Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection

• URL: http://arxiv.org/abs/2605.26468v1
• Date: Tue, 26 May 2026 02:24:44 GMT
• Title: Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
• Abstract summary: We propose the first unsupervised anomaly detection framework incorporating a Diffusion Transformer.<n>Anomaly scores are derived from the noise-prediction error over mid-range diffusion timesteps.<n>Our approach achieves state-of-the-art performance on industrial 16nm IC test data under extreme class imbalance.
• Score: 6.4155075067452385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Latent defect screening is challenged by extremely low failure rates, high-dimensional test data, and absence of labeled anomalies. We propose the first unsupervised anomaly detection framework incorporating a Diffusion Transformer. Raw test measurements are first compressed by an autoencoder, then reshaped into a structured token sequence enriched with sinusoidal and per-device wafer-position embeddings. Anomaly scores are derived from the noise-prediction error over mid-range diffusion timesteps, enabling fast wafer-scale screening without any labeled defects or manual feature engineering. Our approach achieves state-of-the-art performance on industrial 16nm IC test data under extreme class imbalance, offering interpretable failure localization through latent-space reconstruction residuals.

Related papers

• Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection [53.26344422459608]
  We propose textbfPCDiff, a point cloud diffusion framework for instance-level 3D anomaly generation and detection.<n>In the generation phase, an instance-level multi-modal attention is embedded into the generation framework, where anomalies are conditioned with texture gradient, image patch, text and mask.<n>In the detection phase, a joint local-global reconstruction algorithm is introduced to ensure local anomaly restoration and global geometric consistency.
  arXiv  Detail & Related papers  (2026-06-24T12:11:05Z)
• GroundingAnomaly: Spatially-Grounded Diffusion for Few-Shot Anomaly Synthesis [16.74245445257004]
  We propose GroundingAnomaly, a novel few-shot anomaly image generation framework.<n>Our framework introduces a Spatial Conditioning Module that leverages per-pixel semantic maps.<n>A Gated Self-Attention Module is designed to inject conditioning tokens into a frozen U-Net.
  arXiv  Detail & Related papers  (2026-04-09T14:34:50Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• ASD-Diffusion: Anomalous Sound Detection with Diffusion Models [6.659078422704148]
  Anomalous Sound Detection based on Diffusion Models (ASD-Diffusion) is proposed for ASD in real-world factories. Post-processing anomalies filter algorithm is proposed to detect anomalies that exhibit significant deviation from the original input after reconstruction. Denoising diffusion implicit model is introduced to accelerate the inference speed.
  arXiv  Detail & Related papers  (2024-09-24T10:42:23Z)
• Adversarially Robust Industrial Anomaly Detection Through Diffusion Model [23.97654469255749]
  We propose a simple yet effective adversarially robust anomaly detection method, textitAdvRAD, that allows the diffusion model to act both as an anomaly detector and adversarial purifier. Our proposed method exhibits outstanding (certified) adversarial robustness while also maintaining equally strong anomaly detection performance on par with the state-of-the-art methods on industrial anomaly detection benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-09T03:25:19Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection [80.20339155618612]
  DiffusionAD is a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network.<n>A rapid one-step denoising paradigm achieves hundreds of times acceleration while preserving comparable reconstruction quality.<n>Considering the diversity in the manifestation of anomalies, we propose a norm-guided paradigm to integrate the benefits of multiple noise scales.
  arXiv  Detail & Related papers  (2023-03-15T16:14:06Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• SLA$^2$P: Self-supervised Anomaly Detection with Adversarial Perturbation [77.71161225100927]
  Anomaly detection is a fundamental yet challenging problem in machine learning. We propose a novel and powerful framework, dubbed as SLA$2$P, for unsupervised anomaly detection.
  arXiv  Detail & Related papers  (2021-11-25T03:53:43Z)
• Anomalous sound detection based on interpolation deep neural network [9.309962824653033]
  We propose an approach to anomalous detection in which the model utilizes multiple frames of a spectrogram. We show that the proposed approach achieved 27% improvement based on the standard AUC score.
  arXiv  Detail & Related papers  (2020-05-19T06:12:41Z)
• Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks [30.970377781506258]
  It suffers from the class imbalance issue and the lacking in the abnormal instances. In this paper, a novel Gaussian Mixture Generative Adrial Network (GMGAN) is proposed under semi-supervised learning framework. Experiments show that our model has clear superiority over previous methods, and achieves the state-of-the-art results on DCASE dataset.
  arXiv  Detail & Related papers  (2020-02-04T03:39:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.