Evaluation of the Iddq Signature in devices with Gauss-distributed background current

J. Schat
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引用次数: 1

Abstract

In the last decade, the single-threshold IDDQ approach made way for more elaborated techniques like Delta-IDDQ and adaptive IDDQ. Due to increasing background currents, however, also these methods are beginning to have problems to distinguish between good and bad devices. A good evaluation algorithm for IDDQ takes all known information about 'good' and 'bad' parts into account, i.e. it 'knows' how the IDDQ signatures of good and bad parts look like. Unfortunately, not only do the signatures of good parts differ significantly, but the signatures of bad parts differ even more. Moreover, since IDDQ faults are more often than not non-fatal (not impairing the functionality), it is frequently hard to say if a device is really 'good' or bad'. There are two kinds of information, however, which are known without referring to a certain process or IC type: one is the model of the IDDQ fault, and the other is the statistical distribution of the background-IDDQ. Using this information, an estimator with higher discrimination capability than the traditional Delta-IDDQ-approach is created. Measurement results form several lots of a 180 nm chip are presented..
具有高斯分布背景电流的器件Iddq签名的评估
在过去十年中,单阈值IDDQ方法为更复杂的技术(如Delta-IDDQ和自适应IDDQ)让路。然而,由于背景电流的增加,这些方法在区分好器件和坏器件方面也开始出现问题。一个好的IDDQ评估算法会考虑到所有关于“好”和“坏”零件的已知信息,即它“知道”好零件和坏零件的IDDQ签名是什么样的。不幸的是,不仅好零件的特征差别很大,而且坏零件的特征差别更大。此外,由于IDDQ故障往往是非致命的(不损害功能),所以通常很难说一个设备是“好”还是“坏”。然而,有两种信息是已知的,不涉及特定的过程或IC类型:一个是IDDQ故障的模型,另一个是背景IDDQ的统计分布。利用这些信息,创建了一个比传统的delta - iddq方法具有更高判别能力的估计器。给出了180nm芯片的若干批次的测量结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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