分析在全電量之粒子影響下之統計性軟性電子錯誤率

Abstract

近年來，隨著深次微米時代的來臨，製程變異對於系統的穩健帶來了極大的挑戰。其中，軟性電子錯誤率在先進電路的設計上被發現的機率也愈來愈高，對電路之可靠度而言又變成一個重要的研究題目。然而，在前人的研究中，並無一個可有效地估計在製程變異下之軟性電子錯誤率。因此，在本論文中建立出一個準確且快速的方法來有效地估計在製程變異下，軟性電子錯誤率對電路可靠度之影響，其中主要包涵有以下二個部分(1) 資料重建及改良式機器學習方法 (2) 粒子電量邊界選擇自動化。透過改良式機器學習配合資料重建，我們可快速建構出精確的軟性電子錯誤率模型。在建構精確模型後，此方法會自動選擇所需計算之粒子電量，並排除掉其它不需計算電量，以逵加速計算軟性電子錯誤率之目的。實驗結果證明，此方法在ISCAS 電路中與蒙地卡羅電路模擬相比可加速約10^7倍，且只有0.8%的平均誤差

This thesis re-examines the soft error effect caused by radiation-induced particles beyond the deep sub-micron regime. Soft error has become one of critical reliability concerns due to the continuous technology scaling. Hence, it is necessary to develop an approach to accurately estimate soft error rate (SER) integrated with the process-variation impact. Due to inaccuracy of previously published approaches, an accurate-and-efficient framework is proposed in this thesis to perform statistical soft error rate (SSER) analysis considering full-spectrum charge collection. This framework mainly consists of two components (1) intensified learning with data reconstruction and (2) automatic bounding-charge selection. Experimental results show that the proposed framework can speed up SER estimation at the order of 10^7X with only 0.8% accuracy loss compared to Monte-Carlo SPICE simulation