下一步诊断分辨率提高。
扫描诊断失败的测试周期变成有价值的数据和逻辑是一个建立数字半导体缺陷定位的方法。layout-aware扫描诊断的出现代表了一个戏剧性的诊断技术的进步,因为它可以减少怀疑面积高达85%并识别物理网段而不是整个逻辑网[1 - 3]。layout-aware提供的缺陷分类诊断做出诊断的有效工具,不仅定位的缺陷,还对收益分析。Diagnosis-driven收益率分析(DDYA)使卷诊断结果可行的识别产生的最有可能的原因。Layout-aware诊断也有其局限性;诊断结果可能指向多个位置和一个单一的位置由多个根本原因可以解释。例如,开放在一个特定网段可以解释为一个开放的缺陷在金属2段,开放在metal3,单个via3或双via3。换句话说,有一定量的歧义或噪声的诊断结果。根源反褶积(RCD),统计增强技术最近可用Tessent诊断和YieldInsight产品,下一步是诊断分辨率增强。它是通过分析多个layout-aware诊断报告一起来识别潜在的缺陷分布(根本原因分布),最有可能解释这组诊断结果。 The results are then back-annotated to the individual diagnosis suspects. Where layout-aware diagnosis points to a segment, RCD can isolate a particular root cause in that segment. This increase in the FA relevance and success rate dramatically reduces the failure analysis (FA) cycle time from months to days. RCD also enables “virtual FA”, the ability to determine defect distribution for a population of failing devices before any failure analysis is performed. Later in this whitepaper, we will also review silicon results.
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