光學顯微鏡輔助之大範圍原子力顯微鏡精密正弦式局部掃描

Precision Sinusoidal Local Scan for Large Range Atomic Force Microscopy with Auxiliary Optical Microscopy

陳志烈(Chih-Lieh Chen)

指導教授 ： 傅立成

國立臺灣大學/電機資訊學院/電機工程學研究所/碩士(2013年)

https://doi.org/10.6342/NTU.2013.00545

摘要

原子力顯微鏡是一種非常實用的量測儀器，可建立導體與非導體樣本的三維表面輪廓在奈米等級的解析度。然而，因為傳統原子力顯微鏡所使用的掃描方式，容易引發掃描器的機械共振，且存在一些不必要區域的掃瞄，因而造成原子力顯微鏡在掃描速率上的限制。在本論文中，吾人將以自行設計之原子力顯微鏡系統從三個不同的層面來解決上述之問題。
首先，我們將原子力顯微鏡在水平方向的掃描從傳統的柵狀式軌跡改為正弦式軌跡，如此可在不會引發水平掃描器震動的情況下提高掃描速率。其次，根據此熟知的掃描軌跡，基於內部模形原理所設計之類神經網路互補順滑模式控制器與適應性互補順滑模式控制器，可用來達到高精度的掃瞄並處理系統參數的不確定性和外在環境干擾等問題。最後，透過光學顯微鏡的輔助與掃描過程中所得到的資訊，可進一步做掃描路徑的規劃來集中掃描區域在有樣本的地方，進而縮減整體所需的掃描時間。從實際的實驗結果可以顯示出此提出的方法之效果。

關鍵字

原子力顯微鏡 ； 正弦式掃描 ； 內部模型原理 ； 類神經網路 ； 互補順滑模式控制 ； 適應性控制

並列摘要

Atomic force microscopy (AFM) is a powerful measurement instrument which can build three-dimensional topography image of conductive and nonconductive samples at nano-scale resolution. However, due to the scan method of conventional AFM, the induced mechanical resonance of the scanner and the scan in area of uninterested would strictly limit the scan speed. In this thesis, we improve these problems with our designed AFM system from three aspects.
First, the sinusoidal trajectory is applied to lateral scanning of the AFM rather than the traditional raster trajectory, so the scan rate can be increased without inducing vibration of the lateral scanner. Second, with this well-known trajectory, the internal model principle based neural network complementary sliding mode controller and adaptive complementary sliding mode controller are designed to achieve high precision scanning and to cope with the system parameter uncertainties and external disturbance. Finally, with the aid of an auxiliary optical microscopy and the scanned information during the scanning process, scan path planning can be adopted to focus the scanning on samples such that the total scan time is shortened. Experimental results are provided to show the performance of the proposed method.

並列關鍵字

Atomic force microscopy ； sinusoidal scanning ； internal model principle ； neural network ； complementary sliding mode control ； adaptive control

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