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研究生: 簡赫伸
Chien, Ho-Sheng
論文名稱: 以進階壓電力顯微技術結合機器學習探究鋯酸鉛薄膜的反鐵電性
Probing Antiferroelectricity of PbZrO3 Film by Advanced Piezoresponse Force Microscopy Combined with Machine Learning
指導教授: 陳宜君
Chen, Yi-Chun
學位類別: 碩士
Master
系所名稱: 理學院 - 物理學系
Department of Physics
論文出版年: 2023
畢業學年度: 111
語文別: 中文
論文頁數: 74
中文關鍵詞: 鋯酸鉛(PbZrO3)反鐵電壓電力顯微鏡SS-PFM主成分分析k-means演算法
外文關鍵詞: antiferroelectricity, PbZrO3, Piezoresponse Force Microscopy, SS-PFM, principal component analysis, k-means clustering
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  • 反鐵電材料擁有高能量密度以及更佳的能量使用效率的物理特性,展現出在能源應用上的廣闊前景。同樣作為壓電材料,擁有自發極化並且能夠透過外部電場控制極化方向,反鐵電(Antiferroelectric, AFE)與鐵電(Ferroelectric, FE)材料不同的是,反鐵電材料呈現出雙滯回曲線,在正負偏壓區域中各兩處矯頑場分別指出反鐵電的四處相變點,並且在施加較小的外加偏壓時有較低的殘餘極化量。我們使用脈衝雷射沉積法製備鋯酸鉛薄膜,並且利用微觀量測的壓電力顯微鏡(piezoresponse force microscopy, PFM),探測經典的反鐵電材料鋯酸鉛(PbZrO3, PZO)的電性疇域。在不同的量測偏壓設置下,PZO會被控制在反鐵電相或是鐵電相,相變的過程中伴隨著晶格結構以及極化易軸的變化,並且在面內方向的電性疇域分別表現出不同形狀。量測過程中,為了優化反鐵電的壓電訊號,我們使用Switching Spectroscopy PFM(SS-PFM)的階梯狀直流偏壓波型量測反鐵電的雙滯回曲線,並且結合機器學習中的主成分分析(principal components analysis, PCA)與K平均演算法(k-means clustering),幫助我們可以重構出純化的反鐵電訊號,最終建構出PZO的反鐵電疇域的二維掃描影像,並且討論機器學習在分析數據可能出現的誤差以及注入電荷對於量測反鐵電電域時造成的極化翻轉現象。

    Antiferroelectric (AFE) materials possess high energy density and high storage efficiency, which show the great prospect of energy applications. Different from ferroelectrics (FE), antiferroelectric materials display zero remanent polarization and double hysteresis characteristics with four coercive points, corresponding to phase transitions between AFE and FE phases. While the AFE orderings are important, the AFE orientations are difficult to detect because of the nature of antiparallel dipoles within the AFE unit cell.
    To solve this problem, in this study, we utilize advanced piezoresponse force microscopy (PFM) to obtain the domain patterns of the AFE PbZrO₃ (PZO) epitaxial films by abstracting the hidden signals through unsupervised machine-learning techniques. Surprisingly, the measured in-plane domain patterns depend on the cantilever orientation which show the complex polarization directions of PZO. To extract the pure AFE signal from the multi-dimensional piezoresponse data, we adapted switching spectroscopy technique to measure the antiferroelectric hysteresis loop pixel by pixel. The big dataset are further analyzed by the principal component analysis (PCA) method and k-means clustering. After separating the mechanisms of electrostatic effect, oxygen vacancy migration, and the antiferroelectric-to-ferroelectric transitions, we can visualize the 2D lateral domain patterns of the antiferroelectric PZO. Finally, we discussed the stripe structure found in lateral PFM mapping which could be attributed to the charge injection that influenced the dipole moments direction.

    摘要 I Abstract III 誌謝 XIII 目錄 XV 表目錄 XVIII 圖目錄 XIX 第一章 緒論 1 第二章 文獻回顧 2 2.1 反鐵電材料介紹 2 2.1.1 鈣鈦礦結構 2 2.1.2 鐵電性與反鐵電性 3 2.1.3 反鐵電材料的儲能特性 5 2.2 反鐵電的相變理論及鋯酸鉛的相變結構變化 7 2.5 鋯酸鉛單晶薄膜的製備 11 2.3 壓電訊號與非壓電訊號的微觀量測 12 2.4 機器學習與數據分析 15 第三章 實驗原理與方法 17 3.1脈衝雷射沉積法PLD 17 3.2 P-E量測系統 18 3.3 原子力顯微鏡 20 3.4 壓電力顯微鏡 23 3.4.1 Switching Spectroscopy PFM (SS-PFM) 24 3.4.2 DataCube-Contact Resonance mode (DataCube-CR) 26 3.4.3 雙頻共振追蹤技術(Dual-frequency resonance-tracking PFM) 27 3.5 機器學習演算法 27 3.5.1 主成分分析 28 3.5.2 K平均演算法 29 第四章 結果與討論 30 4.1 反鐵電樣品的製備 30 4.2 鋯酸鉛薄膜的反鐵電性量測 32 4.3 反鐵電性的二維疇域影像表現 41 4.4 結合機器學習進行反鐵電數據處理 50 4.5 PZO(111)的疇域極化方向訂定 55 4.6 邊界效應的影響 65 第五章 結論 69 參考文獻 72

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