带有故障性质预测的自动测试向量求解模型

贺丽媛; 黄俊华; 陶继平

doi:10.12263/DZXB.20221121

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带有故障性质预测的自动测试向量求解模型

学术论文 | 更新时间：2025-12-11

- 带有故障性质预测的自动测试向量求解模型
- Automatic Test Pattern Solving with Fault Property Prediction
- 电子学报 2023年51卷第12期页码：3540-3548
- 作者机构：
  
  1.厦门大学自动化系，福建厦门 361102
  2.华为诺亚方舟实验室，广东深圳 518000
- 作者简介：
  
  [ "贺丽媛女，1998年10月出生，湖北武汉人.现为厦门大学自动化系硕士研究生.研究方向为神经组合优化、深度学习等.E-mail: liyuanhe@stu.xmu.edu.cn" ]
  [ "黄俊华女，1997年12月出生，四川南部人.2022年6月毕业于厦门大学自动化系，现为华为诺亚方舟实验室研究员.研究方向为深度学习、布尔可满足问题、电子设计自动化、组合优化等. E-mail: huang.hjh@outlook.com" ]
  [ "陶继平（通讯作者）男，1980年6月出生，安徽怀宁人.毕业于上海交通大学控制科学与工程专业，获得工学博士学位，现任厦门大学自动化系副教授.主要研究兴趣包括调度算法设计与分析、整数规划建模与拉格朗日松弛方法、基于机器学习的组合优化方法等.E-mail: taojiping@xmu.edu.cn" ]
- 基金信息：
  
  福建省自然科学基金(2020J01053)
- DOI：10.12263/DZXB.20221121
  中图分类号： TN407;TP183
- 收稿：2022-10-09，
  
  修回：2023-05-08，
  
  纸质出版：2023-12-25
- 稿件说明：
移动端阅览
贺丽媛,黄俊华,陶继平.带有故障性质预测的自动测试向量求解模型[J].电子学报,2023,51(12):3540-3548.

HE Li-yuan,HUANG Jun-hua,TAO Ji-ping.Automatic Test Pattern Solving with Fault Property Prediction[J].ACTA ELECTRONICA SINICA,2023,51(12):3540-3548.
贺丽媛,黄俊华,陶继平.带有故障性质预测的自动测试向量求解模型[J].电子学报,2023,51(12):3540-3548. DOI： 10.12263/DZXB.20221121.

HE Li-yuan,HUANG Jun-hua,TAO Ji-ping.Automatic Test Pattern Solving with Fault Property Prediction[J].ACTA ELECTRONICA SINICA,2023,51(12):3540-3548. DOI： 10.12263/DZXB.20221121.

摘要

基于布尔满足模型的自动测试向量生成是芯片故障检测的关键环节，相应布尔问题的求解已然成为整个故障检测过程的效率瓶颈.本文研究了主流自动测试向量求解框架中不同算子对求解效率的影响，在保证测试向量求解流程完备性的同时引入基于深度学习的故障分析机制，并将分析结果用于算子的自动选择和初始求解状态的确定，旨在优化整体求解进程.针对因真实电路故障数据不足导致模型学习效果欠佳的问题，本文利用生成对抗网络实现数据增广，结合多层图卷积神经网络促进高效表征学习，从而提高故障性质的预测精度.在若干真实电路上的实验结果表明，本文所提出的新框架与原有框架相比，平均求解效率提升近20%.

Abstract

Automatic test pattern generation (ATPG) based on the Boolean satisfaction model plays a key part in chip fault detection flow

in which solving the corresponding Boolean satisfiability problem (SAT) becomes the efficacy bottleneck of the whole process. In this paper

the influence of different operators on the solution efficiency in the mainstream automatic test pattern solution framework is studied. While ensuring the integrity of the test pattern solution process

a fault analysis mechanism based on deep learning is introduced

and the output vectors are used for the automatic selection of operators and the determination of initial solution states so as to accelerate the overall solution process. To alleviate poor performance mainly caused by insufficient real-world circuit fault data

a generative adversarial network (GAN)

followed by a multi-layer graph convolutional neural network (GCN) which is designed to boost representation learning

is leveraged for data augmentation. Experimental results on several real circuits show that the proposed new framework

compared with the original version

has an average solution improvement of nearly 20%.

关键词

Keywords

references

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