在醫療領域上，生醫晶片可應用於生理訊號的辨識，舉例來說，將神經微電極陣列植入於生物體內，然後對生物體的神經組織進行長期量測，並利用晶片系統辨識生理訊號，再給予生物體適當的刺激或控制人工義肢等電子輔具，藉以恢復病患基本的生活能力。由於生理訊號具有變異性，若想要辨識生物體的高雜訊生理訊號，晶片系統便需要擁有容忍高雜訊的能力。為了使晶片系統順利的辨識高雜訊的生理訊號，所以晶片系統所採用的演算法是由Movellan等人所發表的擴散網路（Diffusion Network）機率型演算法，此演算法可運用自身的隨機性以概括訊號的變異而得出有意義的時變資訊，因此，擴散網路能夠辨識充滿漂移與雜訊的生理訊號，例如辨識心跳訊號。
在擴散網路內，任一神經細胞對所有的神經細胞皆有專屬的連接線。對於擁有此種特性的網路，可擴充性在過去的文獻中並未解決，所以在本文中提出一個可擴充型架構。首先，透過解決每一個神經細胞的突觸數量如何隨著欲處理訊號維度的提高而增加，以及思考神經細胞與神經細胞之間的連線問題，逐步設計適用於擴散網路的可擴充型系統架構。在完成可擴充型系統架構後，接下來便開始設計一個神經細胞的實際電路，在設計的過程需要考慮數值上的參數範圍與電路上的數值範圍之間的關係，然後擴散網路的電路系統便能藉著多個神經細胞的電路連接而組成，再進行電路模擬以驗證擴散網路的電路系統是否能夠成功重建訊號。最後，比較可擴充型擴散網路晶片的實際量測結果與理想電路的模擬結果，接著分析產生誤差的原因，然後提出解決方案以使可擴充型擴散網路晶片能夠成功重建訊號。

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