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  • 學位論文

利用矽光電二極體感測電子束微影系統電子束位置及漂移現象

Electron Beam Position and Drift Detection through Silicon Photodiodes in Electron Beam Lithography System

指導教授 : 顏家鈺
共同指導教授 : 陳永耀(Yung-Yaw Chen)

摘要


目前最有機會的兩個次世代微影技術為極紫外光微影與無光罩多電子束微影,對於後者而言,電子束漂移感測是首要發展的技術。本文以電子顯微鏡作為研究平台,以矽光電二極體作為電子束位置與漂移感測器,利用收集電子束打擊試片所反彈的背向散射電子來量測電子束位置;以及利用影像分析電子束漂移現象,並與電子顯微鏡內建的二次電子感測器做比較。 感測區分為四象限,可用於分析電子束所產生之背向散射電子分布,以及長時間操作下電子束光學系統所產生的電子束漂移現象。此外,配合電子束掃描線圈以及矽光電二極體電子感測器可產出背向散射電子影像。在未來可使發展中之大規模平行寫入多電子束直寫系統(MPML2)具有成像能力。 初步顯示在5 kV使用以及3 mm工作距離下有足夠的電流增益。因此陣列型式的感測器將提供了有效的方案可用於MPML2。

並列摘要


Two potential opportunities for the next generation of the lithography are the extreme ultraviolet lithography (EUV), and the mask-less multi-electron-beam lithography. In the latter, the beam drift detection is the demanding technique. In this dissertation, we used scanning electron microscopy as a research platform to set up the silicon photodiode as the electron beam position and drift detectors. By collection of the back-scattered electrons from the specimen, the electron beam position could be computed. And the beam drift could be derived from electronic image analysis, and the results showed good consistence with those derived from the secondary electron image captured by the SEM built-in electron sensor. In the silicon photodiode, the sensing areas are divided into four quadrants, which can be used to analyze the backscattered electron distribution generated by the primary electron beam, and the beam drift phenomenon accompanies long runtime operation in electron beam system. In addition, with the electron beam scanning coils and the silicon photodiode, the backscattered electron images could be produced. The under-development large-scale parallel multi-electron-beam direct writing system (MPML2) could be capable with imaging capabilities by using silicon photodiodes in the future. The preliminary results showed that there is sufficient current gain in the conditions of 5kV acceleration voltage and 3mm working distance. It seems that the array type of detectors will be good choice in MPML2.

參考文獻


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