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Predictive LBIST model and partial ATPG for seed extraction

机译:用于种子提取的预测Lbist模型和部分ATPG

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摘要

Integrated circuits used in critical and high reliability applications have often strict test requirements including high test coverage and limited test time. Achieving a high test coverage using built-in self-test (BIST) has proven difficult. Methods such as test point insertion or deterministic BIST can provide high test coverage but introduce significant area overhead and design effort. In this paper, we propose a computational algorithm that uses a linear XOR model of the logic BIST (LBIST) structure and fault partitioning to extract seeds for partial ATPG patterns. Partial ATPG patterns are used to decrease the complexity of the algorithm when solving linear XOR equations to generate deterministic seeds. The extracted seeds are stored in a nonvolatile memory on- or off-chip. Results show that for most designs, patterns generated from the extracted ATPG seeds are significantly more effective in detecting faults and can achieve higher test coverage than LBIST.
机译:用于批判性和高可靠性应用的集成电路通常严格的测试要求,包括高测试覆盖率和有限的测试时间。使用内置自检(BIST)实现高测试覆盖率已经证明困难。测试点插入或确定性BIST等方法可以提供高测试覆盖率,但引入显着的区域开销和设计努力。在本文中,我们提出了一种计算算法,该计算算法使用逻辑BIST(LBIST)结构的线性XOR模型和故障分区以提取用于部分ATPG图案的种子。局部ATPG模式用于降低求解线性XOR方程以产生确定性种子的算法的复杂性。提取的种子在芯片或片外储存在非易失性存储器中。结果表明,对于大多数设计,从提取的ATPG种子产生的图案在检测故障方面明显更有效,并且可以实现比Lbist更高的测试覆盖率。

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