DeepTPI：Test Point Insertion with Deep Reinforcement Learning

Test point insertion (TPI) is a widely used technique for testability enhancement, especially for logic built-in self-test (LBIST) due to its relatively low fault coverage. In this paper, we propose a novel TPI approach based on deep reinforcement learning (DRL), named DeepTpi. Unlike previous learning-based solutions that formulate the TPI task as a supervised-learning problem, we train a novel DRL agent, instantiated as the combination of a graph neural network (GNN) and a Deep Q-Learning network (DQN), to maximize the test coverage improvement. Specifically, we model circuits as directed graphs and design a graph-based value network to estimate the action values for inserting different test points. The policy of the DRL agent is defined as selecting the action with the maximum value. Moreover, we apply the general node embeddings from a pretrained model to enhance node features, and propose a dedicated testability-aware attention mechanism for the value network. Experimental results on circuits with various scales show that DeepTPI significantly improves test coverage compared to the commercial DFT tool.

1. The Overview of DeepTPI Agent

2. The statistics of testing circuits in AIG format

3. The Runtime Breakdown of the DeepTPI Inference

@inproceedings{shi2022deeptpi,
  title={DeepTPI: Test Point Insertion with Deep Reinforcement Learning},
  author={Shi, Zhengyuan and Li, Min and Khan, Sadaf and Wang, Liuzheng and Wang, Naixing and Huang, Yu and Xu, Qiang},
  booktitle={2022 IEEE International Test Conference (ITC)},
  pages={194--203},
  year={2022},
  organization={IEEE}
}