WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning

• URL: http://arxiv.org/abs/2604.27629v3
• Date: Thu, 07 May 2026 01:17:49 GMT
• Title: WaferSAGE: Large Language Model-Powered Wafer Defect Analysis via Synthetic Data Generation and Rubric-Guided Reinforcement Learning
• Abstract summary: We present WaferSAGE, a framework for wafer defect visual question answering using small vision-language models.<n>We employ clustering-based cleaning to filter label noise, then generate comprehensive defect descriptions using vision-language models.<n>These rubrics guide the synthesis of VQA pairs, ensuring coverage across defect type identification, spatial distribution, morphology, and root cause analysis.
• Score: 8.029209745978878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present WaferSAGE, a framework for wafer defect visual question answering using small vision-language models. To address data scarcity in semiconductor manufacturing, we propose a three-stage synthesis pipeline incorporating structured rubric generation for precise evaluation. Starting from limited labeled wafer maps, we employ clustering-based cleaning to filter label noise, then generate comprehensive defect descriptions using vision-language models, which are converted into structured evaluation rubrics criteria. These rubrics guide the synthesis of VQA pairs, ensuring coverage across defect type identification, spatial distribution, morphology, and root cause analysis. Our dual assessment framework aligns rule-based metrics with LLM-Judge scores via Bayesian optimization, enabling reliable automated evaluation. Through curriculum-based reinforcement learning with Group Sequence Policy Optimization (GSPO) and rubric-aligned rewards, our 4B-parameter Qwen3-VL model achieves a 6.493 LLM-Judge score, closely approaching Gemini-3-Flash (7.149) while enabling complete on-premise deployment. We demonstrate that small models with domain-specific training can surpass proprietary large models in specialized industrial visual understanding, offering a viable path for privacy-preserving, cost-effective deployment in semiconductor manufacturing.

Related papers

• DualEval: Joint Model-Item Calibration for Unified LLM Evaluation [67.99119926012686]
  DualEval is a latent model-item calibration framework that represents models and evaluation items in a shared space.<n>We apply DualEval across four domains: coding, math, miscellaneous domain-knowledge tasks, and generic everyday user queries.
  arXiv  Detail & Related papers  (2026-06-24T22:40:46Z)
• Gatekeepers and Hallucinations: A Layered Evaluation Framework for LLM-Driven Quantum Circuit Generation [0.0]
  We present a layered evaluation framework for materials-informed Variational Quantum Eigensolver (VQE) circuit generation.<n>We surface a taxonomy of five distinct LLM failure modes (geometry hallucination, nonexistent API usage, integration failures, constraint violations, and plausible-but-unverifiable output)<n>A forensic audit of the evaluation platform's own source code further establishes that two apparent model failures originated in the harness through silent fallback-template substitution.
  arXiv  Detail & Related papers  (2026-06-16T19:17:54Z)
• VG-CoT: Towards Trustworthy Visual Reasoning via Grounded Chain-of-Thought [16.361394107862502]
  We propose the Visual Grounding Chain-of-Thought dataset, which explicitly links each reasoning step to real visual evidence within the image.<n>The pipeline generates step-by-step grounded reasoning with GPT-4o and refines the grounding through a rationale-driven open-set detection process.<n> Experiments with representative LVLMs, including LLaVA-1.5 and Qwen2-VL, demonstrate consistent improvements on most evaluation metrics.
  arXiv  Detail & Related papers  (2026-04-23T08:04:07Z)
• Evian: Towards Explainable Visual Instruction-tuning Data Auditing [14.93566912726999]
  We introduce a novel "Decomposition-then-Evaluation" paradigm that breaks model responses into constituent cognitive components.<n>We instantiate this paradigm via EVIAN, an automated framework that evaluates these components along the axes of Image-Text Consistency, Logical Coherence, and Factual Accuracy.
  arXiv  Detail & Related papers  (2026-04-22T13:28:27Z)
• FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios [58.34124792457706]
  The manufacturing sector is increasingly adopting Multimodal Large Language Models (MLLMs) to transition from simple perception to autonomous execution.<n>Progress is hindered by data scarcity and a lack of fine-grained domain semantics in existing datasets.<n>We first construct a high-quality dataset that combines realworld 2D images and 3D point clouds, annotated with fine-grained domain semantics.<n>We then evaluate 18 state-of-the-art MLLMs across three manufacturing tasks, namely verification, structural surface inspection, and assembly verification, revealing significant performance gaps.
  arXiv  Detail & Related papers  (2026-04-08T12:23:27Z)
• Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training [0.0]
  This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty.<n>We construct a teacher student learning pipeline in which a Conditional Value at Risk (CVaR) generates supervisory labels, and neural models are trained using both real and synthetically augmented data.<n>Student models can match or outperform the CVaR teacher in several settings, while achieving improved robustness under regime shifts and reduced turnover.
  arXiv  Detail & Related papers  (2026-04-04T06:42:38Z)
• Beyond the Academic Monoculture: A Unified Framework and Industrial Perspective for Attributed Graph Clustering [75.50670592447219]
  Attributed Graph Clustering (AGC) is a fundamental unsupervised task that partitions nodes into cohesive groups by jointly modeling structural topology and node attributes.<n>This survey provides a comprehensive, industrially grounded review of AGC from three complementary perspectives.
  arXiv  Detail & Related papers  (2026-03-21T14:15:34Z)
• Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference [69.34278282513593]
  Preference Score Distillation (PSD) is an optimization-based framework for human-aligned text-to-3D synthesis without 3D training data.<n>Our key insight stems from the incompatibility of pixel-level gradients.<n>We introduce an adaptive strategy to co-optimize preference scores and negative text embeddings.
  arXiv  Detail & Related papers  (2026-03-02T08:23:36Z)
• Supervised Contrastive Machine Unlearning of Background Bias in Sonar Image Classification with Fine-Grained Explainable AI [1.4610038284393168]
  Acoustic sonar image analysis plays a critical role in object detection and classification, with applications in both civilian and defense domains.<n>Existing AI models that achieve high accuracy often over-rely on seafloor features, leading to poor generalization.<n>We propose a novel framework that integrates two key modules: (i) a Targeted Contrastive Unlearning (TCU) module, which extends the traditional triplet loss to reduce seafloor-induced background bias, and (ii) the Unlearn to Explain Sonar Framework (UESF), which provides visual insights into what the model has deliberately forgotten while adapting the LIME explainer to generate more faithful and localized
  arXiv  Detail & Related papers  (2025-12-01T05:25:34Z)
• Large Wireless Localization Model (LWLM): A Foundation Model for Positioning in 6G Networks [26.30108656575931]
  We propose a foundation-model-based solution tailored for wireless localization.<n>We first analyze how different self-supervised learning (SSL) tasks acquire general-purpose and task-specific semantic features.<n>We design a pretraining methodology for the proposed Large Wireless localization Model (LWLM)
  arXiv  Detail & Related papers  (2025-05-15T10:04:44Z)
• Benchmarking the Robustness of LiDAR Semantic Segmentation Models [78.6597530416523]
  In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. We propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. We design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications.
  arXiv  Detail & Related papers  (2023-01-03T06:47:31Z)
• Semi-supervised 3D Object Detection with Proficient Teachers [114.54835359657707]
  Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples. Pseudo-Labeling methodology is commonly used for SSL frameworks, however, the low-quality predictions from the teacher model have seriously limited its performance. We propose a new Pseudo-Labeling framework for semi-supervised 3D object detection, by enhancing the teacher model to a proficient one with several necessary designs.
  arXiv  Detail & Related papers  (2022-07-26T04:54:03Z)

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.