Predictive LBIST model and partial ATPG for seed extraction

Gustavo K. Contreras, N. Ahmed, L. Winemberg, M. Tehranipoor
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引用次数: 10

Abstract

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.
种子提取的预测LBIST模型和部分ATPG
用于关键和高可靠性应用的集成电路通常具有严格的测试要求,包括高测试覆盖率和有限的测试时间。使用内置自测(BIST)实现高测试覆盖率已被证明是困难的。测试点插入或确定性BIST等方法可以提供较高的测试覆盖率,但会引入大量的面积开销和设计工作。本文提出了一种利用逻辑BIST (LBIST)结构的线性异或模型和故障划分来提取部分ATPG模式种子的计算算法。在求解线性异或方程生成确定性种子时,采用部分ATPG模式来降低算法的复杂度。提取的种子存储在芯片内或芯片外的非易失性存储器中。结果表明,对于大多数设计,由提取的ATPG种子生成的模式在检测故障方面明显比LBIST更有效,并且可以实现更高的测试覆盖率。
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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