# DEFT: Differentiable Automatic Test Pattern Generation

arXiv: https://arxiv.org/abs/2512.23746 · HTML: https://mlsanity.com/paper/2512.23746/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2025.

## Built on by (most cited first, 2 of 2)

- [Differentiable Integer Linear Programming is not Differentiable & it's not a mere technical problem](https://mlsanity.com/paper/2601.17800/) (2026)
- [Unbiased Single-Queried Gradient for Combinatorial Objective](https://mlsanity.com/paper/2602.05119/) (2026)

## Abstract (arXiv)

Modern IC complexity drives test pattern growth, with the majority of patterns targeting a small set of hard-to-detect (HTD) faults. This motivates new ATPG algorithms to improve test effectiveness specifically for HTD faults. This paper presents DEFT (Differentiable Automatic Test Pattern Generation), a new ATPG approach that reformulates the discrete ATPG problem as a continuous optimization task. DEFT introduces a mathematically grounded reparameterization that aligns the expected continuous objective with discrete fault-detection semantics, enabling reliable gradient-based pattern generation. To ensure scalability and stability on deep circuit graphs, DEFT integrates a custom CUDA kernel for efficient forward-backward propagation and applies gradient normalization to mitigate vanishing gradients. Compared to a leading commercial tool on a wide range of benchmarks, DEFT reduced the pattern count by 27.3% on average and by up to 75.9%. DEFT also supports practical ATPG settings such as partial assignment pattern generation, producing patterns with 19.3% fewer 0/1 bits while still detecting 35% more faults. These results indicate DEFT is a promising and effective ATPG engine, offering a valuable complement to existing heuristics.