光譜晶片為應用MEMS(微機電系統製程) 技術製造，其搭載影像感測器即可成為一台微型光譜儀，與市售光譜儀相比，擁有輕量化、可攜帶性、成本低等優勢。光譜晶片上涵蓋狹縫(Slit)、光柵(Grating)、聚焦鏡(Focusing Mirror) 等超微型光學元件，生產過程繁瑣且複雜，這類應用半導體製程技術生產的元件，在製造的任何一個環節，都有可能產生刮傷、破損、落塵於晶圓上，大大影響良率及品質，因此瑕疵的檢測在半導體廠中是相當重要的環節。
這些微小的瑕疵，過往是以人工方式，透過高倍率的電子顯微鏡進行品質的檢測，要在晶圓上定位到目標微結構位置，並找尋微奈米等級的瑕疵，透過這種人工方式有耗時、人力疲勞、易出錯、效率低等缺點。因此本文針對生產自動化目標為方向，研究透過現有光學影像儀為基礎，將瑕疵檢測影像演算法加入其中，改善過去須手動定位、對焦、取像、量測等缺點，並能自動找尋晶粒瑕疵，進一步計算瑕疵位置及大小。
研究中使用邊緣偵測和最小二乘方等方法進行四吋晶圓定位，透過影像清晰度評價函數進行自動對焦，並到特定目標點進行取像，接著應用不同影像處理技術將瑕疵找出並標示其位置、尺寸，最後自動產生檢測報表。將以往人工檢測平均三個鐘頭時長減少至二十分鐘完成，達到自動化瑕疵檢測的目標。

SpectroChip is manufactured by MEMS (Micro Electro Mechanical System Process) technology, and it can become a miniature spectrometer with an image sensor. Compared
with commercially available spectrometers, it has the advantages of light weight, portability and low cost. SpectroChip covers ultra-micro optical elements such as slit, grating, focusing mirror, etc., and the production process is tedious and complicated. Scratch, damage and particle which greatly affect the yield and quality may happen in any part in the process of semiconductor. Therefore, defect detection plays an important role in the semiconductor fabrication.
In the past, the quality inspection of these tiny defects was carried out manually through a high-magnification electron microscope. It was necessary to locate the target microstructure on the wafer and find the micro-nano level defects. There are some disadvantages in the manual method, such as time-consuming, labor fatigue, error-prone and low efficiency.
Therefore, to achieve the goal of production automation, this paper investigates how to add the defect detection image algorithm to the existing optical projector, which can improve the shortcomings of manual positioning, focusing, image taking and measurement in the past, and can automatically find the grain defect and further calculate their position and size.
In this study, edge detection and least square method are used to locate the four-inch wafer, auto-focusing is performed by image definition evaluation function, and the image is taken at a specific target point. Then, different image processing techniques are used to find out the defects and mark their positions and sizes, and finally the inspection report is automatically generated. In the past, the average time of manual inspection was reduced from three hours to twenty minutes, and the goal of automatic defect detection was achieved.

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