# HybMT: Hybrid Meta-Predictor based ML Algorithm for Fast Test Vector Generation

arXiv: https://arxiv.org/abs/2207.11312 · HTML: https://mlsanity.com/paper/2207.11312/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [CoAtNet: Marrying Convolution and Attention for All Data Sizes](https://mlsanity.com/paper/2106.04803/) (2021)
- [On the Number of Linear Regions of Deep Neural Networks](https://mlsanity.com/paper/1402.1869/) (2014)
- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [DRiLLS: Deep Reinforcement Learning for Logic Synthesis](https://mlsanity.com/paper/1911.04021/) (2019)
- [DeepTPI: Test Point Insertion with Deep Reinforcement Learning](https://mlsanity.com/paper/2206.06975/) (2022)

## Built on by (most cited first, 1 of 1)

- [InF-ATPG: Intelligent FFR-Driven ATPG with Advanced Circuit Representation Guided Reinforcement Learning](https://mlsanity.com/paper/2512.00079/) (2025)

## Abstract (arXiv)

ML models are increasingly being used to increase the test coverage and decrease the overall testing time. This field is still in its nascent stage and up till now there were no algorithms that could match or outperform commercial tools in terms of speed and accuracy for large circuits. We propose an ATPG algorithm HybMT in this paper that finally breaks this barrier. Like sister methods, we augment the classical PODEM algorithm that uses recursive backtracking. We design a custom 2-level predictor that predicts the input net of a logic gate whose value needs to be set to ensure that the output is a given value (0 or 1). Our predictor chooses the output from among two first-level predictors, where the most effective one is a bespoke neural network and the other is an SVM regressor. As compared to a popular, state-of-the-art commercial ATPG tool, HybMT shows an overall reduction of 56.6% in the CPU time without compromising on the fault coverage for the EPFL benchmark circuits. HybMT also shows a speedup of 126.4% over the best ML-based algorithm while obtaining an equal or better fault coverage for the EPFL benchmark circuits.