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Column Parity Row Selection (CPRS) BIST Diagnosis Technique: Modeling and Analysis

机译:列奇偶行选择(CPRS)BIST诊断技术:建模和分析

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Column Selection Row Parity (CPRS) diagnosis is an X-tolerant and low aliasing technique that is suitable for the BIST environment. A row selection LFSR randomly selects outputs of multiple scan chains so that unknowns can be tolerated. Column and row parities of selected outputs are observed to solve linear equations for the error positions. Experimental data show that CPRS achieves nearly perfect diagnosis, even in the presence of 1 percent unknowns. CPRS compresses the diagnosis data because only parities of circuit responses, instead of responses themselves, are observed. Two error distribution models (scattered and clustered) are developed and analyzed to show the effectiveness of CPRS. The analytical results are demonstrated to be accurate by more than 10,000 experiments.
机译:列选择行奇偶校验(CPRS)诊断是X容忍度低的混叠技术,适用于BIST环境。行选择LFSR随机选择多个扫描链的输出,以便可以容忍未知数。观察选定输出的列和行奇偶校验,以求解误差位置的线性方程。实验数据表明,即使存在1%的未知数,CPRS也可以实现近乎完美的诊断。 CPRS压缩诊断数据,因为仅观察到奇偶电路响应,而不是响应本身。开发并分析了两个误差分布模型(分散的和聚类的)以显示CPRS的有效性。超过10,000个实验证明了分析结果的准确性。

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