1.1新興記憶體(Emerging Memories) 1 1.1.1 鐵電隨機存取記憶體(Ferroelectric RAM,FeRAM) 1 1.1.2 鐵電場效電晶體 (Ferroelectric FET, FeFET) 2 1.1.3 鐵電穿隧元件 (Ferroelectric Tunnel Junction, FTJ) 4 1.2 鐵電材料與特性 4 1.2.1 鐵電材料的演進發展 4 1.2.2 新穎氧化鉿鋯鐵電薄膜(FE-HF1-XZRXO2, HZO) 5 1.2.3 鐵電極化機制模型 7 1.3 鐵電材料面臨的挑戰 8 1.3.1 氧空缺、喚醒與疲勞效應、電荷捕捉效應 8 1.3.2 去極化電場與印痕效應、尺寸效應 9 1.4 同型相介面(Morphotropic Phase Boundary, MPB)的演進發展 10 1.4.1 同型相介面(MPB)的概念與早期研究 10 1.4.2 同型相介面(MPB)現代研究的進展與技術演變 11 2 第二章 實驗流程與方法 27 2.1製程步驟 27 2.2 WET bench（RCA clean） 27 2.3 原子層沉積系統（ALD） 27 2.4 金屬物裡氣相沈積（PVD） 28 2.5 黃光微影製程（i-Line） 28 2.6 乾式蝕刻（Etching） 28 2.7 離子佈植（Doping） 29 2.8 退火製程（RTA） 29 3 第三章 電性量測與材料分析 37 3.1 電性量測 37 3.1.1 ID-VG 37 3.1.2 高溫保留度測試（Retention） 37 3.1.3 耐久度評估 37 3.1.4 多層儲存單元操作量測 38 3.1.5 切換速度量測方法 38 3.1.6 介面陷阱量測 38 3.2 材料分析 39 3.2.1 掃描式電子顯微鏡（Scanning Electron Microscope, SEM） 39 3.2.2 穿透式電子顯微鏡（Transmission Electron Microscope, TEM） 39 3.2.3 EDX/EDS 表面元素分析 40 3.2.4 低掠角X射線繞射（Grazing Incidence X-Ray Diffraction, GIXRD） 40 3.2.5 X射線光電子能譜（X-ray Photoelectron Spectroscopy, XPS） 41 4 第四章 實驗結果與討論 50 4.1 FeCAP電性量測 50 4.2 FeFET電性量測 50 4.2.1 基礎電性 50 4.2.2 XPS材料分析與界面缺陷密度(Dit)分析 51 4.2.3 多位元操作 52 4.2.4 操作速度 52 4.2.5 瞬時讀取量測 53 4.3 機器學習（NeuroSim） 53 4.3.1 電導度（Conductance）量測 54 4.3.2 NeuroSim機器學習結果 55 第五章 結論與未來展望 69 參考文獻 70 第六章 附件 77
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指導教授	郭明庭 唐英瓚(Ming-Ting Kuo)	審核日期	2024-11-27
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