PastATPG: A Hybrid ATPG Framework for Better Test Compaction with Partial Assignment SAT

In automatic test pattern generation (ATPG), SAT-based methods are typically used to complement structural approaches, especially for addressing hard-to-detect faults. However, as the size and complexity of circuits grow, SAT-based ATPG faces challenges like pattern inflation and excessive runtime,...

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Vydáno v:2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7
Hlavní autoři: Chao, Zhiteng, Zhang, Xindi, Zhang, Xinyu, Mu, Jianan, Liu, Zizhen, Liang, Shengwen, Cai, Shaowei, Ye, Jing, Li, Xiaowei, Li, Huawei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 22.06.2025
Témata:
Automatic test pattern generation
Circuit faults
Codes
Compaction
Complexity theory
Design automation
Faces
Hybrid power systems
Limiting
Runtime
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Shrnutí:In automatic test pattern generation (ATPG), SAT-based methods are typically used to complement structural approaches, especially for addressing hard-to-detect faults. However, as the size and complexity of circuits grow, SAT-based ATPG faces challenges like pattern inflation and excessive runtime, limiting its overall performance. The key problem lies in the fact that current mainstream SAT solvers perform complete assignments for all primary inputs of the fault's transitive fanin cone without considering the detection of other faults, making test compaction extremely difficult and time consuming. In this paper, a novel SAT solver PA-MiniSat is proposed, which is capable of generating partial assignments for solving variables and significantly reduces the number of specified bits in test cubes. As an extension of MiniSat, it employs a full-literal watching technique and a circuit-adapted heuristic branching strategy, achieving overall improved performance in ATPG. Based on PA-MiniSat, a hybrid ATPG framework PastATPG is proposed for better test compaction, which tightly integrates structural algorithms with the SAT solver into the unified test compaction flow. Experimental results demonstrate that our method outperforms other SAT solvers in pattern compaction and, in some cases, even surpasses commercial ATPG tools in terms of speed. The code is available at https://github.com/sklp-eda-lab/PastATPG.
DOI:10.1109/DAC63849.2025.11132425