攜帶式、植入式應用常需要在面積及功率的限制下進行訊號處理、學習並分類。功率、面積的限制讓結合機器學習演算法及類比電路設計的類比學習電路成為處理這些問題較好的選項。然而，目前以類比電路實現的學習演算法在學習能力上與軟體執行的演算法相較仍有所不足，為了讓學習晶片能有更廣泛的應用，本研究目標以類比電路實現類神經演算法，讓此學習晶片在低功率下運作並具備與程式執行的演算法相似的學習能力。目標應用在生理訊號及電子鼻氣味辨識等低功率、中低速的應用上。
本研究大致分為三個部分，首先是為了電子鼻應用並參考哺乳動物嗅覺系統設計的仿生嗅覺突波神經網路晶片。此晶片能在3.6 μW的低功率下操作，分類速度約5 Hz，並對於商用電子鼻Cyranose 320所取得的水果氣味擁有約87.6 %的正確率。5 Hz的分類速度雖然滿足電子鼻的需求，對於生理訊號辨識上仍不足，除此之外，此晶片也不具備擴充性讓網路大小能根據不同應用而調整。為了提高學習能力、分類速度並實現擴充性，本研究又設計了相容於硬體的機率型突波神經網路演算法。藉由程式模擬得知此演算法不僅適用於類比電路實現，同時對於參數變異、突觸雜訊以及權重解析度均不敏感。這些特性讓此演算法在晶片實現後較可能保持與程式實現時相似的學習能力。為了驗證這點，本研究最後以0.18 μm CMOS製程將此演算法以類比電路實現，除了實現演算法外，也藉由電路設計減小晶片面積、功耗以及製程變異對於學習效能的影響。量測結果證實此晶片消耗功率僅6.8μW，分類速度達3 kHz，對於同樣的電子鼻資料有92 %的正確率，也能以100 %的正確率分辨ECG資料，並且在AUC保持0.8時能學習約80個隨機圖像。除了單晶片操作外，此晶片也具備擴充性。當連接兩顆晶片形成較大的網路架構，同樣的80個隨機圖像資料組在學習後的AUC可以達到0.9。量測結果除了滿足規格要求外，與現有文獻相較，此晶片消耗很低的功率並擁有最好的學習能力。

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