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

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/96473

Title:	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
Authors:	白家碩;Pai, Chia-Shuo
Contributors:	電機工程學系
Keywords:	鐵電電晶體;人工智慧;記憶視窗;同型相界面;超晶格HZO;Ferroelectric Transistor;AI;Memory Window;Morphotropic Phase-Boundary (MPB);SL-HZO
Date:	2024-11-27
Issue Date:	2025-04-09 18:47:44 (UTC+8)
Publisher:	國立中央大學
Abstract:	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。;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.
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