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Embedded memory fail analysis for production yield enhancement

机译:嵌入式内存故障分析可提高产量

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The traditional approach for memory fail bitmap analysis is to identify the topological signatures and perform a Failure Analysis investigation on the most frequent signatures, based on the (x, y) coordinates of the fails. This approach is inappropriate when a large portion of the fails are single bits, because too many investigations are required to statistically identify the major repetitive failure mechanisms. This becomes a problem for fast product development and production yield ramp. This paper presents a methodology to classify single fail bits by their unique fault signature, based on the sequence of failing march element read operations from multiple data backgrounds, in a standard Memory BIST flow. These classifications allow investigations to focus on the most important failure mechanisms with greatest yield impact. The methodology is demonstrated in an industrial environment, with identification of critical yield detractors. Starting from a yield problem associated to MBIST failures at high operating temperature, the fault signatures were used to identify a static noise margin parametric problem and a dislocation fault physical problem.
机译:内存故障位图分析的传统方法是基于故障的(x,y)坐标来识别拓扑特征并对最频繁的特征执行故障分析调查。当大部分故障是单个位时,此方法是不合适的,因为需要进行大量研究才能从统计学上识别出主要的重复性故障机制。对于快速的产品开发和产量增加,这成为一个问题。本文提出了一种方法,该方法根据标准内存BIST流中来自多个数据背景的故障行军元素读取操作的顺序,通过其唯一的故障特征对单个故障位进行分类。这些分类使研究可以集中在对产量影响最大的最重要的失效机理上。该方法已在工业环境中进行了演示,并确定了关键的减产因素。从与高工作温度下MBIST故障相关的屈服问题开始,使用故障签名来识别静态噪声容限参数问题和位错故障物理问题。

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