DeepTPI: Test Point Insertion with Deep Reinforcement Learning

TL;DR

DeepTPI introduces a deep reinforcement learning approach using graph neural networks to optimize test point insertion, significantly enhancing test coverage in circuit testing compared to traditional methods.

Contribution

This paper presents a novel DRL-based TPI method combining GNNs and DQN, with testability-aware attention, for improved test coverage in circuit testing.

Findings

DeepTPI outperforms commercial DFT tools in test coverage.

The approach effectively models circuits as graphs for better decision-making.

Experimental results demonstrate significant improvements across various circuit scales.

Abstract

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 pre-trained 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. The code of this work is available at https://github.com/cure-lab/DeepTPI.