應用進階變異數縮減技術的蒙地卡羅軟性電子錯誤率分析

Abstract

使用統計性的方法在製程變異下準確估計電路的軟性電子錯誤率分析是很重要的。製程變異參數可以分成晶圓間和晶圓內的變異兩個部分，晶圓內的變異存在空間相關性使得越接近彼此的製程變異參數會越相似，此外我們考慮了空間相關性的因素。然而，因為沒有考慮降低變異數，使得現今的軟性錯誤率統計分析研究無法達到良好的準確性。在這篇論文裡，我們提出了一個高準確性的統計模型，利用蒙地卡羅去分析這些統計模型，並且達到了比較好的收斂與增加速度。此外，我們利用降低變異數的方法來分析這些統計模型。實驗結果顯示，我們可以在更短的時間內更準確的估計出軟性錯誤率。

Statistical methods are important to accurately estimate soft error rates (SERs) of circuits with process variations. Process variations can be classified into the inter-die variations and the intra-die variations. The intra-die variations exist spatial correlations where the devices that are close to each other are more alike. Therefore, a SER analysis frameworks should include spatial correlations. However, without variance reduction, current Monte-Carlo-based SER analysis can not achieve a satisfactory accuracy with reasonable speed. In this work, we first review statistical soft error rate analysis based on which a Monte-Carlo framework is built. We further employ the quasi-random sequences, which successfully speeds up the convergence of simulation error and shortens the runtime. Moreover, advanced sampling techniques are incorporated for variance reduction of SSERs. Experimental results show that this framework is capable of more precisely estimating circuit SSERs and reaches better speedups.