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English(EN) Zero-Shot Multi-Class Wafer Map Classification with Rule-Ordered Prompts for Large Multimodal Models

新AI框架无需重新训练即可对晶圆图进行分类 · arXiv cs.AI

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的六类框架，用于对晶圆图进行分类，而无需标记数据或重新训练。该方法结合了连通分量高亮显示和用于大型多模态模型的规则排序提示。该方法在基准数据集上取得了高精度，在Mixed-WM38上的总体准确率为93.8%，在WM811K上的准确率为81.3%，证明了其在缺陷分类方面的有效性。 AI

影响 该方法通过减少对大量数据标记和模型重新训练的需求，可以简化半导体制造中的缺陷检测。

排序理由 详细介绍新AI方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新AI框架无需重新训练即可对晶圆图进行分类 · arXiv cs.AI

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详细介绍新AI方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Topics
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Tianjian Cao, Jun Tao ·

    面向大型多模态模型的零样本多类别晶圆图分类与规则排序提示

    arXiv:2610.35080v1 Announce Type: new Abstract: Wafer-map defect classification often requires labeled data and retraining when production conditions change. We present a training-free six-class framework that combines connected-component highlighting with morphology-, location-,…

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