電子鼻近年來被廣泛的應用在各個領域中，在傳統上電子鼻系統仍是一個龐大的裝置，然而若要能方便的在人類的生活中使用，則必須將電子鼻發展成可隨身攜帶的裝置，此外，在某些應用的需求上，例如環境的監控，考量到成本和體積，我們不可能以大型的電子鼻裝置來運作，因此將電子鼻系統微小化是必要的。電子鼻系統的運作從吸入氣味樣本後，使其與氣體感測器反應，接著將反應的訊號轉換和處理，最後透過資料辨識得到結果，是一個結合多個領域的裝置，在氣味資料辨識的部分，其牽涉到圖形辨識的技術，這些分類演算法通常以電腦或微處理器運行，但若要應用在可攜式的裝置上，以此方式並不划算，因此透過低功率的類比積體電路設計實現是較佳的方式。
在圖形辨識的領域中，目前已經發展相當多種的演算法，支持向量機從90年代被提出後，因為根據統計學習理論可以證明支持向量機的原理在分類的許多考量上較佳，因此在許多的領域中蓬勃發展和應用。本研究論文針對可攜式電子鼻提出一個三類支持向量機晶片，以一對一方法將支持向量機由區分兩類別拓展到多類別，並且在同一片晶片上同時實現參數學習和氣味分類的機制，透過一個進一步簡化的遞迴式類神經網路電路實現參數的訓練。晶片以TSMC 0.18μm CMOS製程製作，經由氣體實驗感測器量測的氣體資料測試晶片，從氣味分類實驗的統計結果知其辨識率在七成以上，並且操作在1.8V時消耗的功率僅125μW。實際量測時，最低操作電壓可到1.2V，此時的功耗更只有58μW，因此本研究相當適合應用在可攜式電子鼻上。

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