- 學位論文
使用機器學習進行高速兩向量測試的動態電路壓降預測
Dynamic IR-drop Prediction of At-speed Two-vector Test Using Machine Learning
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摘要
在積體電路測試當中，過大的動態 IR-drop 會導致時序違規，進而導致測試失敗。相較於固定型故障測試，動態 IR-drop 在高速兩向量測試中成為一個更為嚴重的問題由於高速時鐘的存在。由於動態 IR-drop 分析的長運行時間，我們需要機器學習方法來加速分析過程。在本文中，我們提出了兩種新的方法來預測高速兩向量測試的動態 IR-drop。第一種方法分別使用兩個模型來分析第一個捕獲周期和第二個捕獲周期。第二種方法則結合了兩個捕獲周期的特徵。此外，我們提出了空間窗特徵和時間切片特徵，以提高預測準確性。我們最糟糕的動態 IR-drop預測的平均絕對誤差為 5.230 毫伏，小於供應電壓的 0.6%。與商業工具相比，我們的實驗結果顯示至少實現了 12.6 倍的加速比。憑藉我們的技術，我們可以在短時間內識別出存在過度 IR-drop 的兩向量測試，以防止良率損失。
並列摘要
Excessive dynamic IR-drop in VLSI testing causes timing violations, which leads to test failure. The dynamic IR-drop becomes a more serious problem in at-speed two-vector tests than that in stuck-at fault tests due to the at-speed clock. However, we need Machine Learning methods to speed up the analysis because of the long runtime of dynamic IR-drop analysis. In this paper, we propose two new methods to predict dynamic IR-drop of at-speed two-vector tests. One uses two models for the first capture cycle and the second capture cycle, respectively. The other one combines features of two capture cycles. Also, we propose spaced-window features and time-sliced features to improve prediction accuracy. Our mean absolute error for the worst dynamic IR-drop prediction is 5.230mV, which is less than 0.6% of the supply voltage. Our experiment results show at least a 12.6X speed-up ratio compared to a commercial tool. With our technique, we can identify two-vector tests which have excessive IR-drop in a short time to prevent yield loss.
參考文獻
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