中介層(interposer)在先進製程封裝佔有舉足輕重的地位，透過將多個晶片堆疊至中介層上，利用中介層做為溝通橋梁可以達到更好的異質整合、更好的效能表現，同時達到更精小的體積尺寸(form factor)。然而，製作上的磨薄程序以及被動元件的性質使得傳統測試機制並不適合用於中介層上，進而增加了可能將損壞中介層堆疊後的額外成本損失。因此，我們提出了一個利用熱影像來區別堆疊前(pre-bond)中介層的好壞的無接觸式測試機制，藉由熱影像，我們從中擷取出缺陷(defect)資訊當作特徵點(feature)，並利用機器學習(machine learning)技術自動化判定一個中介層的損壞與否。實驗結果顯示出我們的無接觸式測試機制可以將良率從平均72.13％提昇至96.25％。

Interposers are essential for integrating multiple dies in staked-die products. However, the traditional testing mechanism is not appropriate for interposers. Therefore, in this work, we proposed a contactless testing methodology for pre-bond interposers. Also, to improve the production yield, surface defects and inner defects will be both examined. Our testing mechanism dedicates to detecting defective interposers by thermal images. Critical features will be extracted from thermal images and will be used to construct machine learning algorithms to determine whether the interposer is defective automatically. Experimental results show that our contactless testing mechanism can efficiently improve the production yield of the interposers from average 72.13% to average 96.25%.

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