A fast test compaction method using dedicated Pure MaxSAT solver embedded in DFT flow

被引:0
作者
Chao, Zhiteng [1 ,3 ]
Zhang, Xindi [2 ,3 ]
Huang, Junying [1 ,3 ]
Liu, Zizhen [1 ]
Zhao, Yixuan [1 ]
Ye, Jing [1 ,3 ,4 ]
Cai, Shaowei [2 ,3 ]
Li, Huawei [1 ,3 ,4 ]
Li, Xiaowei [1 ,3 ,4 ]
机构
[1] Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing, Peoples R China
[2] Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China
[3] Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China
[4] CASTEST Co Ltd, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
Static test compaction; Pure MaxSAT; DFT; ATPG; TEST SETS; GENERATION;
D O I
10.1016/j.vlsi.2024.102265
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Minimizing the testing cost is crucial in the context of the design for test (DFT) flow. In our observation, the test patterns generated by ATPG tools in test compression mode still contain redundancy. To tackle this obstacle, we propose a post-flow static test compaction method that utilizes a partial fault dictionary instead of a full fault dictionary to sharply reduce time and memory overhead, and leverages a dedicated Pure MaxSAT solver to re-compact the test patterns generated by ATPG tools. We also observe that ATPG tools offer a more comprehensive selection of candidate patterns for compaction in the "n-detect'' mode, leading to superior compaction efficiency. In our experiments conducted on benchmark circuits ISCAS89, ITC99, and an open-source RISC-V CPU, we employed two methodologies. For commercial tool, we utilized a non-intrusive approach, while we adopted an intrusive method for open-source ATPG. Under the non-intrusive approach, our method achieved a maximum reduction of 34.69% in pattern count and a maximum 29.80% decrease in test cycles as evaluated by a leading commercial tool. Meanwhile, under the intrusive approach, our method attained a maximum 71.90% reduction in pattern count as evaluated by an open-source ATPG tool. Notably, fault coverage remained unchanged throughout the experiments. Furthermore, our approach demonstrates improved performance compared with existing methods.
引用
收藏
页数:11
相关论文
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