在科技快速變遷的時代，產品的生命週期逐漸縮短，產品從研發試作至量產上市的時間愈縮愈短，如何由產品先行試作至快速量產，是企業的核心競爭力之一。

　　由於類神經網路系統（Neural Network）具有高效率的學習模式，特別是對於非線性模型之建立或時間數列預測模式的建構，都顯得非常的有效。因此，本研究將以積層陶瓷電容（Multi-layer Ceramic Capacitor, MLCC）為研究對象，應用類神經網路系統在產品量產初期，以供應商所提供的出貨報告資料和製程相關歷史資料，快速求出穩健的製程參數，以縮短產品試作至量產的時間，降低生產成本。

　　然而，對於類神經網路系統而言，其學習模式的建立，完全仰賴於訓練資料所提供的資訊，唯有充足的訓練資料，才能建立起一個精準的類神經網路系統，但實際上常因原始的資料筆數不足，使得模式的預測不穩定。

　　本研究乃針對在收集到的資料比數不多之情況下，應用整體擴展技術（Mega Trend Diffusion），產生虛擬樣本（Artificial Samples），增加資料筆數，以求得更加準確之預測模式，本研究顯示此模式可在產品量產初期，快速預估其製程重要參數，提供管理決策之參考。

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